IU Computer Vision Lab

2026

• Yu Wang, Juhyung Wang, Frangil Ramirez, Yuchen Wang, and David J. Crandall. GateFusion: Hierarchical Gated Cross-Modal Fusion for Active Speaker Detection. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2026.
• Xizi Wang, Feng Cheng, Ziyang Wang, Huiyu Wang, Md Mohaiminul Islam, Lorenzo Torresani, Mohit Bansal, Gedas Bertasius, and David J. Crandall. TimeRefine: Temporal Grounding with Time Refining Video LLM. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2026.

2025

• Juhyung Ha, Vibhas Kumar Vats, Alimoor Reza, Soon-heung Jung, and David J. Crandall. HVPUNet: Hybrid-Voxel Point-cloud Upsampling Network. In IEEE International Conference on Computer Vision (ICCV), 2025. (poster, 24% acceptance rate). [⁠paper⁠]
• Chi-Hsi Kung, Frangil M Ramirez, Juhyung Ha, Yi-Hsuan Tsai, Yi-Ting Chen, and David J. Crandall. What Changed and What Could Have Changed? State-Change Counterfactuals for Procedure-Aware Video Representation Learning. In IEEE International Conference on Computer Vision (ICCV), 2025. (poster, 24% acceptance rate). [⁠paper⁠]
• Maha Ibrar, Megan Knobeloch, Nayana Christudas Beena, Yaroslav Losovyj, Sheng-Yuan Huang, Claire McCurtain, David Crandall, Stephen Jacobson, and Sara Skrabalak. Stable versus Temporally Sensitive Optical Security Tags from Metal Nanoparticles. Nano Letters, March 2025. (impact factor = 9.6). [⁠paper⁠]
• Diane Kuhn, Nicholas Harrison, Paul Musey, David J. Crandall, Peter Pang, Julie Welch, and Christopher Harle. Preliminary findings regarding the association between patient demographics and ED experience scores across a regional health system: A cross sectional study using natural language processing of patient comments. International Journal of Medical Informatics, March 2025. (impact factor = 4.046).
• Samuel Goree, Jackson Domingo, and David J. Crandall. Human-Centered Evaluation of Aesthetic Quality Assessment Models Using a Smartphone Camera Application. In ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2025. (26.8% acceptance rate). [⁠paper⁠]
• Long-Jing Hsu, Janice Bays, Manasi Swaminathan, Weslie Khoo, Hiroki Sato, Kyrie Jig Amon, Sathvika Dobbala, Min Min Thant, Alex Foster, Katherine M. Tsui, Philip B. Stafford, David J. Crandall, and Selma Sabanovic. Research as care: A reflection on incorporating the ethics of care in design research with people living with dementia. In ACM Designing Interactive Systems Conference (DIS), 2025. Best paper honorable mention. (25% acceptance rate). [⁠paper⁠]
• Long-Jing Hsu, Janice Bays, Manasi Swaminathan, Weslie Khoo, Hiroki Sato, Kyrie Jig Amon, Sathvika Dobbala, Min Min Thant, Alex Foster, Kate Tsui, Philip B. Stafford, David J. Crandall, and Selma Sabanovic. Bittersweet Snapshots of Life: Designing to Address Complex Emotions in a Reminiscence Interaction between Older Adults and a Robot. In ACM CHI Conference on Human Factors in Computing Systems (CHI), 2025. (25.1% acceptance rate). [⁠paper⁠]
• Juhyung Ha, Jong Sung Park, David Crandall, Eleftherios Garyfallidis, and Xuhong Zhang. Multi-resolution Guided 3D GANs for Medical Image Translation. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2025. (poster, 37.8% acceptance rate). [⁠paper⁠]
• Long-Jing Hsu, Swapna Joshi, Erin Seliger, Kyrie Jig Amon, Waki Kamino, Natasha Randall, Weslie Khoo, David J. Crandall, Abhijeet Agnihotri, Kathermine M. Tsui, and Selma Sabanavoci. Let's make up a story: Design and evaluation of an improvisational storytelling robot to foster older adults' meaning and purpose in life. Frontiers in Dementia, 2025. Accepted, to appear.
• Vibhas Vats, Md Alimoor Reza, David J. Crandall, and Soon-heung Jung. Blending 3D Geometry and Machine Learning for Multi-View Stereopsis. Neurocomputing, Nov 2025. Accepted, to appear. (impact factor = 6.5). [⁠paper⁠]
• Zachary Wilkerson, David Leake, David Crandall, and Benjamin Wilkerson. Extracting Features with Deep Learning for Ensemble-Driven Case-Based Classification. In International Conference on Case-based Reasoning (ICCBR), 2025. Best paper award. (37% acceptance rate). [⁠paper⁠]
• Vibhas Vats, Zachary Wilkerson, Hiroki Sato, David Leake, and David Crandall. Learning Case Features with Proxy-Guided Deep Neural Networks. In International Conference on Case-based Reasoning (ICCBR), 2025. (37% acceptance rate). [⁠paper⁠]
• Weslie Khoo, Long-Jing Hsu, Frangil M. Ramirez, Liang Jhen Huang, Trisha Konkimalla, Chia-Fang Chung, and David J. Crandall. Bridging Human Intuition and AI in Colorful Food Assessment. In ACM CHI Conference on Human Factors in Computing Systems (CHI) Late Breaking Work, 2025. (32.83% acceptance rate). [⁠paper⁠]
• Waki Kamino, Andrea Wang, Dhruv Agarwal, Sil Hamilton, Eun Jeong Kang, Jieun Kim, Keigo Kusumegi, Pegah Moradi, Daniel Mwesigwa, Yan Tao, I-Ting Tsai, Ethan Yang, Shengqi Zhu, Shu-Jung Han, Chi-Jung Lee, Michael Joseph Sack, Tianhong Catherine Yu, Weslie Khoo, Andy Elliot Ricci, Yoyo Tsung-Yu Hou, Boyoung Kim, Selma Sabanovic, David Crandall, Karen Levy, and Malte F Jung. Million Eyes on the Robot Umps: The Case for Studying Sports in HRI Through Baseball. In Companion of the ACM/IEEE International Conference on Human Robot Interaction (HRI), 2025.
• Vibhas Vats and David Crandall. Geometric Constraints in Deep Learning Frameworks: A Survey. ACM Computing Surveys, May 2025. (impact factor = 16.6). [⁠paper⁠]
• Kristen Grauman, Andrew Westbury, Eugene Byrne, Vincent Cartillier, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Devansh Kukreja, Miao Liu, Xingyu Liu, Miguel Martin, Tushar Nagarajan, Ilija Radosavovic, Santhosh Kumar Ramakrishnan, Fiona Ryan, Jayant Sharma, Michael Wray, Mengmeng Xu, Eric Zhongcong Xu, Chen Zhao, Siddhant Bansal, Dhruv Batra, Sean Crane, Tien Do, Morrie Doulaty, Akshay Erapalli, Christoph Feichtenhofer, Adriano Fragomeni, Qichen Fu, Abrham Gebreselasie, Cristina Gonzalez, James Hillis, Xuhua Huang, Yifei Huang, Wenqi Jia, Weslie Khoo, Jachym Kolar, Satwik Kottur, Anurag Kumar, Federico Landini, Chao Li, Yanghao Li, Zhenqiang Li, Karttikeya Mangalam, Raghava Modhugu, Jonathan Munro, Tullie Murrell, Takumi Nishiyasu, Will Price, Paola Ruiz Puentes, Merey Ramazanova, Leda Sari, Kiran Somasundaram, Audrey Southerland, Yusuke Sugano, Ruijie Tao, Minh Vo, Yuchen Wang, Xindi Wu, Takuma Yagi, Ziwei Zhao, Yunyi Zhu, Pablo Arbelaez, David Crandall, Dima Damen, Giovanni Maria Farinella, Christian Fuegen, Bernard Ghanem, Vamsi Krishna Ithapu, C. V. Jawahar, Hanbyul Joo, Kris Kitani, Haizhou Li, Richard Newcombe, Aude Oliva, Hyun Soo Park, James M. Rehg, Yoichi Sato, Jianbo Shi, Mike Zheng Shou, Antonio Torralba, Lorenzo Torresani, Mingfei Yan, and Jitendra Malik. Ego4d: Around the world in 3,000 hours of egocentric video. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Nov 2025. (impact factor = 20.8). [⁠project page⁠] [⁠video⁠]
• Kristen Grauman, Andrew Westbury, Lorenzo Torresani, Kris Kitani, Jitendra Malik, Triantafyllos Afouras, Kumar Ashutosh, Vijay Baiyya, Siddhant Bansal, Bikram Boote, Eugene Byrne, Zach Chavis, Joya Chen, Feng Cheng, Fu-Jen Chu, Sean Crane, Avijit Dasgupta, Jing Dong, Maria Escobar, Cristhian Forigua, Abrham Gebreselasie, Sanjay Haresh, Jing Huang, Md Mohaiminul Islam, Suyog Jain, Rawal Khirodkar, Devansh Kukreja, Kevin J. Liang, Jia-Wei Liu, Sagnik Majumder, Yongsen Mao, Miguel Martin, Effrosyni Mavroudi, Tushar Nagarajan, Francesco Ragusa, Santhosh Kumar Ramakrishnan, Luigi Seminara, Arjun Somayazulu, Yale Song, Shan Su, Zihui Xue, Edward Zhang, Jinxu Zhang, Angela Castillo, Changan Chen, Xinzhu Fu, Ryosuke Furuta, Cristina González, Prince Gupta, Jiabo Hu, Yifei Huang, Yiming Huang, Weslie Khoo, Anush Kumar, Robert Kuo, Sach Lakhavani, Miao Liu, Mi Luo, Zhengyi Luo, Brighid Meredith, Austin Miller, Oluwatumininu Oguntola, Xiaqing Pan, Penny Peng, Shraman Pramanick, Merey Ramazanova, Fiona Ryan, Wei Shan, Kiran Somasundaram, Chenan Song, Audrey Southerland, Masatoshi Tateno, Huiyu Wang, Yuchen Wang, Takuma Yagi, Mingfei Yan, Xitong Yang, Zecheng Yu, Shengxin Cindy Zha, Chen Zhao, Ziwei Zhao, Zhifan Zhu, Jeff Zhuo, Pablo Arbeláez, Gedas Bertasius, David Crandall, Dima Damen, Jakob Engel, Giovanni Maria Farinella, Antonino Furnari, Bernard Ghanem, Judy Hoffman, C. V. Jawahar, Richard Newcombe, Hyun Soo Park, James M. Rehg, Yoichi Sato, Manolis Savva, Jianbo Shi, Mike Zheng Shou, and Michael Wray. Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives. International Journal of Computer Vision (IJCV), Dec 2025. (impact factor = 15.5). [⁠paper⁠]
• Xiaomeng Ye, David Leake, Yu Wang, and David J. Crandall. Run Like a Neural Network, Explain Like k-Nearest Neighbor. In International Joint Conference on Artificial Intelligence (IJCAI), 2025. (19.3% acceptance rate). [⁠paper⁠]

2024

• Long-Jing Hsu, Philip B. Stafford, Weslie Khoo, Manasi Swaminathan, Kyrie Jig Amon, Hiroki Sato, Kate Tsui, David J. Crandall, and Selma Sabanovic. "Give it time:" Longitudinal Panels Scaffold Older Adults' Learning and Robot Co-Design. In ACM/IEEE International Conference on Human Robot Interaction (HRI), 2024. (24% acceptance rate). [⁠paper⁠]
• Maha Ibrar, Sheng-Yuan Huang, Zachery McCurtain, Shujon Naha, David J. Crandall, Stephen C. Jacobson, and Sara E. Skrabalak. Modular Anti-counterfeit Tags Formed by Template-Assisted Self-Assembly of Plasmonic Nanocrystals and Authenticated by Machine Learning. Advanced Functional Materials, 2024. (impact factor = 19.0). [⁠paper⁠]
• Satoshi Tsutsui, Yanwei Fu, and David J. Crandall. Reinforcing Generated Images via Meta-learning for One-Shot Fine-Grained Visual Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 46(3):1455-1463, March 2024. (impact factor = 17.861). [⁠paper⁠]
• Vibhas K. Vats, Sripad Joshi, David J. Crandall, Md. Alimoor Reza, and Soon-heung Jung. GC-MVSNet: Multi-View, Multi-Scale, Geometrically-Consistent Multi-View Stereo. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2024. (41.4% acceptance rate). [⁠paper⁠] [⁠project page⁠] [⁠video⁠]
• Xiaomeng Ye, David Leake, Yu Wang, Ziwei Zhao, and David J. Crandall. Network Implementation of CBR: Case Study of a Neural Network K-NN. In International Conference on Case-based Reasoning (ICCBR), 2024. (31.8% acceptance rate). [⁠paper⁠]
• Zachary Wilkerson, David Leake, Vibhas Vats, and David J. Crandall. Extracting Indexing Features for CBR from Deep Neural Networks: A Transfer Learning Approach. In International Conference on Case-based Reasoning (ICCBR), 2024. (31.8% acceptance rate). [⁠paper⁠]
• Kangsan Lee, Jaehyuk Park, Sam Goree, David J. Crandall, and Yong-Yeol Ahn. Social signals predict contemporary art prices better than visual features, particularly in emerging markets. Scientific Reports, May 2024. (impact factor = 4.6). [⁠paper⁠]
• Long-Jing Hsu, Weslie Khoo, Manasi Swaminathan, Kyrie Jig Amon, Rasika Muralidharan, Hiroki Sato, Min Min Thant, Anna S. Kim, Katherine M Tsui, David J Crandall, and Selma Sabanovic. Let's Talk About You: Development and Evaluation of an Autonomous Robot to Support Ikigai Reflection in Older Adults. In IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 2024. [⁠paper⁠]
• Xizi Wang, Feng Cheng, and Gedas Bertasius. LoCoNet: Long-Short Context Network for Active Speaker Detection. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. (poster, 23.6% acceptance rate). [⁠paper⁠] [⁠video⁠]
• Ziwei Zhao, Yuchen Wang, and Chuhua Wang. Fusing Personal and Environmental Cues for Identification and Segmentation of First-Person Camera Wearers in Third-Person. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. (poster, 23.6% acceptance rate). [⁠paper⁠]
• Kristen Grauman, Andrew Westbury, Lorenzo Torresani, Kris Kitani, Jitendra Malik, Triantafyllos Afouras, Kumar Ashutosh, Vijay Baiyya, Siddhant Bansal, Bikram Boote, Eugene Byrne, Zach Chavis, Joya Chen, Feng Cheng, Fu-Jen Chu, Sean Crane, Avijit Dasgupta, Jing Dong, Maria Escobar, Cristhian Forigua, Abrham Gebreselasie, Sanjay Haresh, Jing Huang, Md Mohaiminul Islam, Suyog Jain, Rawal Khirodkar, Devansh Kukreja, Kevin J Liang, Jia-Wei Liu, Sagnik Majumder, Yongsen Mao, Miguel Martin, Effrosyni Mavroudi, Tushar Nagarajan, Francesco Ragusa, Santhosh Kumar Ramakrishnan, Luigi Seminara, Arjun Somayazulu, Yale Song, Shan Su, Zihui Xue, Edward Zhang, Jinxu Zhang, Angela Castillo, Changan Chen, Xinzhu Fu, Ryosuke Furuta, Cristina Gonzalez, Prince Gupta, Jiabo Hu, Yifei Huang, Yiming Huang, Weslie Khoo, Anush Kumar, Robert Kuo, Sach Lakhavani, Miao Liu, Mi Luo, Zhengyi Luo, Brighid Meredith, Austin Miller, Oluwatumininu Oguntola, Xiaqing Pan, Penny Peng, Shraman Pramanick, Merey Ramazanova, Fiona Ryan, Wei Shan, Kiran Somasundaram, Chenan Song, Audrey Southerland, Masatoshi Tateno, Huiyu Wang, Yuchen Wang, Takuma Yagi, Mingfei Yan, Xitong Yang, Zecheng Yu, Shengxin Cindy Zha, Chen Zhao, Ziwei Zhao, Zhifan Zhu, Jeff Zhuo, Pablo Arbelaez, Gedas Bertasius, Dima Damen, Jakob Engel, Giovanni Maria Farinella, Antonino Furnari, Bernard Ghanem, Judy Hoffman, C.V. Jawahar, Richard Newcombe, Hyun Soo Park, James M. Rehg, Yoichi Sato, Manolis Savva, Jianbo Shi, Mike Zheng Shou, and Michael Wray. Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. (oral, 0.8% acceptance rate). [⁠paper⁠] [⁠project page⁠] [⁠video⁠]
• Chuhua Wang, Md Alimoor Reza, Vibhas Vats, Yingnan Ju, Nikhil Thakurdesai, Yuchen Wang, David J. Crandall, Soon-heung Jung, and Jeongil Seo. Deep learning-based 3D reconstruction from multiple images: A survey. Neurocomputing, September 2024. (impact factor = 6.0).
• David Leake, Zachary Wilkerson, and David J. Crandall. Combining Case-Based Reasoning with Deep Learning: Context and Ongoing Case Feature Learning Research. In AAAI Workshop on Neuro-Symbolic Learning and Reasoning in the Era of Large Language Models, 2024.
• Long-Ling Hsu, Weslie Khoo, Peter Lenon Goshomi, Philip B. Stafford, Manasi Swaminathan, Kate Tsui, David J. Crandall, and Selma Sabanovic. Is Now a Good Time? Opportune Moments for Interacting with an Ikigai Support Robot. In Companion of the ACM/IEEE International Conference on Human Robot Interaction (HRI), 2024. [⁠paper⁠]
• Manasi Swaminathan, Long-Ling Hsu, Min Min Thant, Kyrie Jig Amon, Anna Kim, Kate Tsui, Selma Sabanovic, David J. Crandall, and Weslie Khoo. If [YourName] can code, so can you! End-user robot programming for non-experts. In Companion of the ACM/IEEE International Conference on Human Robot Interaction (HRI), 2024. [⁠paper⁠]
• Shujon Naha, Seung Woo Chae, Noriko Hara, and David Crandall. Exploring Medical YouTubers' Parasocial Visual Cues in Their COVID-related Videos. In International Conference on Social Media and Society (SMSociety), 2024.
• Katherine M. Tsui, Sarah Cohen, Selma Sabanovic, Alex Alspach, Rune Baggett, David Crandall, and Steffi Paepcke. Uncovering Older Adult Needs: Applying User-Centered Research Methodologies to Inform Robotics Development and a Call to Action. In Human-Robot Interaction - A Multidisciplinary Overview. IntechOpen, 2024. [⁠paper⁠]
• Mang Ye, Shuoyi Chen, Chenyue Li, Wei-Shi Zheng, David Crandall, and Bo Du. Transformer for Object Re-Identification: A Survey. International Journal of Computer Vision (IJCV), November 2024. (impact factor = 15.5). [⁠paper⁠]
• Zheng Chen, Deepak Duggirala, David J. Crandall, Lei Jiang, and Lantao Liu. SePaint: Semantic Map Inpainting via Multinomial Diffusion. In IEEE International Conference on Intelligent Robots and Systems (IROS), 2024. (47% acceptance rate). [⁠paper⁠]
• Sam Goree, Gabriel Appleby, David J. Crandall, and Norman Su. Attention is All They Need: Exploring the Media Archaeology of the Computer Vision Research Paper. In ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW), 2024. (26% acceptance rate). [⁠paper⁠]

2023

• Feng Cheng, Xizi Wang, Jie Lei, David Crandall, Mohit Bansal, and Gedas Bertasius. VindLU: A recipe for Effective Video-and-Language Pretraining. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023. (poster, 25.8% acceptance rate). [⁠paper⁠] [⁠video⁠]
• Sam Goree, Weslie Khoo, and David Crandall. Correct for Whom? Subjectivity and the Evaluation of Personalized Image Aesthetics Assessment Models. In AAAI Conference on Artificial Intelligence (AAAI), 2023. (Oral, 19.6% acceptance rate). [⁠paper⁠]
• Tianfei Zhou, Fatih Porikli, David J. Crandall, Luc Van Gool, and Wenguan Wang. A Survey on Deep Learning Techniques for Video Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), June 2023. (impact factor = 17.861). [⁠paper⁠]
• Yu Yao, Xizi Wang, Mingze Xu, Zelin Pu, Yuchen Wang, Ella Atkins, and David J. Crandall. DoTA: Unsupervised Detection of Traffic Anomaly in Driving Videos. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 45(1):444 - 459, January 2023. (impact factor = 17.861). [⁠paper⁠] [⁠project page⁠]
• Sam Goree, David Crandall, and Norman Su. "It Was Really All About Books:" Speech-like Techno-Masculinity in the Rhetoric of Dot-Com Era Web Design Books. ACM Transactions on Computer-Human Interaction, 30(2):1-27, 2023. (impact factor = 3.147). [⁠video⁠]
• Junbo Yin, Jianbing Shen, Xin Gao, David Crandall, and Ruigang Yang. Graph Neural Network and Spatiotemporal Transformer Attention for 3D Video Object Detection from Point Clouds. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), August 2023. (impact factor = 17.861). [⁠paper⁠]
• Filippo Menczer, David Crandall, Yong-Yeol Ahn, and Apu Kapadia. Addressing the harms of AI-generated inauthentic content. Nature Machine Intelligence, July 2023. (impact factor = 25.898). [⁠paper⁠]
• Imran Kabir, Shaurya Shubham, Vijayalaxmi Bhimrao Maigur, Mahesh Ravindra Latnekar, Mayank Kumar Raunak, Nikhil Shripad Thakurdesai, David Crandall, and Md Reza. Few-shot segmentation and Semantic Segmentation for Underwater Imagery. In IEEE International Conference on Intelligent Robots and Systems (IROS), 2023. (43% acceptance rate).
• Zheng Chen, Zhengming Ding, David Crandall, and Lantao Liu. Polyline Generative Navigable Space Segmentation for Autonomous Visual Navigation. In IEEE International Conference on Intelligent Robots and Systems (IROS), 2023. (43% acceptance rate). [⁠paper⁠]
• Zachary Wilkerson, Vibhas Vats, Karan Acharya, David Leake, and David Crandall. Examining the Impact of Network Architecture on Extracted Feature Quality for CBR. In International Conference on Case-based Reasoning (ICCBR), 2023. (36% acceptance rate). [⁠paper⁠]
• Long-Jing Hsu, Weslie Khoo, Natasha Randall, Waki Kamino, Swapna Joshi, Hiroki Sato, David Crandall, Kate Tsui, and Selma Sabanovic. Finding Its Voice: Exploring the Influence of Robot Voices on Fit, Social Attributes, and Willingness Among Older Adults in the U.S. and Japan. In IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 2023. [⁠paper⁠]
• Jane Yang, Linda Smith, David Crandall, and Chen Yu. Using manual actions to create visual saliency: an outside-in solution to sustained attention and joint attention. In Annual Conference of the Cognitive Science Society (CogSci), 2023. [⁠paper⁠]
• Zheng Chen, Zhengming Ding, David J. Crandall, and Lantao Liu. Polyline Generative Navigable Space Segmentation for Autonomous Visual Navigation. IEEE Robotics and Automation Letters (RA-L), April 2023. (impact factor = 3.741). [⁠paper⁠]
• Sam Goree and David Crandall. Situated Cameras, Situated Knowledges: Towards an Egocentric Epistemology for Computer Vision. In IEEE CVPR Joint International Third Ego4D and Eleventh EPIC Workshop, 2023. [⁠paper⁠]
• Weslie Khoo, Long-Jing Hsu, Kyrie Jig Amon, Pranav Vijay Chakilam, Wei-Chu Chen, Zachary Kaufman, Agness Lungu, Hiroki Sato, Erin Seliger, Manasi Swaminathan, Katherine Tsui, David Crandall, and Selma Sabanovic. Spill the Tea: When Robot Conversation Agents Support Well-being for Older Adults. In Companion of the ACM/IEEE International Conference on Human Robot Interaction (HRI), 2023. [⁠paper⁠]
• David Leake, Zachary Wilkerson, Xiaomeng Ye, and David J. Crandall. Enhancing Case-Based Reasoning with Neural Networks. In Compendium of Neurosymbolic Artificial Intelligence, pages 387-409. IOS Press, 2023. [⁠paper⁠]
• Long-Jing Hsu, Waki Kamino, Weslie Khoo, Katherine Tsui, David J. Crandall, and Selma Sabanovic. Working Together Toward ikigai: Co-Designing Robots That Can Help Us Achieve Meaning and Purpose in Life. XRDS: Crossroads, 30(1):38-45, 2023. [⁠paper⁠]

2022

• Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, Miguel Martin, Tushar Nagarajan, Ilija Radosavovic, Santhosh Kumar Ramakrishnan, Fiona Ryan, Jayant Sharma, Michael Wray, Mengmeng Xu, Eric Zhongcong Xu, Chen Zhao, Siddhant Bansal, Dhruv Batra, Vincent Cartillier, Sean Crane, Tien Do, Morrie Doulaty, Akshay Erapalli, Christoph Feichtenhofer, Adriano Fragomeni, Qichen Fu, Christian Fuegen, Abrham Gebreselasie, Cristina Gonzalez, James Hillis, Xuhua Huang, Yifei Huang, Wenqi Jia, Weslie Khoo, Jachym Kolar, Satwik Kottur, Anurag Kumar, Federico Landini, Chao Li, Yanghao Li, Zhenqiang Li, Karttikeya Mangalam, Raghava Modhugu, Jonathan Munro, Tullie Murrell, Takumi Nishiyasu, Will Price, Paola Ruiz Puentes, Merey Ramazanova, Leda Sari, Kiran Somasundaram, Audrey Southerland, Yusuke Sugano, Ruijie Tao, Minh Vo, Yuchen Wang, Xindi Wu, Takuma Yagi, Yunyi Zhu, Pablo Arbelaez, David Crandall, Dima Damen, Giovanni Maria Farinella, Bernard Ghanem, Vamsi Krishna Ithapu, C. V. Jawahar, Hanbyul Joo, Kris Kitani, Haizhou Li, Richard Newcombe, Aude Oliva, Hyun Soo Park, James M. Rehg, Yoichi Sato, Jianbo Shi, Mike Zheng Shou, Antonio Torralba, Lorenzo Torresani, Mingfei Yan, and Jitendra Malik. Ego4D: Around the World in 3,000 Hours of Egocentric Video. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. (Oral, 14.2% acceptance rate). [⁠paper⁠] [⁠project page⁠] [⁠video⁠]
• Chuhua Wang, Yuchen Wang, Mingze Xu, and David Crandall. Stepwise Goal-Driven Networks for Trajectory Prediction. IEEE Robotics and Automation Letters (RA-L), April 2022. (impact factor = 3.741). [⁠paper⁠] [⁠video⁠]
• Xiankai Lu, Wenguan Wang, Jianbing Shen, David Crandall, and Jiebo Luo. Zero-Shot Video Object Segmentation with Co-Attention Siamese Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 4(4):2228–2242, April 2022. (impact factor = 17.861).
• Xiankai Lu, Wenguan Wang, Jianbing Shen, David J. Crandall, and Luc Van Gool. Segmenting Objects from Relational Visual Data. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), November 2022. (impact factor = 17.861).
• Zhenhua Chen, Chuhua Wang, and David Crandall. Semantically Stealthy Adversarial Attacks against Segmentation Models. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2022. (35.0% acceptance rate). [⁠paper⁠] [⁠video⁠]
• Zehua Zhang and David Crandall. Hierarchically Decoupled Spatial-Temporal Contrast for Self-supervised Video Representation Learning. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2022. (35.0% acceptance rate). [⁠paper⁠] [⁠video⁠]
• Satoshi Tsutsui, Xizi Wang, Guangyuan Weng, Yayun Zhang, David Crandall, and Chen Yu. Action Recognition based on Cross-Situational Action-object Statistics. In IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL), 2022. [⁠paper⁠]
• Xiaomeng Ye, David Leake, and David Crandall. Case Adaptation with Neural Networks: Capabilities and Limitations. In International Conference on Case-based Reasoning (ICCBR), 2022. (38% acceptance rate). [⁠paper⁠]
• David Leake, Zachary Wilkerson, and David Crandall. Extracting Case Indices from Convolutional Neural Networks: A Comparative Study. In International Conference on Case-based Reasoning (ICCBR), 2022. (38% acceptance rate). [⁠paper⁠]
• Xiaomeng Ye, Ziwei Zhao, David Leake, and David Crandall. Generation and Evaluation of Creative Images from Limited Data: A Class-to-Class VAE Approach. In International Conference on Computational Creativity (ICCC), 2022. [⁠video⁠]
• Zehua Zhang, David Crandall, Michael Proulx, Sachin Talathi, and Abhishek Sharma. Can Gaze Inform Egocentric Action Recognition? In ACM Symposium on Eye Tracking Research and Applications (ETRA), 2022. [⁠paper⁠]
• Vibhas Vats and David Crandall. Controlling the Quality of Distillation in Response-Based Network Compression. In AAAI International Workshop on Practical Deep Learning in the Wild, 2022. [⁠paper⁠]
• Ziwei Zhao, David Leake, Xiaomeng Ye, and David Crandall. Generating Counterfactual Images: Toward a C2C-VAE Approach. In International Conference on Case-based Reasoning Workshop on Case-Based Reasoning for the Explanation of Intelligent Systems, 2022. [⁠paper⁠]

2021

• Norman Su and David J. Crandall. The Affective Growth of Computer Vision. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. (Poster, 23.7% acceptance rate). [⁠paper⁠] [⁠video⁠]
• Joshua Smith, Md Alimoor Reza, Nathanael Smith, Jianxin Gu, Maha Ibrar, David Crandall, and Sara Skrabalak. Plasmonic Anti-counterfeit Tags with High Encoding Capacity Rapidly Authenticated with Deep Machine Learning. ACS Nano, 15(2):2901 - 2910, February 2021. (impact factor = 14.588).
• Jagpreet Chawla, Nikhil Shripad Thakurdesai, Anuj Balasaheb Godase, Md Alimoor Reza, David J. Crandall, and Soon-Heung Jung. Error Diagnosis of Deep Monocular Depth Estimation Models. In IEEE International Conference on Intelligent Robots and Systems (IROS), 2021. (45% acceptance rate). [⁠paper⁠] [⁠video⁠]
• Xiaomeng Ye, David Leake, Vahid Jalali, and David Crandall. Learning Adaptations for Case-Based Classification: A Neural Network Approach. In International Conference on Case-based Reasoning (ICCBR), 2021. (oral, 24.7% acceptance rate). [⁠paper⁠]
• Zachary Wilkerson, David Leake, and David Crandall. On Combining Knowledge-Engineered and Network-Extracted Features for Retrieval. In International Conference on Case-based Reasoning (ICCBR), 2021. (oral, 24.7% acceptance rate). [⁠paper⁠] [⁠video⁠]
• Yayun Zhang, Andrei Amatuni, Ellis Cain, Xizi Wang, David Crandall, and Chen Yu. Human Learners Integrate Visual and Linguistic Information in Cross-Situational Verb Learning. In Annual Conference of the Cognitive Science Society (CogSci), 2021. [⁠paper⁠]
• Ryan Peters, Andrei Amatuni, Sara Schroer, Shujon Naha, David Crandall, and Chen Yu. Are you with me? Modeling joint attention from egocentric vision. In Annual Conference of the Cognitive Science Society (CogSci), 2021. [⁠paper⁠]
• Andrei Amatuni, Sara Schroer, Ryan Peters, Md Alimoor Reza, Yayun Zhang, David Crandall, and Chen Yu. In-the-Moment Visual Information Determines Learning. In Annual Conference of the Cognitive Science Society (CogSci), 2021. [⁠paper⁠]
• Sam Goree, Bardia Doosti, David J. Crandall, and Norman Su. Investigating the Homogenization of Web Design: A Mixed-Methods Approach. In ACM CHI Conference on Human Factors in Computing Systems (CHI), 2021. (Oral, 26% acceptance rate). [⁠paper⁠] [⁠video⁠]
• Satoshi Tsutsui, Yanwei Fu, and David J. Crandall. Whose hand is this? Person Identification from Egocentric Hand Gestures. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2021. (34.5% acceptance rate). [⁠paper⁠] [⁠video⁠]
• Shujon Naha, Qingyang Xiao, Prianka Banik, Md Alimoor Reza, and David J. Crandall. Part Segmentation of Unseen Objects Using Keypoint Guidance. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2021. (34.5% acceptance rate). [⁠paper⁠] [⁠video⁠]
• Xiaomeng Ye, Ziwei Zhao, David Leake, Xizi Wang, and David Crandall. Applying the Case Difference Heuristic to Learn Adaptations from Deep Network Features. In IJCAI Workshop on Deep Learning, Case-Based Reasoning, and AutoML: Present and Future Synergies, 2021. [⁠paper⁠] [⁠video⁠]
• Chia-Fang Chung, Alejandra Ramos, Pei-Ni Chiang, Chien-Chun Wu, Connie Anne Tan, Weslie Khoo, and David Crandall. Computer Vision for Dietary Assessment. In CHI Workshop on Realizing AI in Healthcare: Challenges Appearing in the Wild, 2021. [⁠paper⁠] [⁠video⁠]
• David Leake, Xiaomeng Ye, and David Crandall. Supporting Case-Based Reasoning with Neural Networks: An Illustration for Case Adaptation. In AAAI Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE), 2021. [⁠paper⁠]
• Yuchen Wang, Mingze Xu, John Paden, Lara Koenig, Geoffrey C. Fox, and David J. Crandall. Deep Tiered Image Segmentation for Detecting Internal Ice Layers in Radar Imagery. In IEEE International Conference on Multimedia and Expo (ICME), 2021. (Oral, 15% acceptance rate). [⁠paper⁠]

2020

• Mang Ye, Jianbing Shen, David J. Crandall, Ling Shao, and Jiebo Luo. Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-Identification. In European Conference on Computer Vision (ECCV), 2020. (27% acceptance rate). [⁠paper⁠]
• Satoshi Tsutsui, Arjun Chandrasekaran, Md Alimoor Reza, David Crandall, and Chen Yu. A Computational Model of Early Word Learning from the Infant's Point of View. In Annual Conference of the Cognitive Science Society (CogSci), 2020. (Oral, 22% acceptance rate). [⁠paper⁠] [⁠video⁠]
• Rakibul Hasan, David Crandall, Mario Fritz, and Apu Kapadia. Automatically Detecting Bystanders in Photos to Reduce Privacy Risks. In IEEE Security and Privacy (Oakland), 2020. [⁠paper⁠] [⁠video⁠]
• Shujon Naha, Md Alimoor Reza, Chen Yu, and David J. Crandall. Localizing novel attended objects in egocentric views. In British Machine Vision Conference (BMVC), 2020. (Poster, 29.1% acceptance rate). [⁠paper⁠]
• Lei Yuan, Violet Xiang, David Crandall, and Linda Smith. Learning the generative principles of a symbol system from limited examples. Cognition, 2020. (impact factor = 3.537). [⁠paper⁠]
• Roberto Hoyle, Luke Stark, Qatrunnada Ismail, David Crandall, Apu Kapadia, and Denise Anthony. Privacy Norms and Preferences for Photos Posted Online. ACM Transactions on Computer-Human Interaction, 2020. (impact factor = 2.227). [⁠paper⁠] [⁠video⁠]
• David Leake and David Crandall. Bringing Case Based Reasoning to Deep Learning. In International Conference on Case-Based Reasoning Special Track on Challenges and Promises, 2020. [⁠paper⁠]
• Sam Goree and David Crandall. Studying Empirical Color Harmony in Design. In IEEE Conference on Computer Vision and Pattern Recognition Workshop on Computer Vision for Fashion, Art, and Design, 2020. [⁠paper⁠] [⁠video⁠]
• Md Alimoor Reza, Kai Chen, Akshay Naik, David J. Crandall, and Soon-Heung Jung. Automatic dense annotation for monocular 3d scene understanding. IEEE Access, 8:68852 - 68865, 2020. (impact factor = 4.098). [⁠paper⁠]
• Md Alimoor Reza, Zhenhua Chen, and David J. Crandall. Deep Neural Network-based Detection and Verification of Microelectronic Images. Journal of Hardware and Systems Security, 4:44–54, 2020. (impact factor = 1.278). [⁠paper⁠]
• Sam Goree, Bardia Doosti, David Crandall, and Norman Su. Yes, websites really are starting to look more similar. The Conversation, 2020. [⁠paper⁠]
• Ishtiak Zaman and David Crandall. Genetic-GAN: Synthesizing images between two domains by genetic crossover. In European Conference on Computer Vision Workshop on Advances in Manipulation, 2020. [⁠project page⁠]
• Shujon Naha, Qingyang Xiao, Prianka Banik, Md Alimoor Reza, and David J. Crandall. Pose-guided knowledge transfer for object part segmentation. In IEEE Conference on Computer Vision and Pattern Recognition Workshop on Visual Learning with Limited Labels, 2020. [⁠paper⁠]
• Oluwanisola Ibikunle, John Paden, Maryam Rahnemoonfar, David Crandall, and Masoud Yari. Snow Radar Layer Tracking using an Iterative Neural Network Approach. In IEEE International Geoscience and Remote Sensing Symposium, 2020.
• Bardia Doosti, Shujon Naha, Majid Mirbagheri, and David Crandall. HOPE-Net: A Graph-based Model for Hand-Object Pose Estimation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. (Poster, 22.1% acceptance rate). [⁠paper⁠] [⁠project page⁠] [⁠video⁠]
• Xiankai Lu, Wenguan Wang, Jianbing Shen, Yu-Wing Tai, David Crandall, and Steven Hoi. Learning Video Object Segmentation from Unlabeled Videos. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. (Poster, 22.1% acceptance rate). [⁠paper⁠]
• Zehua Zhang, Ashish Tawari, Sujitha Martin, and David Crandall. Interaction Graph for Object Importance Estimation in On-road Driving Videos. In IEEE Conference on Robotics and Automation (ICRA), 2020. (Oral, 42% acceptance rate). [⁠paper⁠] [⁠video⁠]

2019

• Zehua Zhang, Chen Yu, and David Crandall. A Self Validation Network for Object-Level Human Attention Estimation. In Advances in Neural Information Processing Systems (NeurIPS), 2019. (Poster, 21.6% acceptance rate). [⁠paper⁠] [⁠project page⁠]
• Jianwei Yang, Zhile Ren, Mingze Xu, Xinlei Chen, David Crandall, Devi Parikh, and Dhruv Batria. Embodied Visual Recognition: Learning to Move for Amodal Perception. In IEEE International Conference on Computer Vision (ICCV), 2019. (Poster, 25.0% acceptance rate). [⁠paper⁠]
• Mingze Xu, Mingfei Gao, Yi-Ting Chen, Larry Davis, and David J. Crandall. Temporal Recurrent Networks for Online Action Detection. In IEEE International Conference on Computer Vision (ICCV), 2019. (Poster, 25.0% acceptance rate). [⁠paper⁠]
• Yu Yao, Mingze Xu, Chiho Choi, David J. Crandall, Ella M. Atkins, and Behzad Dariush. Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems. In IEEE Conference on Robotics and Automation (ICRA), 2019. (Oral, 44% acceptance rate). [⁠paper⁠]
• Rakibul Hasan, Yifang Li, Eman Hassan, Kelly Caine, David J. Crandall, Roberto Hoyle, and Apu Kapadia. Can Privacy Be Satisfying? On Improving Viewer Satisfaction for Privacy-Enhanced Photos Using Aesthetic Transforms. In ACM CHI Conference on Human Factors in Computing Systems (CHI), 2019. (Oral, 23.8% acceptance rate). [⁠paper⁠]
• Wenguan Wang, Xiankai Lu, Jianbing Shen, David Crandall, and Ling Shao. Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks. In IEEE International Conference on Computer Vision (ICCV), 2019. (Oral, 25.0% acceptance rate). [⁠paper⁠]
• Yu Yao, Mingze Xu, Yuchen Wang, David Crandall, and Ella Atkins. Unsupervised Traffic Accident Detection in First-Person Videos. In IEEE International Conference on Intelligent Robots and Systems (IROS), 2019. (Oral, 45.0% acceptance rate). [⁠paper⁠]
• Md Alimoor Reza, Akshay Naik, Kai Chen, and David Crandall. Automatic Annotation for Semantic Segmentation in Indoor Scenes. In IEEE International Conference on Intelligent Robots and Systems (IROS), 2019. (Oral, 45.0% acceptance rate). [⁠paper⁠]
• Jangwon Lee, Bardia Doosti, Yupeng Gu, David Cartledge, David J. Crandall, and Christopher Raphael. Observing Pianist Accuracy and Form with Computer Vision. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2019. (Poster+Oral, 39% acceptance rate). [⁠paper⁠]
• Suzanne Menzel, Katie Siek, and David Crandall. Hello Research! Developing an Intensive Research Experience for Undergraduate Women. In ACM Technical Symposium on Computer Science Education (SIGCSE), 2019. (Oral, 34% acceptance rate). [⁠paper⁠] [⁠project page⁠]
• Jeremy I. Borjon, Sara E. Schroer, Sven Bambach, Lauren K. Slone, Drew H. Abney, David J. Crandall, and Linda B. Smith. A View of Their Own: Capturing the Egocentric View of Infants and Toddlers with Head-Mounted Cameras. Journal of Visualized Experiments, 2019. (impact factor = 1.325).
• Hadar Karmazyn Raz, Drew H. Abney, David Crandall, Chen Yu, and Linda Smith. How do infants start learning object names in a sea of clutter? In Annual Conference of the Cognitive Science Society (CogSci), 2019. [⁠paper⁠]
• Aniruddha M. Godbole and David J. Crandall. Empowering Borrowers in their Choice of Lenders: Decoding Service Quality from Customer Complaints. In ACM International Web Science Conference (WebSci), 2019. (Oral, 34.2% acceptance rate). [⁠paper⁠] [⁠project page⁠]
• Katie Spoon, David Crandall, and Katie Siek. Towards Detecting Dyslexia in Children's Handwriting Using Neural Networks. In ICML Workshop on AI for Social Good, 2019. Best poster award. [⁠paper⁠] [⁠project page⁠]
• Satoshi Tsutsui, Dian Zhi, Md Alimoor Reza, David Crandall, and Chen Yu. Active Object Manipulation Facilitates Visual Object Learning: An Egocentric Vision Study. In IEEE CVPR Workshop on Egocentric Perception, Interaction, and Computing (EPIC), 2019. [⁠paper⁠]
• Zhenhua Chen, Chuhua Wang, Tiancong Zhao, and David Crandall. Generalized Capsule Networks with Trainable Routing Procedure. In International Conference on Machine Learning Workshop on Generalization, 2019. [⁠paper⁠]
• Tousif Ahmed, Rakibul Hasan, Kay Connelly, David Crandall, and Apu Kapadia. Conveying Situational Information to People with Visual Impairments. In CHI Workshop on Addressing the Challenges of Situationally-Induced Impairments and Disabilities in Mobile Interaction, 2019. [⁠paper⁠]
• David Crandall. Artificial Intelligence and Manufacturing. In Smart Factories: Issues of Information Governance. 2019. [⁠paper⁠]
• Geoffrey Fox, Judy Qiu, David Crandall, Gregor Von Laszewski, Oliver Beckstein, John Paden, Ioannis Paraskevakos, Shantenu Jha, Fusheng Wang, Madhav Marathe, Anil Vullikanti, and Thomas Cheatham. Contributions to High-Performance Big Data Computing. In Future Trends of HPC in a Disruptive Scenario, volume 34, pages 34 - 81. IOS Press, 2019. [⁠paper⁠]
• Victor Berger, Mingze Xu, Mohanad Al-Ibadi, Shane Chu, David Crandall, John Paden, and Geoffrey Fox. Automated Ice-Bottom Tracking of 2D and 3D Ice Radar Imagery Using Viterbi and TRW-S. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS), 12(9):3272 – 3285, 2019. (impact factor = 3.392). [⁠paper⁠]
• Noam Levin, Saleem Ali, David Crandall, and Salit Kark. World Heritage in danger: Big data and remote sensing can help protect sites in conflict zones. Global Environmental Change, 55:97–104, 2019. (impact factor = 6.371). [⁠paper⁠]
• Satoshi Tsutsui, Yanwei Fu, and David Crandall. Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual Recognition. In Advances in Neural Information Processing Systems (NeurIPS), 2019. (Poster, 21.6% acceptance rate). [⁠paper⁠] [⁠project page⁠]

2018

• Sven Bambach, David Crandall, Linda Smith, and Chen Yu. Toddler-Inspired Visual Object Learning. In Advances in Neural Information Processing Systems (NeurIPS), 2018. (Poster, 20.8% acceptance rate). [⁠paper⁠]
• Mingze Xu, Chenyou Fan, Yuchen Wang, Michael Ryoo, and David Crandall. Joint Person Segmentation and Identification in Synchronized First- and Third-person Videos. In European Conference on Computer Vision (ECCV), 2018. (Poster). [⁠paper⁠] [⁠project page⁠]
• Rakibul Hasan, Eman Hassan, Yifang Li, Kelly Caine, David J. Crandall, Roberto Hoyle, and Apu Kapadia. Viewer Experience of Obscuring Scene Elements in Photos to Enhance Privacy. In ACM CHI Conference on Human Factors in Computing Systems (CHI), 2018. (Oral, 25.7% acceptance rate). [⁠paper⁠]
• Ashwin Vijayakumar, Michael Cogswell, Ramprasaath Selvaraju, Qing Sun, Stefan Lee, David Crandall, and Dhruv Batra. Diverse Beam Search for Improved Description of Complex Scenes. In AAAI Conference on Artificial Intelligence (AAAI), 2018. (Poster, 24.6% acceptance rate). [⁠paper⁠]
• Satoshi Tsutsui, Sven Bambach, David Crandall, and Chen Yu. Estimating Head Motion from Egocentric Vision. In ACM International Conference on Multimodal Interaction (ICMI), 2018. [⁠paper⁠]
• Zehua Zhang, Sven Bambach, David Crandall, and Chen Yu. From Coarse Attention to Fine-Grained Gaze: A Two-stage 3D Fully Convolutional Network for Predicting Eye Gaze in First Person Video. In British Machine Vision Conference (BMVC), 2018. (Oral, 4.3% acceptance rate). [⁠paper⁠] [⁠project page⁠]
• Mingze Xu, Aidean Sharghi, Xin Chen, and David Crandall. Fully-Coupled Two-Stream Spatiotemporal Networks for Extremely Low Resolution Action Recognition. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2018. (40% acceptance rate). [⁠paper⁠]
• Mingze Xu, Chenyou Fan, John Paden, Geoffrey Fox, and David Crandall. Multi-Task Spatiotemporal Neural Networks for Structured Surface Reconstruction. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2018. (40% acceptance rate). [⁠paper⁠] [⁠project page⁠] [⁠video⁠]
• Mohanad Al-Ibadi, Jordan Sprick, Sravya Athinarapu, Victor Berger, Theresa Stumpf, John Paden, Carl Leuschen, Fernando Rodriguez, Mingze Xu, David Crandall, Geoffrey Fox, David Burgess, Martin Sharp, Luke Copland, and Wesley Van Wychen. Crossover Analysis and Automated Layer-Tracking Assessment of the Extracted DEM of the Basal Topography of the Canadian Arctic Archipelago Ice-Cap. In IEEE Radar Conference, 2018. [⁠paper⁠]
• Victor Berger, Mingze Xu, David Crandall, John Paden, and Geoffrey Fox. Automated Tracking of 2d and 3d Ice Radar Imagery using Viterbi and TRW-S. In IEEE International Geoscience and Remote Sensing Symposium, 2018.
• Satoshi Tsutsui, Tommi Kerola, Shunta Saito, and David Crandall. Minimizing Supervision for Free-space Segmentation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshop on Autonomous Driving (WAD), 2018. [⁠paper⁠]
• Jangwon Lee, Haodan Tan, David Crandall, and Selma Sabanovic. Forecasting Hand Gestures for Human-Drone Interaction. In ACM/IEEE International Conference on Human Robot Interaction (HRI), 2018. (Late-breaking Report). [⁠paper⁠]
• Bo-chiuan Chen, Dong-Chul Seo, Hsien-Chang Lin, and David Crandall. A Framework for Estimating Sleep Timing from Digital Footprints. BMJ Innovations, 2018. (impact factor = 2.899). [⁠paper⁠]
• Chenyou Fan, Zehua Zhang, and David Crandall. Deepdiary: Lifelogging image captioning and summarization. Journal of Visual Communication and Image Representation, 55:40-55, August 2018. (impact factor = 2.164). [⁠paper⁠] [⁠project page⁠]
• Noam Levin, Saleem Ali, and David Crandall. Utilizing remote sensing and big data to quantify conflict intensity: The Arab Spring as a case study. Applied Geography, 2018. (impact factor = 2.56). [⁠paper⁠]

2017

• Scott Workman, Menghua Zhai, David Crandall, and Nathan Jacobs. A unified model for near and remote sensing. In IEEE International Conference on Computer Vision (ICCV), 2017. (Poster, 28% acceptance rate). [⁠paper⁠]
• Chenyou Fan, Jangwon Lee, Mingze Xu, Krishna Kumar Singh, Yong Jae Lee, David Crandall, and Michael Ryoo. Identifying first-person camera wearers in third-person videos. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. (Poster, 29.0% acceptance rate). [⁠paper⁠] [⁠project page⁠]
• Wen Chen, David Crandall, and Norman Su. Understanding the Aesthetic Evolution of Websites: Towards a Notion of Design Periods. In ACM CHI Conference on Human Factors in Computing Systems (CHI), 2017. (Oral, 25.0% acceptance rate). [⁠paper⁠]
• Mingze Xu, David J. Crandall, Geoffrey C. Fox, and John D. Paden. Automatic estimation of ice bottom surfaces from radar imagery. In IEEE International Conference on Image Processing (ICIP), 2017. (Oral, 45.0% acceptance rate). [⁠paper⁠] [⁠project page⁠]
• Satoshi Tsutsui and David J. Crandall. A Data Driven Approach for Compound Figure Separation Using Convolutional Neural Networks. In IAPR International Conference on Document Analysis and Recognition (ICDAR), 2017. [⁠paper⁠] [⁠project page⁠]
• Bardia Doosti, David J. Crandall, and Norman Makoto Su. A deep study into the history of web design. In ACM International Web Science Conference (WebSci), 2017. (Oral, 35.3% acceptance rate). [⁠paper⁠] [⁠project page⁠]
• Sven Bambach, David Crandall, Linda Smith, and Chen Yu. An Egocentric Perspective on Active Vision and Visual Object Learning in Toddlers. In IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL), 2017. (Oral, 37.1% acceptance rate). [⁠paper⁠]
• Eman Hassan, Rakibul Hasan, Patrick Shaffer, David Crandall, and Apu Kapadia. Cartooning for enhanced privacy in lifelogging and streaming video. In IEEE Conference on Computer Vision and Pattern Recognition Workshop on Computer Vision Challenges and Opportunities for Privacy and Security (CVPR CV-COPS), 2017. [⁠paper⁠]
• Zhenhua Chen, Tingyi Wanyan, Ramya Rao, Benjamin Cutilli, James Sowinski, David Crandall, and Robert Templeman. Addressing supply chain risks of microelectronic devices through computer vision. In IEEE Applied Imagery Pattern Recognition Workshop (AIPR), 2017. [⁠paper⁠]
• Sven Bambach, Zehua Zhang, David Crandall, and Chen Yu. Exploring inter-observer differences in first-person object views using deep learning models. In IEEE International Conference on Computer Vision Workshop on Mutual Benefits of Cognitive and Computer Vision, 2017. [⁠paper⁠]
• Jangwon Lee, Jingya Wang, David Crandall, Selma Sabanovic, and Geoffrey Fox. Real-Time, Cloud-based Object Detection for Unmanned Aerial Vehicles. In IEEE Robotic Computing, 2017. (Oral). [⁠paper⁠]
• Mohanad Al-Ibadi, Jordan Sprick, Sravya Athinarapu, Theresa Stumpf, John Paden, Carl Leuschen, Fernando Rodriguez, Mingze Xu, David Crandall, Geoffrey Fox, David Burgess, Martin Sharp, Luke Copland, and Wesley Van Wychen. DEM Extraction of the Basal Topography of the Canadian Archipelago Ice Caps via 2d automated layer-tracking. In IEEE International Geoscience and Remote Sensing Symposium, 2017. (Oral). [⁠paper⁠]
• Satoshi Tsutsui, Guilin Meng, Xiaohui Yao, David Crandall, and Ying Ding. Analyzing figures of brain images from Alzheimer's Disease Papers. In iConference, 2017. [⁠paper⁠]
• Tousif Ahmed, Roberto Hoyle, Patrick Shaffer, Kay Connelly, David Crandall, and Apu Kapadia. Understanding the Physical Safety, Security, and Privacy Concerns of People with Visual Impairments. IEEE Internet Computing, 21(3):56 – 63, 2017. (impact factor = 2.0).
• Johan Bollen, David Crandall, Damion Junk, Ying Ding, and Katy Borner. An efficient system to fund science: from proposal review to peer-to-peer distributions. Scientometrics, 110(1):521–528, 2017. (impact factor = 2.084).

2016

• Mohammed Korayem, Robert Templeman, Dennis Chen, David Crandall, and Apu Kapadia. Enhancing Lifelogging Privacy by Detecting Screens. In ACM CHI Conference on Human Factors in Computing Systems (CHI), 2016. Honorable Mention Award. (CHI Note, 23.4% acceptance rate). [⁠paper⁠] [⁠project page⁠]
• Sven Bambach, David Crandall, Linda Smith, and Chen Yu. Active Viewing in Toddlers Facilitates Visual Object Learning: An Egocentric Vision Approach. In Annual Conference of the Cognitive Science Society (CogSci), 2016. (Oral, 34% acceptance rate). [⁠paper⁠]
• Jingya Wang, Mohammed Korayem, Saul Blanco, and David Crandall. Tracking Natural Events through Social Media and Computer Vision. In ACM International Conference on Multimedia (MM), 2016. [⁠paper⁠] [⁠project page⁠]
• Sven Bambach, Linda Smith, David Crandall, and Chen Yu. Objects in the Center: How the Infant's Body Constrains Infant Scenes. In IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL), 2016. Best paper award. (Oral, 33.7% acceptance rate). [⁠paper⁠]
• Chenyou Fan and David Crandall. DeepDiary: Automatically Captioning Lifelogging Image Streams. In European Conference on Computer Vision International Workshop on Egocentric Perception, Interaction, and Computing (EPIC), 2016. [⁠paper⁠] [⁠project page⁠]
• David Crandall, Yunpeng Li, Stefan Lee, and Daniel Huttenlocher. Recognizing landmarks in large-scale social image collections. In Asaad Hakeem, Richard Szeliski, Mubarak Shah, Luc Van Gool, and Amir Zamir, editors, Visual Analysis and Geolocalization of Large Scale Imagery. Springer, 2016. [⁠paper⁠]
• Tousif Ahmed, Patrick Shaffer, Kay Connelly, David Crandall, and Apu Kapadia. Addressing Physical Safety, Security, and Privacy for People with Visual Impairments. In USENIX Symposium on Usable Privacy and Security (SOUPS), 2016. (Oral, 27.8% acceptance rate). [⁠paper⁠]
• Tousif Ahmed, Roberto Hoyle, Patrick Shaffer, Kay Connelly, David Crandall, and Apu Kapadia. Considering Privacy Implications of Assistive Devices for People with Visual Impairments. In CHI Workshop on Interactive Systems in Healthcare (WISH), 2016.
• Kathy Tang and David Crandall. Applying Deep Learning to Improve Maritime Situational Awareness. In ACM International Conference on Knowledge Discovery and Data Mining Workshop on Large-scale Deep Learning for Data Mining, 2016. [⁠video⁠]
• Mohammed Korayem, Khalifeh Aljadda, and David Crandall. Sentiment/Subjectivity Analysis Survey for Languages other than English. Social Network Analysis and Mining, 2016. [⁠paper⁠]
• Noam Levin, David Crandall, and Salit Kark. Scale matters: differences between local, regional, and global analyses (letter to the editor). Ecological Applications, 26(7):2359–2362, December 2016. (impact factor = 4.126).
• Sven Bambach. Analyzing Hands with First-Person Computer Vision. PhD thesis, Indiana University, 2016. (Ph.D. Dissertation).
• Stefan Lee. Data-driven Computer Vision for Science and the Humanities. PhD thesis, Indiana University, 2016. (Ph.D. Dissertation).
• Sumit Gupta. Evaluation of Convolutional Neural Networks for Infrared, Fine-grained Recognition, and Ego-centric Scene Classification. Master's thesis, Indiana University, 2016. (M.S. Thesis).
• Manu Singh. Tag selection and propagation for large-scale visual landmark recognition. Master's thesis, Indiana University, 2016. (M.S. Thesis).
• Stefan Lee, Senthil Purushwalkam, Michael Cogswell, Viresh Ranjan, David Crandall, and Dhruv Batra. Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles. In Advances in Neural Information Processing Systems (NeurIPS), 2016. (Poster, 22.7% acceptance rate). [⁠paper⁠]

2015

• Sven Bambach, Stefan Lee, David Crandall, and Chen Yu. Lending A Hand: Detecting Hands and Recognizing Activities in Complex Egocentric Interactions. In IEEE International Conference on Computer Vision (ICCV), 2015. (Poster, 30.3% acceptance rate). [⁠paper⁠] [⁠project page⁠]
• Stefan Lee, Nicolas Maisonneuve, David Crandall, Alexei Efros, and Josef Sivic. Linking past to present: Discovering style in two centuries of architecture. In IEEE International Conference on Computational Photography (ICCP), 2015. (Oral, 24% acceptance rate). [⁠paper⁠] [⁠project page⁠]
• Roberto Hoyle, Robert Templeman, Denise Anthony, David Crandall, and Apu Kapadia. Sensitive Lifelogs: A Privacy Analysis of Photos from Wearable Cameras. In ACM CHI Conference on Human Factors in Computing Systems (CHI), 2015. (CHI Note). [⁠paper⁠]
• Sven Bambach, David Crandall, and Chen Yu. Viewpoint Integration for Hand-Based Recognition of Social Interactions from a First-Person View. In ACM International Conference on Multimodal Interaction (ICMI), 2015. (Poster, 41% acceptance rate). [⁠paper⁠]
• Tousif Ahmed, Roberto Hoyle, Kay Connelly, David Crandall, and Apu Kapadia. Privacy Concerns and Behaviors of People with Visual Impairments. In ACM CHI Conference on Human Factors in Computing Systems (CHI), 2015. (Full paper, 23% acceptance rate). [⁠paper⁠]
• Stefan Lee, Haipeng Zhang, and David Crandall. Predicting Geo-informative Attributes in Large-scale Image Collections using Convolutional Neural Networks. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2015. (Oral and poster, 36.7% acceptance rate). [⁠paper⁠] [⁠project page⁠]
• Roberto Hoyle, Apu Kapadia, and David Crandall. Challenges in Running Wearable Camera-Related User Studies. In ACM Conference on Computer-Supported Cooperative Work and Social Computing Workshop on The Future of Networked Privacy: Challenges and Opportunities, 2015. [⁠paper⁠]
• Supun Kamburugamuve, Hengjing He, Geoffrey Fox, and David Crandall. Cloud-based parallel implementation of SLAM for mobile robots. In International Supercomputing Conference (ISC) Cloud & Big Data Conference, 2015.
• Kun Duan, Dhruv Batra, and David Crandall. Human pose estimation through composite multi-layer models. Signal Processing, 110:15-26, May 2015. (impact factor = 2.238). [⁠paper⁠] [⁠project page⁠]
• Noam Levin, Salit Kark, and David Crandall. Where have all the people gone? Enhancing global conservation using night lights and social media. Ecological Applications, 25(8):2153–2167, December 2015. (impact factor = 4.126). [⁠paper⁠]
• Mohammed Korayem. Social and Egocentric Image Classification for Scientific and Privacy Applications. PhD thesis, Indiana University, 2015. (Ph.D. Dissertation).
• Devendra Dhami. Morphological Classification of Galaxies into Spirals and Non-Sprirals. Master's thesis, Indiana University, 2015. (M.S. Thesis).
• Harsh Seth. Automated Answering Apps for People with Visual Impairments using Google Glass. Master's thesis, Indiana University, 2015. (M.S. Thesis).

2014

• Stefan Lee, Sven Bambach, David Crandall, John M. Franchak, and Chen Yu. This Hand is My Hand: A Probabilistic Approach to Hand Disambiguation in Egocentric Video. In IEEE Conference on Computer Vision and Pattern Recognition Workshop on Egocentric Vision, 2014. Best paper award winner. (Oral). [⁠paper⁠]
• Robert Templeman, Roberto Hoyle, David Crandall, and Apu Kapadia. Reactive Security: Responding to Visual Stimuli from Wearable Cameras. In Ubicomp Workshop on Usable Privacy and Security for Wearable and Domestic Ubiquitous Devices (UPSIDE), 2014. [⁠paper⁠]
• Johan Bollen, David Crandall, Damion Junk, Ying Ding, and Katy Borner. From funding agencies to scientific agency: Collective allocation of science funding as an alternative to peer review. EMBO Reports, 15:131-133, 2014. (impact factor = 7.189). [⁠paper⁠]
• Kun Duan, David Crandall, and Dhruv Batra. Multimodal Learning in Loosely-organized Web Images. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014. (Poster, 29.9% acceptance rate). [⁠paper⁠]
• Robert Templeman, Mohammed Korayem, David Crandall, and Apu Kapadia. PlaceAvoider: Steering First-Person Cameras away from Sensitive Spaces. In Network and Distributed System Security Symposium (NDSS), 2014. (Oral, 18.6% acceptance rate). [⁠paper⁠]
• Stefan Lee, Jerome Mitchell, David Crandall, and Geoffrey C. Fox. Estimating Bedrock and Surface Layer Boundaries and Confidence Intervals in Ice Sheet Radar Imagery using MCMC. In IEEE International Conference on Image Processing (ICIP), 2014. (Oral, 44% acceptance rate). [⁠paper⁠] [⁠project page⁠]
• Roberto Hoyle, Robert Templeman, Steven Armes, Denise Anthony, David Crandall, and Apu Kapadia. Privacy Behaviors of Lifeloggers using Wearable Cameras. In ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), 2014. (Oral, 20.7% acceptance rate). [⁠paper⁠] [⁠video⁠]
• Sven Bambach, John Franchak, David Crandall, and Chen Yu. Detecting Hands in Children's Egocentric Views to Understand Embodied Attention during Social Interaction. In Annual Conference of the Cognitive Science Society (CogSci), 2014. (Oral, 41.0% acceptance rate). [⁠paper⁠]
• Kun Duan, Luca Marchesotti, and David Crandall. Vehicle Recognition with Constrained Multiple Instance SVMs. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2014. (Oral and poster, 40% acceptance rate). [⁠paper⁠]
• Haipeng Zhang, Zhixian Yan, Jun Yang, Emmanuel Munguia Tapia, and David Crandall. mFingerprint: Privacy-preserving user modeling with multimodal mobile device footprints. In International Conference on Social Computing, Behavior-Cultural Modeling, & Prediction (SBP), 2014. (Oral, 24% acceptance rate). [⁠paper⁠]
• Haipeng Zhang. Analyzing the dynamics between the user-sensed data and the real world. PhD thesis, Indiana University, 2014. (Ph.D. Dissertation).
• Kun Duan. Conditional random field models for structured visual object recognition. PhD thesis, Indiana University, 2014. (Ph.D. Dissertation).
• Johan Bollen, David Crandall, Damion Junk, Ying Ding, and Katy Borner. Response: "Why we still need grant peer review". EMBO Reports, 15(5):467, May 2014. (impact factor = 7.189). [⁠paper⁠]

2013

• Jingya Wang, Mohammed Korayem, and David Crandall. Observing the natural world with Flickr. In International Conference on Computer Vision Workshop on Computer Vision for Converging Perspectives, 2013. Best paper award winner. (Oral). [⁠paper⁠]
• Jerome E. Mitchell, David Crandall, Geoffrey Fox, and John Paden. Automatic Near Surface Estimation from Snow Radar Imagery. In IEEE International Geoscience and Remote Sensing Symposium, 2013. (Oral). [⁠paper⁠]
• Jerome E. Mitchell, David Crandall, Geoffrey C. Fox, Maryam Rahnemoonfar, and John D. Paden. A Semi-Automatic Approach for Estimating Bedrock and Surface Layers from Multichannel Coherent Radar Depth Sounder Imagery. In SPIE Conference on Remote Sensing, 2013. (Oral).
• David Crandall, Andrew Owens, Noah Snavely, and Daniel Huttenlocher. SfM with MRFs: Discrete-Continuous Optimization for Large-Scale Structure from Motion. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 35(12):2841-2853, December 2013. (impact factor = 4.795). [⁠paper⁠] [⁠project page⁠]
• Robert Templeman, Zahidur Rahman, David Crandall, and Apu Kapadia. PlaceRaider: Virtual Theft in Physical Spaces with Smartphones. In Network and Distributed System Security Symposium (NDSS), 2013. (Oral, 18% acceptance rate). [⁠paper⁠] [⁠project page⁠]
• Sven Bambach, David Crandall, and Chen Yu. Understanding Embodied Visual Attention in Child-Parent Interaction. In IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL), 2013. (Oral, 33% acceptance rate). [⁠paper⁠]
• Mohammed Korayem and David Crandall. De-anonymizing users across heterogeneous social computing platforms. In AAAI International Conference on Weblogs and Social Media (ICWSM), 2013. (Poster). [⁠paper⁠]
• Haipeng Zhang, Nish Parikh, Gyanit Singh, and Neel Sundaresan. Chelsea Won, and You Bought a T-shirt: Characterizing the Interplay Between Twitter and E-Commerce. In Advances in Social Network Analysis and Mining (ASONAM), 2013.

2012

• Mohammed Korayem, Abdallah A. Mohamed, David Crandall, and Roman V. Yampolskiy. Learning visual features for the Avatar Captcha Recognition Challenge. In International Conference on Machine Learning Applications (ICMLA), 2012. [⁠paper⁠]
• Mohammed Korayem, Abdallah A. Mohamed, David Crandall, and Roman V. Yampolskiy. Solving Avatar Captchas automatically. In International Conference on Advanced Machine Learning Technologies and Applications, 2012. (Oral). [⁠paper⁠]
• Mohammed Korayem, David Crandall, and Muhammad Abdul-Mageed. Subjectivity and Sentiment Analysis of Arabic: A Survey. In International Conference on Advanced Machine Learning Technologies and Applications, 2012. (Poster). [⁠paper⁠]
• David Crandall and Noah Snavely. Modeling people and places with internet photo collections. Communications of the ACM (CACM), 55(6):52–60, 2012. (impact factor = 2.51) Also appeared in ACM Queue magazine. [⁠paper⁠]
• Kun Duan, Devi Parikh, David Crandall, and Kristen Grauman. Discovering Localized Attributes for Fine-grained Recognition. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012. (Poster, 26% acceptance rate). [⁠paper⁠] [⁠project page⁠]
• Haipeng Zhang, Mohammed Korayem, David Crandall, and Gretchen LeBuhn. Mining Photo-sharing Websites to Study Ecological Phenomena. In International World Wide Web Conference (WWW), 2012. (Oral, 12% acceptance rate). [⁠paper⁠] [⁠project page⁠]
• Kun Duan, Dhruv Batra, and David Crandall. A Multi-layer Composite Model for Human Pose Estimation. In British Machine Vision Conference (BMVC), 2012. (Poster, 32% acceptance rate). [⁠paper⁠]
• David Crandall, Geoffrey Fox, and John Paden. Layer-finding in radar echograms using probabilistic graphical models. In IAPR International Conference on Pattern Recognition (ICPR), 2012. (Oral, 15% acceptance rate). [⁠paper⁠] [⁠project page⁠]
• Haipeng Zhang, Mohammed Korayem, Erkang You, and David Crandall. Beyond Co-occurrence: Discovering and Visualizing Tag Relationships from Geo-spatial and Temporal Similarities. In ACM International Conference on Web Search and Data Mining (WSDM), 2012. (Oral, 8.3% acceptance rate). [⁠paper⁠] [⁠project page⁠] [⁠video⁠]

2011

• David Crandall, Andrew Owens, Noah Snavely, and Daniel Huttenlocher. Discrete-Continuous Optimization for Large-scale Structure from Motion. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011. Best paper runner-up. (Oral, 3.5% acceptance rate). [⁠paper⁠] [⁠project page⁠]
• David Crandall and Noah Snavely. Networks of Landmarks, Photos, and People. Leonardo, 44(3):240–243, 2011. [⁠paper⁠]

2010

• David Crandall, Lars Backstrom, Daniel Cosley, Siddharth Suri, Daniel Huttenlocher, and Jon Kleinberg. Inferring Social Ties from Geographic Coincidences. Proceedings of the National Academy of Sciences (PNAS), 107(52):22436–22441, 2010. (impact factor = 9.737). [⁠paper⁠]

2009

• Yunpeng Li, David Crandall, and Daniel Huttenlocher. Landmark Classification in Large-scale Image Collections. In IEEE International Conference on Computer Vision (ICCV), 2009. (Poster, 23.2% acceptance rate). [⁠paper⁠]
• David Crandall, Lars Backstrom, Daniel Huttenlocher, and Jon Kleinberg. Mapping the World's Photos. In International World Wide Web Conference (WWW), 2009. Best paper honorable mention. (Oral, 13% acceptance rate). [⁠paper⁠] [⁠project page⁠]

2008

• David Crandall, Daniel Cosley, Daniel Huttenlocher, Jon Kleinberg, and Sid Suri. Feedback Effects between Similarity and Social Influence in Online Communities. In ACM International Conference on Knowledge Discovery and Data Mining (KDD), 2008. (Oral, 18.6% acceptance rate). [⁠paper⁠] [⁠video⁠]
• David Crandall. Part-based Statistical Models for Visual Object Class Recognition. PhD thesis, Cornell University, 2008. (Ph.D. Dissertation).

2007

• David Crandall and Daniel Huttenlocher. Composite Models of Objects and Scenes for Category Recognition. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2007. (Poster, 23.4% acceptance rate). [⁠paper⁠]
• David Crandall, Pedro Felzenszwalb, and Daniel Huttenlocher. Object Recognition by Combining Appearance and Geometry. In J. Ponce, M. Hebert, C. Schmid, and A. Zisserman, editors, Toward Category-Level Object Recognition. Springer, 2007. [⁠paper⁠] [⁠project page⁠]

2006

• Jiebo Luo and David Crandall. Color Object Detection using Spatial-Color Joint Probability Functions. IEEE Transactions on Image Processing (TIP), 15(6):1443–1453, 2006. (impact factor = 3.199).
• David Crandall and Daniel Huttenlocher. Weakly supervised learning of part-based spatial models for visual object recognition. In European Conference on Computer Vision (ECCV), 2006. (Oral, 4.4% acceptance rate). [⁠paper⁠] [⁠project page⁠]

2005

• David Crandall, Pedro Felzenszwalb, and Daniel Huttenlocher. Spatial Priors for Part-Based Recognition using Statistical Models. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2005. (Oral, 3.7% acceptance rate). [⁠paper⁠] [⁠project page⁠]

2004

• David Crandall and Jiebo Luo. Robust Color Object Detection using Spatial-Color Joint Probability Functions. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2004. (Poster, 23.6% acceptance rate). [⁠paper⁠]

2003

• Jiebo Luo, David Crandall, Amit Singhal, Matthew Boutell, and Robert Gray. Psychophysical study of image orientation perception. Spatial Vision, 16(5):429–456, 2003. (impact factor = 1.037). [⁠paper⁠]
• Jiebo Luo, Amit Singhal, David Crandall, and Robert T. Gray. A Psychophysical Study of Image Orientation Determination. In SPIE Conference on Human Vision and Image Processing, 2003.

2002

• David Crandall, Sameer Antani, and Rangachar Kasturi. Extraction of special effects caption text events from digital video. International Journal of Document Analysis and Recognition (IJDAR), 5(2-3):138–157, 2002. (impact factor = 0.800). [⁠paper⁠]

2001

• Rangachar Kasturi, Sameer Antani, and David Crandall. A Framework for Reliable Text-Based Indexing of Video. In Symposium on Document Image Understanding Technology, 2001. [⁠paper⁠]
• David Crandall and Rangachar Kasturi. Robust Detection of Stylized Text Events in Digital Video. In IAPR International Conference on Document Analysis and Recognition (ICDAR), 2001. [⁠paper⁠]
• David Crandall. Extraction of Unconstrained Caption Text from General-Purpose Video. Master's thesis, The Pennsylvania State University, 2001. (M.S. Thesis).

2000

• Sameer Antani, David Crandall, Anand Narasimamurthy, Vladimir Y. Mariano, and Rangachar Kasturi. Evaluation of Methods for Detection and Localization of Text from Video. In IAPR Workshop on Document Analysis Systems, 2000. [⁠paper⁠]
• Sameer Antani, David Crandall, and Rangachar Kasturi. Robust Extraction of Text in Video. In IAPR International Conference on Pattern Recognition (ICPR), 2000. [⁠paper⁠]

1999

• Ullas Gargi, David Crandall, Sameer Antani, Tarak Gandhi, Ryan Keener, and Rangachar Kasturi. A System for Automatic Text Detection in Video. In IAPR International Conference on Document Analysis and Recognition (ICDAR), 1999. [⁠paper⁠]