| Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models A Barbu, D Mayo, J Alverio, W Luo, C Wang, D Gutfreund, J Tenenbaum, ... Advances in neural information processing systems 32, 2019 | 661 | 2019 |
| Recognize human activities from partially observed videos Y Cao, D Barrett, A Barbu, S Narayanaswamy, H Yu, A Michaux, Y Lin, ... Proceedings of the IEEE conference on computer vision and pattern …, 2013 | 243 | 2013 |
| Video in sentences out A Barbu, A Bridge, Z Burchill, D Coroian, S Dickinson, S Fidler, A Michaux, ... UAI 2012, 2012 | 205 | 2012 |
| Measuring social biases in grounded vision and language embeddings C Ross, B Katz, A Barbu arXiv preprint arXiv:2002.08911, 2020 | 74 | 2020 |
| Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas YL Kuo, B Katz, A Barbu 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2020 | 55 | 2020 |
| A compositional framework for grounding language inference, generation, and acquisition in video H Yu, N Siddharth, A Barbu, JM Siskind Journal of Artificial Intelligence Research 52, 601-713, 2015 | 49 | 2015 |
| Temporal grounding graphs for language understanding with accrued visual-linguistic context R Paul, A Barbu, S Felshin, B Katz, N Roy arXiv preprint arXiv:1811.06966, 2018 | 48 | 2018 |
| Seeing what you're told: Sentence-guided activity recognition in video N Siddharth, A Barbu, J Mark Siskind Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2014 | 44 | 2014 |
| Correlating videos and sentences JM Siskind, A Barbu, S Narayanaswamy, H Yu US Patent 9,183,466, 2015 | 43 | 2015 |
| Do You See What I Mean? Visual Resolution of Linguistic Ambiguities Y Berzak, A Barbu, D Harari, B Katz, S Ullman Conference on Empirical Methods in Natural Language Processing (EMNLP), 1477 …, 2015 | 42 | 2015 |
| Learning a natural-language to LTL executable semantic parser for grounded robotics C Wang, C Ross, YL Kuo, B Katz, A Barbu Conference on Robot Learning, 1706-1718, 2021 | 41 | 2021 |
| Seeing is Worse than Believing: Reading People’s Minds Better than Computer-Vision Methods Recognize Actions A Barbu, DP Barrett, W Chen, N Siddharth, C Xiong, JJ Corso, ... Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland …, 2014 | 41 | 2014 |
| BrainBERT: Self-supervised representation learning for intracranial recordings C Wang, V Subramaniam, AU Yaari, G Kreiman, B Katz, I Cases, A Barbu International Conference on Learning Representations (ICLR), 2023 | 36 | 2023 |
| Phase: Physically-grounded abstract social events for machine social perception A Netanyahu, T Shu, B Katz, A Barbu, JB Tenenbaum Proceedings of the aaai conference on artificial intelligence 35 (1), 845-853, 2021 | 36 | 2021 |
| Anchoring and agreement in syntactic annotations Y Berzak, Y Huang, A Barbu, A Korhonen, B Katz arXiv preprint arXiv:1605.04481, 2016 | 35 | 2016 |
| Neural regression, representational similarity, model zoology & neural taskonomy at scale in rodent visual cortex C Conwell, D Mayo, A Barbu, M Buice, G Alvarez, B Katz Advances in Neural Information Processing Systems 34, 5590-5607, 2021 | 30 | 2021 |
| Deep compositional robotic planners that follow natural language commands YL Kuo, B Katz, A Barbu 2020 IEEE international conference on robotics and automation (ICRA), 4906-4912, 2020 | 29 | 2020 |
| Simultaneous object detection, tracking, and event recognition A Barbu, A Michaux, S Narayanaswamy, JM Siskind ACS 2012, 2012 | 29 | 2012 |
| Learning physically-instantiated game play through visual observation A Barbu, S Narayanaswamy, JM Siskind 2010 IEEE International Conference on Robotics and Automation, 1879-1886, 2010 | 29 | 2010 |
| Deep sequential models for sampling-based planning YL Kuo, A Barbu, B Katz IROS 2018, 2018 | 27 | 2018 |
Jeffrey Mark SiskindPurdue UniversityBestätigte E-Mail-Adresse bei purdue.edu
