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Antonio Torralba
Researcher at Massachusetts Institute of Technology
Publications - 437
Citations - 105763
Antonio Torralba is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Computer science & Object detection. The author has an hindex of 119, co-authored 388 publications receiving 84607 citations. Previous affiliations of Antonio Torralba include Vassar College & Nvidia.
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Proceedings Article
A Compositional Object-Based Approach to Learning Physical Dynamics
TL;DR: The NPE's compositional representation of the structure in physical interactions improves its ability to predict movement, generalize across variable object count and different scene configurations, and infer latent properties of objects such as mass.
Proceedings ArticleDOI
LabelMe video: Building a video database with human annotations
TL;DR: An online and openly accessible video annotation system that allows anyone with a browser and internet access to efficiently annotate object category, shape, motion, and activity information in real-world videos is designed.
Posted Content
Debiased Contrastive Learning
TL;DR: A debiased contrastive objective is developed that corrects for the sampling of same-label datapoints, even without knowledge of the true labels, and consistently outperforms the state-of-the-art for representation learning in vision, language, and reinforcement learning benchmarks.
Journal ArticleDOI
Interpreting Deep Visual Representations via Network Dissection
TL;DR: In this paper, the authors quantified the interpretability of CNN representations by evaluating the alignment between individual hidden units and visual semantic concepts and found that deep representations are more transparent and interpretable than they would be under a random equivalently powerful basis.
Posted Content
CLEVRER: CoLlision Events for Video REpresentation and Reasoning
TL;DR: This work introduces the CoLlision Events for Video REpresentation and Reasoning (CLEVRER), a diagnostic video dataset for systematic evaluation of computational models on a wide range of reasoning tasks, and evaluates various state-of-the-art models for visual reasoning on a benchmark.