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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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Journal ArticleDOI
A boosting approach for the simultaneous detection and segmentation of generic objects
TL;DR: This paper proposes a general framework to simultaneously perform object detection and segmentation on objects of different nature based on a boosting procedure which automatically decides - according to the object properties - whether it is better to give more weight to the detection or segmentation process to improve both results.
Posted Content
DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effort
Yuxuan Zhang,Huan Ling,Jun Gao,Kangxue Yin,Jean-Francois Lafleche,Adela Barriuso,Antonio Torralba,Sanja Fidler +7 more
TL;DR: DatasetGAN as mentioned in this paper uses GANs to generate high-quality semantically segmented images, which can then be used for training any computer vision architecture just as real datasets are.
Journal ArticleDOI
Role of Low-level Mechanisms in Brightness Perception
Pawan Sinha,Antonio Torralba +1 more
TL;DR: In this article, the role of low-level mechanisms, such as lateral inhibition, as explanations for brightness phenomena has been examined and it was shown that brightness percepts in these displays are governed by lowlevel stimulus properties, even when these percepts are inconsistent with higher level interpretations of scene layout.
Posted Content
3D Neural Scene Representations for Visuomotor Control
TL;DR: In this article, the authors combine Neural Radiance Fields (NeRF) and time contrastive learning with an autoencoding framework to learn viewpoint-invariant 3D-aware scene representations.
Proceedings ArticleDOI
NOPA: Neurally-guided Online Probabilistic Assistance for Building Socially Intelligent Home Assistants
TL;DR: In this article , the authors propose an online goal inference module combining neural goal proposals with inverse planning and particle filtering for robust inference under uncertainty, and a helping planner that discovers valuable subgoals to help with and is aware of the uncertainty in goal inference.