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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.
Papers
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Proceedings ArticleDOI
Statistical context priming for object detection
Antonio Torralba,Pawan Sinha +1 more
TL;DR: A simple probabilistic framework for modeling the relationship between context and object properties is introduced, representing global context information in terms of the spatial layout of spectral components and serving as an effective procedure for context driven focus of attention and scale-selection on real-world scenes.
Posted Content
Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding
TL;DR: In this article, a neural-symbolic visual question answering (NS-VQA) system first recovers a structural scene representation from the image and a program trace from the question and then executes the program on the scene representation to obtain an answer.
Proceedings Article
Describing Visual Scenes using Transformed Dirichlet Processes
TL;DR: This work develops a hierarchical probabilistic model for the spatial structure of visual scenes based on the transformed Dirichlet process, a novel extension of the hierarchical DP in which a set of stochastically transformed mixture components are shared between multiple groups of data.
ReportDOI
Understanding the Intrinsic Memorability of Images
TL;DR: In this article, the authors used the publicly available memorability dataset of Isola et al. and augmented object and scene annotations with interpretable spatial, content, and aesthetic image properties to determine a compact set of attributes that characterizes the memorability of any individual image.
Journal ArticleDOI
A Tree-Based Context Model for Object Recognition
TL;DR: It is demonstrated that the context model improves object recognition performance and provides a coherent interpretation of a scene, which enables a reliable image querying system by multiple object categories and can be applied to scene understanding tasks that local detectors alone cannot solve.