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Aaron Chan
Researcher at University of Southern California
Publications - 19
Citations - 571
Aaron Chan is an academic researcher from University of Southern California. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 4, co-authored 8 publications receiving 370 citations. Previous affiliations of Aaron Chan include University of Pennsylvania & National Institutes of Health.
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Proceedings ArticleDOI
6-DoF object pose from semantic keypoints
TL;DR: In this paper, the authors combine semantic keypoints predicted by a convolutional network (convnet) with a deformable shape model to estimate the continuous 6-DoF pose of an object from a single RGB image.
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6-DoF Object Pose from Semantic Keypoints
TL;DR: A novel approach to estimating the continuous six degree of freedom (6-DoF) pose (3D translation and rotation) of an object from a single RGB image by combining semantic keypoints predicted by a convolutional network with a deformable shape model.
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Learning Contextualized Knowledge Structures for Commonsense Reasoning
Jun Yan,Mrigank Raman,Aaron Chan,Tianyu Zhang,Ryan A. Rossi,Handong Zhao,Sungchul Kim,Nedim Lipka,Xiang Ren +8 more
TL;DR: A novel neural-symbolic model is presented, named Hybrid Graph Network (HGN), which jointly generates feature representations for new triples, determines the relevance of the triples to the reasoning context, and learns graph module parameters for encoding the relational information.
Proceedings ArticleDOI
Egocentric Basketball Motion Planning from a Single First-Person Image
TL;DR: A model that uses a single first-person image to generate an egocentric basketball motion sequence in the form of a 12D camera configuration trajectory, which encodes a player's 3D location and 3D head orientation throughout the sequence.
Proceedings ArticleDOI
PINTO: Faithful Language Reasoning Using Prompt-Generated Rationales
TL;DR: This paper proposed PINTO, an LMs pipeline that rationalizes via prompt-based learning, and learns to faithfully reason over rationales via counterfactual regularization, which significantly improves the generalization ability of the reasoning LMs.