activity
20182022
most citedLearning Perceptual Inference by Contrasting

40 citations · 87 across the 6 of their papers we have counts for

collaborators

9 papers

cs.CV20222 cited

Perceive, Ground, Reason, and Act: A Benchmark for General-purpose Visual Representation

Jiangyong Huang, William Yicheng Zhu, Baoxiong Jia +4

Current computer vision models, unlike the human visual system, cannot yet achieve general-purpose visual understanding. Existing efforts to create a general vision model are limit…

cs.CV202213 cited

EgoTaskQA: Understanding Human Tasks in Egocentric Videos

Baoxiong Jia, Ting Lei, Song-Chun Zhu +1

Understanding human tasks through video observations is an essential capability of intelligent agents. The challenges of such capability lie in the difficulty of generating a detai…

cs.CV2021

ACRE: Abstract Causal REasoning Beyond Covariation

Chi Zhang, Baoxiong Jia, Mark Edmonds +2

Causal induction, i.e., identifying unobservable mechanisms that lead to the observable relations among variables, has played a pivotal role in modern scientific discovery, especia…

cs.AI2021

Abstract Spatial-Temporal Reasoning via Probabilistic Abduction and Execution

Chi Zhang, Baoxiong Jia, Song-Chun Zhu +1

Spatial-temporal reasoning is a challenging task in Artificial Intelligence (AI) due to its demanding but unique nature: a theoretic requirement on representing and reasoning based…

cs.CV20201 cited

LEMMA: A Multi-view Dataset for Learning Multi-agent Multi-task Activities

Baoxiong Jia, Yixin Chen, Siyuan Huang +2

Understanding and interpreting human actions is a long-standing challenge and a critical indicator of perception in artificial intelligence. However, a few imperative components of…

cs.CV201940 cited

Learning Perceptual Inference by Contrasting

Chi Zhang, Baoxiong Jia, Feng Gao +3

"Thinking in pictures," [1] i.e., spatial-temporal reasoning, effortless and instantaneous for humans, is believed to be a significant ability to perform logical induction and a cr…