activity
20182022
most citedLook Closer to Ground Better: Weakly-Supervised Temporal Grounding of Sentence in Video

49 citations · 111 across the 9 of their papers we have counts for

collaborators

10 papers

cs.CV202211 cited

S-NeRF: Neural Reflectance Field from Shading and Shadow under a Single Viewpoint

Wenqi Yang, Guanying Chen, Chaofeng Chen +2

In this paper, we address the "dual problem" of multi-view scene reconstruction in which we utilize single-view images captured under different point lights to learn a neural scene…

cs.CV202214 cited

ComPhy: Compositional Physical Reasoning of Objects and Events from Videos

Zhenfang Chen, Kexin Yi, Yunzhu Li +4

Objects' motions in nature are governed by complex interactions and their properties. While some properties, such as shape and material, can be identified via the object's visual a…

cs.CV2022

A Unified Framework for Masked and Mask-Free Face Recognition via Feature Rectification

Shaozhe Hao, Chaofeng Chen, Zhenfang Chen +1

Face recognition under ideal conditions is now considered a well-solved problem with advances in deep learning. Recognizing faces under occlusion, however, still remains a challeng…

cs.CV202110 cited

Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language

Mingyu Ding, Zhenfang Chen, Tao Du +3

In this work, we propose a unified framework, called Visual Reasoning with Differ-entiable Physics (VRDP), that can jointly learn visual concepts and infer physics models of object…

cs.CV2021

The Blessings of Unlabeled Background in Untrimmed Videos

Yuan Liu, Jingyuan Chen, Zhenfang Chen +3

Weakly-supervised Temporal Action Localization (WTAL) aims to detect the action segments with only video-level action labels in training. The key challenge is how to distinguish th…

cs.CV202112 cited

Grounding Physical Concepts of Objects and Events Through Dynamic Visual Reasoning

Zhenfang Chen, Jiayuan Mao, Jiajun Wu +3

We study the problem of dynamic visual reasoning on raw videos. This is a challenging problem; currently, state-of-the-art models often require dense supervision on physical object…