activity
20142023
most citedMaxMatch: Semi-Supervised Learning with Worst-Case Consistency

34 citations · 41 across the 8 of their papers we have counts for

collaborators

8 papers

cs.LG20232 cited

A Study of Neural Collapse Phenomenon: Grassmannian Frame, Symmetry and Generalization

Peifeng Gao, Qianqian Xu, Peisong Wen +3

In this paper, we extend original Neural Collapse Phenomenon by proving Generalized Neural Collapse hypothesis. We obtain Grassmannian Frame structure from the optimization and gen…

cs.CV2023

Neighborhood Contrastive Transformer for Change Captioning

Yunbin Tu, Liang Li, Li Su +2

Change captioning is to describe the semantic change between a pair of similar images in natural language. It is more challenging than general image captioning, because it requires…

cs.CV2023

Stable Attribute Group Editing for Reliable Few-shot Image Generation

Guanqi Ding, Xinzhe Han, Shuhui Wang +3

Few-shot image generation aims to generate data of an unseen category based on only a few samples. Apart from basic content generation, a bunch of downstream applications hopefully…

cs.LG202234 cited

MaxMatch: Semi-Supervised Learning with Worst-Case Consistency

Yangbangyan Jiang, Xiaodan Li, Yuefeng Chen +5

In recent years, great progress has been made to incorporate unlabeled data to overcome the inefficiently supervised problem via semi-supervised learning (SSL). Most state-of-the-a…

cs.CV20222 cited

Multi-Attention Network for Compressed Video Referring Object Segmentation

Weidong Chen, Dexiang Hong, Yuankai Qi +5

Referring video object segmentation aims to segment the object referred by a given language expression. Existing works typically require compressed video bitstream to be decoded to…

cs.CV2022

Entity-enhanced Adaptive Reconstruction Network for Weakly Supervised Referring Expression Grounding

Xuejing Liu, Liang Li, Shuhui Wang +4

Weakly supervised Referring Expression Grounding (REG) aims to ground a particular target in an image described by a language expression while lacking the correspondence between ta…