activity
20142023
most citedALID: Scalable Dominant Cluster Detection

2 citations · 4 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2023

ImageNet-E: Benchmarking Neural Network Robustness via Attribute Editing

Xiaodan Li, Yuefeng Chen, Yao Zhu +3

Recent studies have shown that higher accuracy on ImageNet usually leads to better robustness against different corruptions. Therefore, in this paper, instead of following the trad…

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.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…

cs.DB20142 cited

ALID: Scalable Dominant Cluster Detection

Lingyang Chu, Shuhui Wang, Siyuan Liu +2

Detecting dominant clusters is important in many analytic applications. The state-of-the-art methods find dense subgraphs on the affinity graph as the dominant clusters. However, t…