24 citations · 57 across the 7 of their papers we have counts for
7 papers
Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators
Sikai Bai, Shuaicheng Li, Weiming Zhuang +7
Federated learning has become a popular method to learn from decentralized heterogeneous data. Federated semi-supervised learning (FSSL) emerges to train models from a small fracti…
How does Contrastive Learning Organize Images?
Yunzhe Zhang, Yao Lu, Qi Xuan
Contrastive learning, a dominant self-supervised technique, emphasizes similarity in representations between augmentations of the same input and dissimilarity for different ones. A…
Probing Visual-Audio Representation for Video Highlight Detection via Hard-Pairs Guided Contrastive Learning
Shuaicheng Li, Feng Zhang, Kunlin Yang +4
Video highlight detection is a crucial yet challenging problem that aims to identify the interesting moments in untrimmed videos. The key to this task lies in effective video repre…
Pyramid Region-based Slot Attention Network for Temporal Action Proposal Generation
Shuaicheng Li, Feng Zhang, Rui-Wei Zhao +4
It has been found that temporal action proposal generation, which aims to discover the temporal action instances within the range of the start and end frames in the untrimmed video…
Better Teacher Better Student: Dynamic Prior Knowledge for Knowledge Distillation
Zengyu Qiu, Xinzhu Ma, Kunlin Yang +4
Knowledge distillation (KD) has shown very promising capabilities in transferring learning representations from large models (teachers) to small models (students). However, as the…
GroupFormer: Group Activity Recognition with Clustered Spatial-Temporal Transformer
Shuaicheng Li, Qianggang Cao, Lingbo Liu +4
Group activity recognition is a crucial yet challenging problem, whose core lies in fully exploring spatial-temporal interactions among individuals and generating reasonable group…
Video Crowd Localization with Multi-focus Gaussian Neighborhood Attention and a Large-Scale Benchmark
Haopeng Li, Lingbo Liu, Kunlin Yang +5
Video crowd localization is a crucial yet challenging task, which aims to estimate exact locations of human heads in the given crowded videos. To model spatial-temporal dependencie…