20 citations · 40 across the 7 of their papers we have counts for
8 papers
Enhancing Federated Class-Incremental Learning via Spatial-Temporal Statistics Aggregation
Zenghao Guan, Guojun Zhu, Yucan Zhou +4
Federated Class-Incremental Learning (FCIL) enables Class-Incremental Learning (CIL) from distributed data. Existing FCIL methods typically integrate old knowledge preservation int…
Capture Global Feature Statistics for One-Shot Federated Learning
Zenghao Guan, Yucan Zhou, Xiaoyan Gu
Traditional Federated Learning (FL) necessitates numerous rounds of communication between the server and clients, posing significant challenges including high communication costs,…
Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification
Xiaohua Chen, Yucan Zhou, Dayan Wu +4
Real-world data often follows a long-tailed distribution, which makes the performance of existing classification algorithms degrade heavily. A key issue is that samples in tail cat…
MMF: Multi-Task Multi-Structure Fusion for Hierarchical Image Classification
Xiaoni Li, Yucan Zhou, Yu Zhou +1
Hierarchical classification is significant for complex tasks by providing multi-granular predictions and encouraging better mistakes. As the label structure decides its performance…
Rescuing Deep Hashing from Dead Bits Problem
Shu Zhao, Dayan Wu, Yucan Zhou +2
Deep hashing methods have shown great retrieval accuracy and efficiency in large-scale image retrieval. How to optimize discrete hash bits is always the focus in deep hashing metho…
Exploring Relations in Untrimmed Videos for Self-Supervised Learning
Dezhao Luo, Bo Fang, Yu Zhou +3
Existing video self-supervised learning methods mainly rely on trimmed videos for model training. However, trimmed datasets are manually annotated from untrimmed videos. In this se…
Expert Training: Task Hardness Aware Meta-Learning for Few-Shot Classification
Yucan Zhou, Yu Wang, Jianfei Cai +3
Deep neural networks are highly effective when a large number of labeled samples are available but fail with few-shot classification tasks. Recently, meta-learning methods have rec…
SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition
Zhi Qiao, Yu Zhou, Dongbao Yang +2
Scene text recognition is a hot research topic in computer vision. Recently, many recognition methods based on the encoder-decoder framework have been proposed, and they can handle…