activity
20232026
most citedRobust Asymmetric Heterogeneous Federated Learning with Corrupted Clients

12 citations · 29 across the 10 of their papers we have counts for

collaborators

10 papers

cs.CV2026

Cross-Species Animal Re-Identification with Semantic Consistency Learning

Shuoyi Chen, Yuejia Li, Mang Ye

Generalizable animal Re-Identification (ReID) aims to recognize individual animals across species with diverse morphologies and ecological contexts. Unlike person ReID, where diffe…

cs.CV2026

QASA: Quality-Guided K-Adaptive Slot Attention for Unsupervised Object-Centric Learning

Tianran Ouyang, Xingping Dong, Jing Zhang +4

Slot Attention, an approach that binds different objects in a scene to a set of "slots", has become a leading method in unsupervised object-centric learning. Most methods assume a…

cs.CV2025

SA-Person: Text-Based Person Retrieval with Scene-aware Re-ranking

Yingjia Xu, Jinlin Wu, Daming Gao +5

Text-based person retrieval aims to identify a target individual from an image gallery using a natural language description. Existing methods primarily focus on appearance-driven c…

cs.LG2025★ 12 cited

Robust Asymmetric Heterogeneous Federated Learning with Corrupted Clients

Xiuwen Fang, Mang Ye, Bo Du

This paper studies a challenging robust federated learning task with model heterogeneous and data corrupted clients, where the clients have different local model structures. Data c…

cs.CV2024

Adaptive High-Frequency Transformer for Diverse Wildlife Re-Identification

Chenyue Li, Shuoyi Chen, Mang Ye

Wildlife ReID involves utilizing visual technology to identify specific individuals of wild animals in different scenarios, holding significant importance for wildlife conservation…

cs.CV2024★ 5 cited

EmoLLM: Multimodal Emotional Understanding Meets Large Language Models

Qu Yang, Mang Ye, Bo Du

Multi-modal large language models (MLLMs) have achieved remarkable performance on objective multimodal perception tasks, but their ability to interpret subjective, emotionally nuan…

cs.LG2024★ 1 cited

Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey

Mang Ye, Wei Shen, Bo Du +3

Vertical Federated Learning (VFL) is a privacy-preserving distributed learning paradigm where different parties collaboratively learn models using partitioned features of shared sa…

cs.CV2024

All in One Framework for Multimodal Re-identification in the Wild

He Li, Mang Ye, Ming Zhang +1

In Re-identification (ReID), recent advancements yield noteworthy progress in both unimodal and cross-modal retrieval tasks. However, the challenge persists in developing a unified…

cs.CV2024★ 3 cited

Transformer for Object Re-Identification: A Survey

Mang Ye, Shuoyi Chen, Chenyue Li +3

Object Re-identification (Re-ID) aims to identify specific objects across different times and scenes, which is a widely researched task in computer vision. For a prolonged period,…

cs.LG2023★ 8 cited

Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark

Wenke Huang, Mang Ye, Zekun Shi +4

Federated learning has emerged as a promising paradigm for privacy-preserving collaboration among different parties. Recently, with the popularity of federated learning, an influx…