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20232026
most citedFederated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark

8 citations · 15 across the 30 of their papers we have counts for

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10 papers · 1 filter

cs.CV2026

Decouple, Purify and Unite: Semantic-Structural Prototype Learning for Federated Medical Segmentation

Xingyue Zhao, Wenke Huang, Linghao Zhuang +11

Federated learning enables medical institutions to train a global model without sharing data, yet feature heterogeneity from diverse scanners or protocols remains challenging. Exis…

cs.CV2026

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning

Qianlong Yang, Bowen Ye, Xianda Guo +4

Despite the progress of multimodal large language models (MLLMs), they continue to exhibit deficiencies in visual perception. Following visual instruction tuning, internal MLLM rep…

cs.CV2026

Switch-Reasoner: Learn When to Think in Multitask Mixtures via Reinforcement Learning

Yiyang Fang, Pei Fu, Jinjie Li +7

Multimodal Large Language Models (MLLMs) often follow a fixed Think-then-Answer paradigm, which is inefficient in heterogeneous multitask settings because simple inputs may not req…

cs.CV2026

Cross-Resolution Semantic Transfer for Robust Text-to-Image Retrieval in Low-Resolution Surveillance

Wenjie Qian, Bin Yang, Xiao Wang +4

Text-to-image person re-identification (TIPR) retrieves target persons using natural language descriptions. However, existing methods largely overlook resolution variance in real-w…

cs.CV2026

StereoFactory: A Unified Merging Framework for Robust Stereo Matching

Xianda Guo, Pinhan Fu, Ruilin Wang +3

Stereo matching has advanced through foundation models trained on large-scale datasets, yet this paradigm suffers from a scalability bottleneck: incorporating new data requires cos…

cs.CV2026

Shift-Dependent Asymmetry: Orthogonal Inverse Low-Rank Adaptation for Federated Medical Segmentation

Xingyue Zhao, Wenke Huang, Linghao Zhuang +7

Low-Rank Adaptation (LoRA) enables efficient federated fine-tuning of segmentation foundation models for medical imaging. However, most federated LoRA methods adopt a uniform aggre…

cs.CV2026

Mining Multi-Modality Spatio-Temporal Cues for Video Important Person Identification

Xiao Wang, Minglei Yang, Bin Yang +4

Identifying key individuals in video scenes is essential for applications such as automated video editing and intelligent surveillance. Current methods primarily focus on static im…

cs.CV2025

Calibrating Biased Distribution in VFM-derived Latent Space via Cross-Domain Geometric Consistency

Yanbiao Ma, Wei Dai, Bowei Liu +5

Despite the fast progress of deep learning, one standing challenge is the gap of the observed training samples and the underlying true distribution. There are multiple reasons for…

cs.CV2025

ROVR-Open-Dataset: A Large-Scale Depth Dataset for Autonomous Driving

Xianda Guo, Ruijun Zhang, Yiqun Duan +9

Depth estimation is a fundamental component of spatial perception for autonomous driving and other unmanned systems operating in open urban environments. Existing depth datasets su…

cs.CV2025

Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated Learning

Yanbiao Ma, Wei Dai, Wenke Huang +1

Data heterogeneity in federated learning, characterized by a significant misalignment between local and global distributions, leads to divergent local optimization directions and h…