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20172026
most citedRethinking Client Drift in Federated Learning: A Logit Perspective

6 citations · 54 across the 65 of their papers we have counts for

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Showing 2026 · cs.CVShow all

5 papers · 2 filters

cs.CV2026

DRT: Dense Reasoning Trace for Efficient and Grounded Multimodal Reasoning

Wan Xu, Yuanfan Guo, Kevin Han +2

Despite the remarkable progress in Multimodal Large Language Models (MLLMs), prevailing Chain-of-Thought (CoT) paradigms remain confined to the natural-language expression space. C…

cs.CV2026

Improving Complex Moiré Removal with Generative Supervision

Xinyang Gu, Zhilu Zhang, Honglei Xu +3

The availability of high-quality paired data is essential for training learning-based image demoiréing models. However, it remains challenging for existing datasets to encompass th…

cs.CV2026

GS-RealBlur: A Flexible Data Acquisition Framework for Real-World Image Deblurring

Mingyang Chen, Zhilu Zhang, Honglei Xu +3

High-quality, large-scale paired data is essential for training learning-based image deblurring models. However, synthetic blurry images generally lack realism, while real-world ca…

cs.CV2026

FedSmoothLoRA: Toward Smoother and Faster Convergence in Federated Low-Rank Adaptation

Zehao Wang, Guanglei Yang, Yihan Zeng +4

Federated fine-tuning of foundation models with Low-Rank Adaptation (LoRA) provides an efficient solution for reducing communication and computation costs while preserving data loc…

cs.CV2026

Mind the Generative Details: Direct Localized Detail Preference Optimization for Video Diffusion Models

Zitong Huang, Kaidong Zhang, Yukang Ding +4

Aligning text-to-video diffusion models with human preferences is crucial for generating high-quality videos. Existing Direct Preference Otimization (DPO) methods rely on multi-sam…