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20182026
most citedEfficient Model Personalization in Federated Learning via Client-Specific Prompt Generation

2 citations · 2 across the 15 of their papers we have counts for

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cs.CV2026

ReactVAU: A Slow-Fast Decoupled Framework for Streaming Video Anomaly Understanding

Chia-Hui Chen, Shih-Ying Yeh, Fu-En Yang +2

In this paper, we propose ReactVAU, a Slow-Fast Decoupled Framework for real-time streaming Video Anomaly Understanding (VAU). Existing VAU methods rely on offline inference with g…

cs.CV2026

Scal3R: Learning Efficient Multi-Relative Pose Query for Scalable Online 3D Reconstruction

Chin-Yang Lin, Yang-Che Sun, Cheng Sun +5

Online 3D reconstruction models perform poorly on long videos. This happens because regressing poses relative to a fixed first-frame anchor forces extrapolation far beyond the trai…

cs.CV2026

Physics in 2-Steps: Locking Motion Priors Before Visual Refinement Erases Them

Woojung Han, Seil Kang, Youngjun Jun +3

Image-to-Video diffusion models leverage input images to generate visually stunning content, yet frequently produce motion that violates physical laws. We reveal a surprising findi…

cs.CV2026

CANDLE: Illumination-Invariant Semantic Priors for Color Ambient Lighting Normalization

Rong-Lin Jian, Ting-Yao Chen, Yu-Fan Lin +4

Color ambient lighting normalization under multi-colored illumination is challenging due to severe chromatic shifts, highlight saturation, and material-dependent reflectance. Exist…

cs.CV2026

NTIRE 2026 Challenge on Single Image Reflection Removal in the Wild: Datasets, Results, and Methods

Jie Cai, Kangning Yang, Zhiyuan Li +50

In this paper, we review the NTIRE 2026 challenge on single-image reflection removal (SIRR) in the wild. SIRR is a fundamental task in image restoration. Despite progress in academ…

cs.CV2026

Frequency Switching Mechanism for Parameter-E!cient Multi-Task Learning

Shih-Wen Liu, Yen-Chang Chen, Wei-Ta Chu +2

Multi-task learning (MTL) aims to enable a single model to solve multiple tasks efficiently; however, current parameter-efficient fine-tuning (PEFT) methods remain largely limited…