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cs.CV2026

Beyond Global Routing Aggregation: Phase-Aware Expert Merging for MoE Vision-Language Models

Hongyu Zhang, Cheng Yan, Xiang Xia +1

Mixture-of-experts vision-language models (MoE-VLMs) increase model capacity with sparse expert activation, yet deployment requires storing the full expert pool. Training-free expe…

cs.CV2026

DAVET: Denoising-Aware Visual Evidence Trajectory Allocation for Diffusion Vision-Language Models

Yongkang Zhou, Xiang Xia, Cheng Yan +2

Diffusion vision-language models (dVLMs) iteratively denoise masked responses while conditioning each denoising step on visual evidence, making visual conditioning a substantial re…

cs.CV2026

Let Your Image Move with Your Motion! -- Implicit Multi-Object Multi-Motion Transfer

Yuze Li, Dong Gong, Xiao Cao +6

Motion transfer has emerged as a promising direction for controllable video generation, yet existing methods largely focus on single-object scenarios and struggle when multiple obj…

cs.CV2025

ROS-SAM: High-Quality Interactive Segmentation for Remote Sensing Moving Object

Zhe Shan, Yang Liu, Lei Zhou +3

The availability of large-scale remote sensing video data underscores the importance of high-quality interactive segmentation. However, challenges such as small object sizes, ambig…

cs.CV2025

Learning Visual Proxy for Compositional Zero-Shot Learning

Shiyu Zhang, Cheng Yan, Yang Liu +3

Compositional Zero-Shot Learning (CZSL) aims to recognize novel attribute-object compositions by leveraging knowledge from seen compositions. Current methods align textual prototyp…