most citedIn-context Prompt Learning for Test-time Vision Recognition with Frozen Vision-language Model

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cs.CV2026

MIEScore: Human-Aligned Evaluation for Multi-Source Image Editing

Zitong Xu, Huiyu Duan, Xinyun Zhang +7

Recent advances in unified multimodal models have significantly improved text-guided image editing abilities. In particular, models such as Nano-Banana-Pro and GPT-Image-2 demonstr…

cs.CV2026

ReasonEdit: Towards Interpretable Image Editing Evaluation via Reinforcement Learning

Honghua Chen, Zitong Xu, Huiyu Duan +3

Recent text-guided image editing (TIE) models have achieved remarkable progress, however, many edited results still suffer from artifacts, unintended modifications, and suboptimal…

cs.CV2026

Learning Visual Feature-Based World Models via Residual Latent Action

Xinyu Zhang, Zhengtong Xu, Yutian Tao +3

World models predict future transitions from observations and actions. Existing works predominantly focus on image generation only. Visual feature-based world models, on the other…

cs.CV2026

Plug-and-play Class-aware Knowledge Injection for Prompt Learning with Visual-Language Model

Junhui Yin, Nan Pu, Xinyu Zhang +4

Prompt learning has become an effective and widely used technique in enhancing vision-language models (VLMs) such as CLIP for various downstream tasks, particularly in zero-shot cl…

cs.CV2024★ 1 cited

In-context Prompt Learning for Test-time Vision Recognition with Frozen Vision-language Model

Junhui Yin, Xinyu Zhang, Lin Wu +1

Current pre-trained vision-language models, such as CLIP, have demonstrated remarkable zero-shot generalization capabilities across various downstream tasks. However, their perform…