CV-Arena: An Open Benchmark for Instructional Computer Vision Problem Solving with Human-AI Collaborative Preferences 文章

ArXiv CS.CV2026-06-02NEWSen作者: Fangzhou Lin, Peiran Li, Lingyu Xu, Wenjing Chen, Qianwen Ge, Shuo Xing, Mingyang Wu, Xiangbo Gao, Siyuan Yang, Kazunori Yamada, Ziming Zhang, Haichong Zhang, Zhen Dong, Ming-Hsuan Yang, Zhengzhong Tu

摘要

arXiv:2606.00931v1 Announce Type: new Abstract: Instruction-guided image editing is becoming a general interface for visual work, yet existing benchmarks still focus largely on narrow appearance edits and do not fully capture the diversity of real-image tasks in professional workflows. Here, we define instructional computer vision problem solving as a broader formulation of image editing: given a real input image and a natural-language instruction, a system must produce an edited output that realizes the requested transformation while satisfying explicit preservation, geometric, physical, and usability constraints. We introduce CV-Arena, an open benchmark designed to evaluate this capability at professional scales.

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