From Web to Pixels: Bringing Agentic Search into Visual Perception
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866917487233204224 |
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| author | Yang, Bokang Sun, Xinyi Feng, Kaituo Dong, Xingping Wu, Dongming Yue, Xiangyu |
| author_facet | Yang, Bokang Sun, Xinyi Feng, Kaituo Dong, Xingping Wu, Dongming Yue, Xiangyu |
| contents | Visual perception connects high-level semantic understanding to pixel-level perception, but most existing settings assume that the decisive evidence for identifying a target is already in the image or frozen model knowledge. We study a more practical yet harder open-world case where a visible object must first be resolved from external facts, recent events, long-tail entities, or multi-hop relations before it can be localized. We formalize this challenge as Perception Deep Research and introduce WebEye, an object-anchored benchmark with verifiable evidence, knowledge-intensive queries, precise box/mask annotations, and three task views: Search-based Grounding, Search-based Segmentation, and Search-based VQA. WebEyes contains 120 images, 473 annotated object instances, 645 unique QA pairs, and 1,927 task samples. We further propose Pixel-Searcher, an agentic search-to-pixel workflow that resolves hidden target identities and binds them to boxes, masks, or grounded answers. Experiments show that Pixel-Searcher achieves the strongest open-source performance across all three task views, while failures mainly arise from evidence acquisition, identity resolution, and visual instance binding. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_12497 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | From Web to Pixels: Bringing Agentic Search into Visual Perception Yang, Bokang Sun, Xinyi Feng, Kaituo Dong, Xingping Wu, Dongming Yue, Xiangyu Computer Vision and Pattern Recognition Visual perception connects high-level semantic understanding to pixel-level perception, but most existing settings assume that the decisive evidence for identifying a target is already in the image or frozen model knowledge. We study a more practical yet harder open-world case where a visible object must first be resolved from external facts, recent events, long-tail entities, or multi-hop relations before it can be localized. We formalize this challenge as Perception Deep Research and introduce WebEye, an object-anchored benchmark with verifiable evidence, knowledge-intensive queries, precise box/mask annotations, and three task views: Search-based Grounding, Search-based Segmentation, and Search-based VQA. WebEyes contains 120 images, 473 annotated object instances, 645 unique QA pairs, and 1,927 task samples. We further propose Pixel-Searcher, an agentic search-to-pixel workflow that resolves hidden target identities and binds them to boxes, masks, or grounded answers. Experiments show that Pixel-Searcher achieves the strongest open-source performance across all three task views, while failures mainly arise from evidence acquisition, identity resolution, and visual instance binding. |
| title | From Web to Pixels: Bringing Agentic Search into Visual Perception |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.12497 |