From Web to Pixels: Bringing Agentic Search into Visual Perception
This work addresses the practical problem of grounding objects that require external knowledge for identification, which is a bottleneck for current visual perception systems.
The paper introduces Perception Deep Research, a new task where visible objects must be resolved using external knowledge before localization, and presents WebEye benchmark and Pixel-Searcher agent. Pixel-Searcher achieves state-of-the-art open-source performance on Search-based Grounding, Segmentation, and VQA tasks.
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.