Towards High-Resolution Visual Perception via Hierarchical Entity Exploration

High-resolution (HR) image perception remains a key chal-lenge in multimodal large language models (MLLMs), as fine-graineddetails are often lost when the image is processed as a whole. Existingmethods either require training to teach models where to look or heuris-tically divide the image into fixed regions, both of which struggle togeneralize in complex HR scenes. In this work, we propose HierarchicalEntity Exploration (HEE), a training-free and model-agnostic frameworkthat transforms static image understanding into dynamic, query-guidedentity exploration. HEE first evaluates each region using a dual scoringmechanism to determine whether it already contains sufficient evidenceto answer the question. If not, it applies object detection within themost promising region to extract fine-grained entities, clusters them intocoherent subregions, and organizes them into a multi-level semantic hi-erarchy for deeper exploration. When deeper regions still fail to yieldconfident answers, a confidence-guided backtracking mechanism revisitsalternative paths to ensure adaptive perception. Extensive results showthat HEE outperforms training-free methods like ZoomEye and RAPin both accuracy and efficiency on two complex HR benchmarks (VisualProbe and HR-Bench), across different MLLMs such as Qwen2.5-VL andLLaVA-OneVision. Moreover, HEE demonstrates generalization on theMME-RealWorld benchmark.