{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/perceive-anything-recognize-explain-caption","title":"Perceive Anything: Recognize, Explain, Caption, and Segment Anything in Images and Videos","arxiv_id":"2506.05302","date":"2025-06-05","proceeding":null,"authors":["Weifeng Lin","Xinyu Wei","Ruichuan An","Tianhe Ren","TingWei Chen","Renrui Zhang","Ziyu Guo","Wentao Zhang","Lei Zhang","Hongsheng Li"],"abstract":"We present Perceive Anything Model (PAM), a conceptually straightforward and efficient framework for comprehensive region-level visual understanding in images and videos. Our approach extends the powerful segmentation model SAM 2 by integrating Large Language Models (LLMs), enabling simultaneous object segmentation with the generation of diverse, region-specific semantic outputs, including categories, label definition, functional explanations, and detailed captions. A key component, Semantic Perceiver, is introduced to efficiently transform SAM 2's rich visual features, which inherently carry general vision, localization, and semantic priors into multi-modal tokens for LLM comprehension. To support robust multi-granularity understanding, we also develop a dedicated data refinement and augmentation pipeline, yielding a high-quality dataset of 1.5M image and 0.6M video region-semantic annotations, including novel region-level streaming video caption data. PAM is designed for lightweightness and efficiency, while also demonstrates strong performance across a diverse range of region understanding tasks. It runs 1.2-2.4x faster and consumes less GPU memory than prior approaches, offering a practical solution for real-world applications. We believe that our effective approach will serve as a strong baseline for future research in region-level visual understanding.","url_abs":"https://arxiv.org/abs/2506.05302v1","url_pdf":"https://arxiv.org/pdf/2506.05302v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"perceive-anything-recognize-explain-caption","repo_url":"https://github.com/AFeng-x/Draw-and-Understand","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":null,"task_name":"GPU"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"sam","method_name":"SAM"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2506.05302","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.05302"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/AFeng-x/Draw-and-Understand","reach":{"status":"ok","spdx":"Apache-2.0"}}],"summary":{"ran":1,"unverified":5},"by_repo_kind":{"listed":{"samples":6,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"476ee9ec95271a06","entry":"probe_tokenizer_path_from_pretrained","repo":"AFeng-x/Draw-and-Understand","repo_kind":"listed","path":"accessory/model/tokenizer.py","file_url":"https://github.com/AFeng-x/Draw-and-Understand/blob/HEAD/accessory/model/tokenizer.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"476ee9ec95271a06"}},{"code_sha256_prefix":"f5b33c6a8295f129","entry":"collate_fn","repo":"AFeng-x/Draw-and-Understand","repo_kind":"listed","path":"accessory/eval/infer_and_save.py","file_url":"https://github.com/AFeng-x/Draw-and-Understand/blob/HEAD/accessory/eval/infer_and_save.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"f5b33c6a8295f129"}},{"code_sha256_prefix":"7412d9bb6f8eb47f","entry":"draw_masks","repo":"AFeng-x/Draw-and-Understand","repo_kind":"listed","path":"accessory/demos/visual_prompt_gradio.py","file_url":"https://github.com/AFeng-x/Draw-and-Understand/blob/HEAD/accessory/demos/visual_prompt_gradio.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"7412d9bb6f8eb47f"}},{"code_sha256_prefix":"d0026e3327b2cdbe","entry":"extract_masks_from_draw","repo":"AFeng-x/Draw-and-Understand","repo_kind":"listed","path":"accessory/demos/visual_prompt_gradio.py","file_url":"https://github.com/AFeng-x/Draw-and-Understand/blob/HEAD/accessory/demos/visual_prompt_gradio.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"d0026e3327b2cdbe"}},{"code_sha256_prefix":"4ae716b03c71bf84","entry":"get_qa_list","repo":"AFeng-x/Draw-and-Understand","repo_kind":"listed","path":"accessory/eval/infer_and_save.py","file_url":"https://github.com/AFeng-x/Draw-and-Understand/blob/HEAD/accessory/eval/infer_and_save.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"4ae716b03c71bf84"}},{"code_sha256_prefix":"8d4839d960333aa8","entry":"vp_normalize","repo":"AFeng-x/Draw-and-Understand","repo_kind":"listed","path":"accessory/eval/infer_and_save.py","file_url":"https://github.com/AFeng-x/Draw-and-Understand/blob/HEAD/accessory/eval/infer_and_save.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"8d4839d960333aa8"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}