{"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/efficient-diffusion-on-region-manifolds","title":"Efficient Diffusion on Region Manifolds: Recovering Small Objects with Compact CNN Representations","arxiv_id":"1611.05113","date":"2016-11-16","proceeding":"CVPR 2017 7","authors":["Ahmet Iscen","Giorgos Tolias","Yannis Avrithis","Teddy Furon","Ondrej Chum"],"abstract":"Query expansion is a popular method to improve the quality of image retrieval with both conventional and CNN representations. It has been so far limited to global image similarity. This work focuses on diffusion, a mechanism that captures the image manifold in the feature space. The diffusion is carried out on descriptors of overlapping image regions rather than on a global image descriptor like in previous approaches. An efficient off-line stage allows optional reduction in the number of stored regions. In the on-line stage, the proposed handling of unseen queries in the indexing stage removes additional computation to adjust the precomputed data. We perform diffusion through a sparse linear system solver, yielding practical query times well below one second. Experimentally, we observe a significant boost in performance of image retrieval with compact CNN descriptors on standard benchmarks, especially when the query object covers only a small part of the image. Small objects have been a common failure case of CNN-based retrieval.","url_abs":"https://arxiv.org/abs/1611.05113v3","url_pdf":"https://arxiv.org/pdf/1611.05113v3.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":"efficient-diffusion-on-region-manifolds","repo_url":"https://github.com/gtolias/mom","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"efficient-diffusion-on-region-manifolds","repo_url":"https://github.com/ducha-aiki/manifold-diffusion","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"efficient-diffusion-on-region-manifolds","repo_url":"https://github.com/ahmetius/diffusion-retrieval","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"image-retrieval","task_name":"Image Retrieval"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1611.05113","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1611.05113"}},"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/ducha-aiki/manifold-diffusion","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/ahmetius/diffusion-retrieval","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/gtolias/mom","reach":{"status":"ok"}}],"summary":{"ran_draft_wrong":2,"ran_honours":1},"by_repo_kind":{"listed":{"samples":3,"ran":3,"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":"0182e8a61631b045","entry":"normalize_connection_graph","repo":"ducha-aiki/manifold-diffusion","repo_kind":"listed","path":"diffussion.py","file_url":"https://github.com/ducha-aiki/manifold-diffusion/blob/HEAD/diffussion.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"0182e8a61631b045"}},{"code_sha256_prefix":"25486b07615905ad","entry":"sim_kernel","repo":"ducha-aiki/manifold-diffusion","repo_kind":"listed","path":"diffussion.py","file_url":"https://github.com/ducha-aiki/manifold-diffusion/blob/HEAD/diffussion.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"25486b07615905ad"}},{"code_sha256_prefix":"b3f29c8a2d31090b","entry":"topK_W","repo":"ducha-aiki/manifold-diffusion","repo_kind":"listed","path":"diffussion.py","file_url":"https://github.com/ducha-aiki/manifold-diffusion/blob/HEAD/diffussion.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"b3f29c8a2d31090b"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}