{"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/anomalyxfusion-multi-modal-anomaly-synthesis","title":"AnomalyXFusion: Multi-modal Anomaly Synthesis with Diffusion","arxiv_id":"2404.19444","date":"2024-04-30","proceeding":null,"authors":["Jie Hu","Yawen Huang","Yilin Lu","Guoyang Xie","Guannan Jiang","Yefeng Zheng","Zhichao Lu"],"abstract":"Anomaly synthesis is one of the effective methods to augment abnormal samples for training. However, current anomaly synthesis methods predominantly rely on texture information as input, which limits the fidelity of synthesized abnormal samples. Because texture information is insufficient to correctly depict the pattern of anomalies, especially for logical anomalies. To surmount this obstacle, we present the AnomalyXFusion framework, designed to harness multi-modality information to enhance the quality of synthesized abnormal samples. The AnomalyXFusion framework comprises two distinct yet synergistic modules: the Multi-modal In-Fusion (MIF) module and the Dynamic Dif-Fusion (DDF) module. The MIF module refines modality alignment by aggregating and integrating various modality features into a unified embedding space, termed X-embedding, which includes image, text, and mask features. Concurrently, the DDF module facilitates controlled generation through an adaptive adjustment of X-embedding conditioned on the diffusion steps. In addition, to reveal the multi-modality representational power of AnomalyXFusion, we propose a new dataset, called MVTec Caption. More precisely, MVTec Caption extends 2.2k accurate image-mask-text annotations for the MVTec AD and LOCO datasets. Comprehensive evaluations demonstrate the effectiveness of AnomalyXFusion, especially regarding the fidelity and diversity for logical anomalies. Project page: http:github.com/hujiecpp/MVTec-Caption","url_abs":"https://arxiv.org/abs/2404.19444v2","url_pdf":"https://arxiv.org/pdf/2404.19444v2.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":"anomalyxfusion-multi-modal-anomaly-synthesis","repo_url":"https://github.com/hujiecpp/mvtec-caption","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2404.19444","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.19444"}},"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/hujiecpp/mvtec-caption","reach":{"status":"ok"}}],"summary":{"ran_violates":1,"ran_honours":2,"ran_draft_wrong":2},"by_repo_kind":{"official":{"samples":5,"ran":5,"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":5,"samples":[{"code_sha256_prefix":"4cb732f513d69dfd","entry":"disabled_train","repo":"hujiecpp/mvtec-caption","repo_kind":"official","path":"ddpm.py","file_url":"https://github.com/hujiecpp/mvtec-caption/blob/HEAD/ddpm.py","link_basis":"harvester_set","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"4cb732f513d69dfd"}},{"code_sha256_prefix":"d5726f9e3a5684c3","entry":"get_bert_token_for_string","repo":"hujiecpp/mvtec-caption","repo_kind":"official","path":"embedding_manager.py","file_url":"https://github.com/hujiecpp/mvtec-caption/blob/HEAD/embedding_manager.py","link_basis":"plan_row","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"d5726f9e3a5684c3"}},{"code_sha256_prefix":"41c6eb3a0aa3eb46","entry":"get_clip_token_for_string","repo":"hujiecpp/mvtec-caption","repo_kind":"official","path":"embedding_manager.py","file_url":"https://github.com/hujiecpp/mvtec-caption/blob/HEAD/embedding_manager.py","link_basis":"plan_row","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"41c6eb3a0aa3eb46"}},{"code_sha256_prefix":"71c2df28a1008d03","entry":"get_embedding_for_clip_token","repo":"hujiecpp/mvtec-caption","repo_kind":"official","path":"embedding_manager.py","file_url":"https://github.com/hujiecpp/mvtec-caption/blob/HEAD/embedding_manager.py","link_basis":"plan_row","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"71c2df28a1008d03"}},{"code_sha256_prefix":"d48d8354986e3b0e","entry":"uniform_on_device","repo":"hujiecpp/mvtec-caption","repo_kind":"official","path":"ddpm.py","file_url":"https://github.com/hujiecpp/mvtec-caption/blob/HEAD/ddpm.py","link_basis":"harvester_set","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"d48d8354986e3b0e"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}