{"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/revisiting-multimodal-fusion-for-3d-anomaly","title":"Revisiting Multimodal Fusion for 3D Anomaly Detection from an Architectural Perspective","arxiv_id":"2412.17297","date":"2024-12-23","proceeding":null,"authors":["Kaifang Long","Guoyang Xie","Lianbo Ma","Jiaqi Liu","Zhichao Lu"],"abstract":"Existing efforts to boost multimodal fusion of 3D anomaly detection (3D-AD) primarily concentrate on devising more effective multimodal fusion strategies. However, little attention was devoted to analyzing the role of multimodal fusion architecture (topology) design in contributing to 3D-AD. In this paper, we aim to bridge this gap and present a systematic study on the impact of multimodal fusion architecture design on 3D-AD. This work considers the multimodal fusion architecture design at the intra-module fusion level, i.e., independent modality-specific modules, involving early, middle or late multimodal features with specific fusion operations, and also at the inter-module fusion level, i.e., the strategies to fuse those modules. In both cases, we first derive insights through theoretically and experimentally exploring how architectural designs influence 3D-AD. Then, we extend SOTA neural architecture search (NAS) paradigm and propose 3D-ADNAS to simultaneously search across multimodal fusion strategies and modality-specific modules for the first time.Extensive experiments show that 3D-ADNAS obtains consistent improvements in 3D-AD across various model capacities in terms of accuracy, frame rate, and memory usage, and it exhibits great potential in dealing with few-shot 3D-AD tasks.","url_abs":"https://arxiv.org/abs/2412.17297v1","url_pdf":"https://arxiv.org/pdf/2412.17297v1.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":"revisiting-multimodal-fusion-for-3d-anomaly","repo_url":"https://github.com/longkaifang/3d-adnas","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"3d-anomaly-detection","task_name":"3D Anomaly Detection"},{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"architecture-search","task_name":"Neural Architecture Search"}],"methods":[{"method_slug":"attention","method_name":"Attention"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}