{"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/mipa-mixed-patch-infrared-visible-modality","title":"MiPa: Mixed Patch Infrared-Visible Modality Agnostic Object Detection","arxiv_id":"2404.18849","date":"2024-04-29","proceeding":null,"authors":["Heitor R. Medeiros","David Latortue","Eric Granger","Marco Pedersoli"],"abstract":"In real-world scenarios, using multiple modalities like visible (RGB) and infrared (IR) can greatly improve the performance of a predictive task such as object detection (OD). Multimodal learning is a common way to leverage these modalities, where multiple modality-specific encoders and a fusion module are used to improve performance. In this paper, we tackle a different way to employ RGB and IR modalities, where only one modality or the other is observed by a single shared vision encoder. This realistic setting requires a lower memory footprint and is more suitable for applications such as autonomous driving and surveillance, which commonly rely on RGB and IR data. However, when learning a single encoder on multiple modalities, one modality can dominate the other, producing uneven recognition results. This work investigates how to efficiently leverage RGB and IR modalities to train a common transformer-based OD vision encoder, while countering the effects of modality imbalance. For this, we introduce a novel training technique to Mix Patches (MiPa) from the two modalities, in conjunction with a patch-wise modality agnostic module, for learning a common representation of both modalities. Our experiments show that MiPa can learn a representation to reach competitive results on traditional RGB/IR benchmarks while only requiring a single modality during inference. Our code is available at: https://github.com/heitorrapela/MiPa.","url_abs":"https://arxiv.org/abs/2404.18849v2","url_pdf":"https://arxiv.org/pdf/2404.18849v2.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":"mipa-mixed-patch-infrared-visible-modality","repo_url":"https://github.com/heitorrapela/mipa","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"multispectral-object-detection","task_name":"Multispectral Object Detection"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"pedestrian-detection","task_name":"Pedestrian Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multispectral-object-detection-on-flir-1","task":"Multispectral Object Detection","dataset":"FLIR","model":"MiPa","rank_in_archive_order":7,"of":18,"metrics":{"mAP":"44.8%","mAP50":"81.3%"},"uses_additional_data":false},{"leaderboard":"/sota/object-detection-on-flir","task":"Object Detection","dataset":"FLIR","model":"MiPa","rank_in_archive_order":1,"of":1,"metrics":{"AP 0.5":"0.813"},"uses_additional_data":false},{"leaderboard":"/sota/object-detection-on-llvip","task":"Object Detection","dataset":"LLVIP","model":"MiPa","rank_in_archive_order":1,"of":1,"metrics":{"AP":"0.665"},"uses_additional_data":false},{"leaderboard":"/sota/pedestrian-detection-on-llvip","task":"Pedestrian Detection","dataset":"LLVIP","model":"MiPa","rank_in_archive_order":3,"of":15,"metrics":{"AP":"0.665"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}