{"ID":2890304,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.18925","arxiv_id":"2507.18925","title":"WiSE-OD: Benchmarking Robustness in Infrared Object Detection","abstract":"Object detection (OD) in infrared (IR) imagery is critical for low-light and nighttime applications. However, the scarcity of large-scale IR datasets forces models to rely on weights pre-trained on RGB images. While fine-tuning on IR improves accuracy, it often compromises robustness under distribution shifts due to the inherent modality gap between RGB and IR. To address this, we introduce LLVIP-C and FLIR-C, two cross-modality out-of-distribution (OOD) benchmarks built by applying corruptions to standard IR datasets. Additionally, to fully leverage the complementary knowledge from RGB and infrared-trained models, we propose WiSE-OD, a weight-space ensembling method with two variants: WiSE-OD$_{ZS}$, which combines RGB zero-shot and IR fine-tuned weights, and WiSE-OD$_{LP}$, which blends zero-shot and linear probing. Evaluated using four RGB-pretrained detectors and two robust baselines on our benchmark and in the real-world out-of-distribution M3FD dataset, our WiSE-OD improves robustness across modalities and to corruption in synthetic and real-world distribution shifts without any additional training or inference costs. Our code is available at: https://github.com/heitorrapela/wiseod.","short_abstract":"Object detection (OD) in infrared (IR) imagery is critical for low-light and nighttime applications. However, the scarcity of large-scale IR datasets forces models to rely on weights pre-trained on RGB images. While fine-tuning on IR improves accuracy, it often compromises robustness under distribution shifts due to th...","url_abs":"https://arxiv.org/abs/2507.18925","url_pdf":"https://arxiv.org/pdf/2507.18925v2","authors":"[\"Heitor R. Medeiros\",\"Atif Belal\",\"Masih Aminbeidokhti\",\"Eric Granger\",\"Marco Pedersoli\"]","published":"2025-07-25T03:33:50Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[]","has_code":false,"code_links":[{"ID":611762,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2890304,"paper_url":"https://arxiv.org/abs/2507.18925","paper_title":"WiSE-OD: Benchmarking Robustness in Infrared Object Detection","repo_url":"https://github.com/heitorrapela/wiseod","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}