[2604.08261] DBMF: A Dual-Branch Multimodal Framework for Out-of-Distribution Detection

Computer Science > Computer Vision and Pattern Recognition arXiv:2604.08261 (cs) [Submitted on 9 Apr 2026 (v1), last revised 15 Apr 2026 (this version, v2)] Title:DBMF: A Dual-Branch Multimodal Framework for Out-of-Distribution Detection Authors:Jiangbei Yue, Darren Treanor, Venkataraman Subramanian, Sharib Ali View a PDF of the paper titled DBMF: A Dual-Branch Multimodal Framework for Out-of-Distribution Detection, by Jiangbei Yue and 2 other authors View PDF HTML (experimental) Abstract:The complex and dynamic real-world clinical environment demands reliable deep learning (DL) systems. Out-of-distribution (OOD) detection plays a critical role in enhancing the reliability and generalizability of DL models when encountering data that deviate from the training distribution, such as unseen disease cases. However, existing OOD detection methods typically rely either on a single visual modality or solely on image-text matching, failing to fully leverage multimodal information. To overcome the challenge, we propose a novel dual-branch multimodal framework by introducing a text-image branch and a vision branch. Our framework fully exploits multimodal representations to identify OOD samples through these two complementary branches. After training, we compute scores from the text-image branch ($S_t$) and vision branch ($S_v$), and integrate them to obtain the final OOD score $S$ that is compared with a threshold for OOD detection. Comprehensive experiments on publicly available en...