Out-of-distribution (OOD) detection remains challenging for image classifiers, especially when near-OOD samples lie close to in-distribution (ID) class boundaries. Recent vision-language detectors imp...
arXiv:2609.01027v1 Announce Type: new
Abstract: Out-of-distribution (OOD) detection predicts whether a test image belongs to none of the predefined classes. To evaluate this task, benchmarks need ima...
By Ruslan Rozumnyi, Mat\v{e}j Such\'anek, Tom\'a\v{s} Voj\'i\v{r}, Kl\'ara Janou\v{s}kov\'a, Ji\v{r}\'i Matas
arXiv:2603. 18481v2 Announce Type: replace-cross Abstract: Out-of-distribution (OOD) detection remains a critical challenge in open-world learning, where models must adapt to evolving data distributions.
By Aditi Naiknaware, Salimeh Sekeh
arXiv:2508.10148v2 Announce Type: replace-cross
Abstract: Accurate and explainable out-of-distribution (OOD) detection is required to use machine learning systems safely. Previous work has shown that...
By Maria Stoica, Francesco Leofante, Alessio Lomuscio
arXiv:2608. 08308v1 Announce Type: cross Abstract: Modern vision systems must operate in "open-world" settings, where models must recognize known categories and detect unseen or anomalous content.
By Anastasios Romanos Varvarigos, Nikos Giakoumoglou, Tania Stathaki
CODE: Cross-Modal Calibration and Dynamic Suppression for Open World Object Detection introduces a unified inference-time framework that addresses semantic ambiguity and over-suppression in multimodal OWOD systems. It comprises Cross-Modal Joint Confidence Calibration, Uncertainty-Guided Universal Objectness Enhancement, and Dynamic Outlier Suppression via Confidence Margin. Experiments on the Real-World Detection benchmark with the OWL‑ViT L/14 backbone show CODE achieving 21.7 U‑mAP and 40.8 K‑mAP, surpassing prior state‑of‑the‑art results by 2.6 and 2.3 points respectively.
By Hao Xu, Zhaoning Shi, Hehe Jin, Bo Ma