A new OOD object detection benchmark with near, far, and farther splits and open-set metrics shows close unknown objects are found more often but are also more frequently mistaken for known objects.
The BRAVO Semantic Segmentation Challenge Results in UNCV2024
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abstract
We propose the unified BRAVO challenge to benchmark the reliability of semantic segmentation models under realistic perturbations and unknown out-of-distribution (OOD) scenarios. We define two categories of reliability: (1) semantic reliability, which reflects the model's accuracy and calibration when exposed to various perturbations; and (2) OOD reliability, which measures the model's ability to detect object classes that are unknown during training. The challenge attracted nearly 100 submissions from international teams representing notable research institutions. The results reveal interesting insights into the importance of large-scale pre-training and minimal architectural design in developing robust and reliable semantic segmentation models.
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FindMeIfYouCan: Bringing Open Set metrics to $\textit{near} $, $ \textit{far} $ and $\textit{farther}$ Out-of-Distribution Object Detection
A new OOD object detection benchmark with near, far, and farther splits and open-set metrics shows close unknown objects are found more often but are also more frequently mistaken for known objects.