Previous works on object detection have achieved high accuracy in closed-set scenarios, but their performance in open-world scenarios is not satisfactory. One of the challenging open-world problems is corner case detection in autonomous driving. Existing detectors struggle with these cases, relying heavily on visual appearance and exhibiting poor generalization ability. In this paper, we propose a solution by reducing the discrepancy between known and unknown classes and introduce a multimodal-enhanced objectness notion learner. Leveraging both vision-centric and image-text modalities, our semi-supervised learning framework imparts objectness knowledge to the student model, enabling class-aware detection. Our approach, Multimodal-Enhanced Objectness Learner (MENOL) for Corner Case Detection, significantly improves recall for novel classes with lower training costs. By achieving a 76.6% mAR-corner and 79.8% mAR-agnostic on the CODA-val dataset with just 5100 labeled training images, MENOL outperforms the baseline ORE by 71.3% and 60.6%, respectively. The code will be available at https://github.com/tryhiseyyysum/MENOL.
@article{arxiv.2402.02026,
title = {Multimodal-Enhanced Objectness Learner for Corner Case Detection in Autonomous Driving},
author = {Lixing Xiao and Ruixiao Shi and Xiaoyang Tang and Yi Zhou},
journal= {arXiv preprint arXiv:2402.02026},
year = {2024}
}
Comments
Accepted to 2024 IEEE International Conference on Image Processing (ICIP) as oral presentation