  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.
