Summary of Multimodal-enhanced Objectness Learner For Corner Case Detection in Autonomous Driving, by Lixing Xiao et al.
Multimodal-Enhanced Objectness Learner for Corner Case Detection in Autonomous Driving
by Lixing Xiao, Ruixiao Shi, Xiaoyang Tang, Yi Zhou
First submitted to arxiv on: 3 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the challenge of object detection in open-world scenarios, specifically corner case detection in autonomous driving. Existing detectors rely heavily on visual appearance and struggle with novel classes. The proposed Multimodal-Enhanced Objectness Learner (MENOL) framework reduces the discrepancy between known and unknown classes by leveraging both vision-centric and image-text modalities. MENOL enables class-aware detection through semi-supervised learning, significantly improving recall for novel classes at lower training costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a nutshell, this paper develops an innovative approach to detect objects in new and unexpected situations, like corner cases in autonomous driving. It’s like teaching a machine to recognize things it hasn’t seen before by combining visual and text information. The result is better object detection with fewer labeled examples needed. This could help make self-driving cars safer and more reliable. |
Keywords
» Artificial intelligence » Object detection » Recall » Semi supervised
