CoDet: Co-Occurrence Guided Region-Word Alignment for Open-Vocabulary Object Detection

• URL: http://arxiv.org/abs/2310.16667v1
• Date: Wed, 25 Oct 2023 14:31:02 GMT
• Title: CoDet: Co-Occurrence Guided Region-Word Alignment for Open-Vocabulary Object Detection
• Authors: Chuofan Ma, Yi Jiang, Xin Wen, Zehuan Yuan, Xiaojuan Qi
• Abstract summary: CoDet is a novel approach to learn object-level vision-language representations for open-vocabulary object detection. By grouping images that mention a shared concept in their captions, objects corresponding to the shared concept shall exhibit high co-occurrence. CoDet has superior performances and compelling scalability in open-vocabulary detection.
• Score: 78.0010542552784
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deriving reliable region-word alignment from image-text pairs is critical to learn object-level vision-language representations for open-vocabulary object detection. Existing methods typically rely on pre-trained or self-trained vision-language models for alignment, which are prone to limitations in localization accuracy or generalization capabilities. In this paper, we propose CoDet, a novel approach that overcomes the reliance on pre-aligned vision-language space by reformulating region-word alignment as a co-occurring object discovery problem. Intuitively, by grouping images that mention a shared concept in their captions, objects corresponding to the shared concept shall exhibit high co-occurrence among the group. CoDet then leverages visual similarities to discover the co-occurring objects and align them with the shared concept. Extensive experiments demonstrate that CoDet has superior performances and compelling scalability in open-vocabulary detection, e.g., by scaling up the visual backbone, CoDet achieves 37.0 $\text{AP}^m_{novel}$ and 44.7 $\text{AP}^m_{all}$ on OV-LVIS, surpassing the previous SoTA by 4.2 $\text{AP}^m_{novel}$ and 9.8 $\text{AP}^m_{all}$. Code is available at https://github.com/CVMI-Lab/CoDet.

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