Cyclic Contrastive Knowledge Transfer for Open-Vocabulary Object Detection

• URL: http://arxiv.org/abs/2503.11005v2
• Date: Wed, 02 Apr 2025 07:12:56 GMT
• Title: Cyclic Contrastive Knowledge Transfer for Open-Vocabulary Object Detection
• Authors: Chuhan Zhang, Chaoyang Zhu, Pingcheng Dong, Long Chen, Dong Zhang,
• Abstract summary: We propose CCKT-Det trained without any extra supervision.<n>The proposed framework constructs a cyclic and dynamic knowledge transfer from language queries and visual region features extracted from vision-language models (VLMs)<n> CCKT-Det can consistently improve performance as the scale of VLMs increases, all while requiring the detector at a moderate level of overhead.
• Score: 11.497620257835964
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In pursuit of detecting unstinted objects that extend beyond predefined categories, prior arts of open-vocabulary object detection (OVD) typically resort to pretrained vision-language models (VLMs) for base-to-novel category generalization. However, to mitigate the misalignment between upstream image-text pretraining and downstream region-level perception, additional supervisions are indispensable, eg, image-text pairs or pseudo annotations generated via self-training strategies. In this work, we propose CCKT-Det trained without any extra supervision. The proposed framework constructs a cyclic and dynamic knowledge transfer from language queries and visual region features extracted from VLMs, which forces the detector to closely align with the visual-semantic space of VLMs. Specifically, 1) we prefilter and inject semantic priors to guide the learning of queries, and 2) introduce a regional contrastive loss to improve the awareness of queries on novel objects. CCKT-Det can consistently improve performance as the scale of VLMs increases, all while requiring the detector at a moderate level of computation overhead. Comprehensive experimental results demonstrate that our method achieves performance gain of +2.9% and +10.2% AP50 over previous state-of-the-arts on the challenging COCO benchmark, both without and with a stronger teacher model.

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