DetCLIPv2: Scalable Open-Vocabulary Object Detection Pre-training via Word-Region Alignment

• URL: http://arxiv.org/abs/2304.04514v1
• Date: Mon, 10 Apr 2023 11:08:15 GMT
• Title: DetCLIPv2: Scalable Open-Vocabulary Object Detection Pre-training via Word-Region Alignment
• Authors: Lewei Yao, Jianhua Han, Xiaodan Liang, Dan Xu, Wei Zhang, Zhenguo Li, Hang Xu
• Abstract summary: DetCLIPv2 is an efficient training framework that incorporates large-scale image-text pairs to achieve open-vocabulary object detection. DetCLIPv2 directly learns the fine-grained word-region alignment from massive image-text pairs in an end-to-end manner. With 13M image-text pairs for pre-training, DetCLIPv2 demonstrates superior open-vocabulary detection performance.
• Score: 104.54362490182335
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper presents DetCLIPv2, an efficient and scalable training framework that incorporates large-scale image-text pairs to achieve open-vocabulary object detection (OVD). Unlike previous OVD frameworks that typically rely on a pre-trained vision-language model (e.g., CLIP) or exploit image-text pairs via a pseudo labeling process, DetCLIPv2 directly learns the fine-grained word-region alignment from massive image-text pairs in an end-to-end manner. To accomplish this, we employ a maximum word-region similarity between region proposals and textual words to guide the contrastive objective. To enable the model to gain localization capability while learning broad concepts, DetCLIPv2 is trained with a hybrid supervision from detection, grounding and image-text pair data under a unified data formulation. By jointly training with an alternating scheme and adopting low-resolution input for image-text pairs, DetCLIPv2 exploits image-text pair data efficiently and effectively: DetCLIPv2 utilizes 13X more image-text pairs than DetCLIP with a similar training time and improves performance. With 13M image-text pairs for pre-training, DetCLIPv2 demonstrates superior open-vocabulary detection performance, e.g., DetCLIPv2 with Swin-T backbone achieves 40.4% zero-shot AP on the LVIS benchmark, which outperforms previous works GLIP/GLIPv2/DetCLIP by 14.4/11.4/4.5% AP, respectively, and even beats its fully-supervised counterpart by a large margin.

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