RTGen: Generating Region-Text Pairs for Open-Vocabulary Object Detection

• URL: http://arxiv.org/abs/2405.19854v1
• Date: Thu, 30 May 2024 09:03:23 GMT
• Title: RTGen: Generating Region-Text Pairs for Open-Vocabulary Object Detection
• Authors: Fangyi Chen, Han Zhang, Zhantao Yang, Hao Chen, Kai Hu, Marios Savvides,
• Abstract summary: Open-vocabulary object detection requires solid modeling of the region-semantic relationship. We propose RTGen to generate scalable open-vocabulary region-text pairs.
• Score: 20.630629383286262
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Open-vocabulary object detection (OVD) requires solid modeling of the region-semantic relationship, which could be learned from massive region-text pairs. However, such data is limited in practice due to significant annotation costs. In this work, we propose RTGen to generate scalable open-vocabulary region-text pairs and demonstrate its capability to boost the performance of open-vocabulary object detection. RTGen includes both text-to-region and region-to-text generation processes on scalable image-caption data. The text-to-region generation is powered by image inpainting, directed by our proposed scene-aware inpainting guider for overall layout harmony. For region-to-text generation, we perform multiple region-level image captioning with various prompts and select the best matching text according to CLIP similarity. To facilitate detection training on region-text pairs, we also introduce a localization-aware region-text contrastive loss that learns object proposals tailored with different localization qualities. Extensive experiments demonstrate that our RTGen can serve as a scalable, semantically rich, and effective source for open-vocabulary object detection and continue to improve the model performance when more data is utilized, delivering superior performance compared to the existing state-of-the-art methods.

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