LLMDet: Learning Strong Open-Vocabulary Object Detectors under the Supervision of Large Language Models

• URL: http://arxiv.org/abs/2501.18954v1
• Date: Fri, 31 Jan 2025 08:27:31 GMT
• Title: LLMDet: Learning Strong Open-Vocabulary Object Detectors under the Supervision of Large Language Models
• Authors: Shenghao Fu, Qize Yang, Qijie Mo, Junkai Yan, Xihan Wei, Jingke Meng, Xiaohua Xie, Wei-Shi Zheng,
• Abstract summary: Recent open-vocabulary detectors achieve promising performance with abundant region-level annotated data. We show that an open-vocabulary detector co-training with a large language model by generating image-level detailed captions for each image can further improve performance.
• Score: 44.578308186225826
• License:
• Abstract: Recent open-vocabulary detectors achieve promising performance with abundant region-level annotated data. In this work, we show that an open-vocabulary detector co-training with a large language model by generating image-level detailed captions for each image can further improve performance. To achieve the goal, we first collect a dataset, GroundingCap-1M, wherein each image is accompanied by associated grounding labels and an image-level detailed caption. With this dataset, we finetune an open-vocabulary detector with training objectives including a standard grounding loss and a caption generation loss. We take advantage of a large language model to generate both region-level short captions for each region of interest and image-level long captions for the whole image. Under the supervision of the large language model, the resulting detector, LLMDet, outperforms the baseline by a clear margin, enjoying superior open-vocabulary ability. Further, we show that the improved LLMDet can in turn build a stronger large multi-modal model, achieving mutual benefits. The code, model, and dataset is available at https://github.com/iSEE-Laboratory/LLMDet.

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