Efficient Feature Distillation for Zero-shot Annotation Object Detection

• URL: http://arxiv.org/abs/2303.12145v4
• Date: Thu, 2 Nov 2023 03:08:51 GMT
• Title: Efficient Feature Distillation for Zero-shot Annotation Object Detection
• Authors: Zhuoming Liu, Xuefeng Hu, Ram Nevatia
• Abstract summary: We propose a new setting for detecting unseen objects called Zero-shot object Detection (ZAD) It expands the zero-shot object detection setting by allowing the novel objects to exist in the training images. It also restricts the additional information the detector uses to novel category names.
• Score: 12.116491963892821
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a new setting for detecting unseen objects called Zero-shot Annotation object Detection (ZAD). It expands the zero-shot object detection setting by allowing the novel objects to exist in the training images and restricts the additional information the detector uses to novel category names. Recently, to detect unseen objects, large-scale vision-language models (e.g., CLIP) are leveraged by different methods. The distillation-based methods have good overall performance but suffer from a long training schedule caused by two factors. First, existing work creates distillation regions biased to the base categories, which limits the distillation of novel category information. Second, directly using the raw feature from CLIP for distillation neglects the domain gap between the training data of CLIP and the detection datasets, which makes it difficult to learn the mapping from the image region to the vision-language feature space. To solve these problems, we propose Efficient feature distillation for Zero-shot Annotation object Detection (EZAD). Firstly, EZAD adapts the CLIP's feature space to the target detection domain by re-normalizing CLIP; Secondly, EZAD uses CLIP to generate distillation proposals with potential novel category names to avoid the distillation being overly biased toward the base categories. Finally, EZAD takes advantage of semantic meaning for regression to further improve the model performance. As a result, EZAD outperforms the previous distillation-based methods in COCO by 4% with a much shorter training schedule and achieves a 3% improvement on the LVIS dataset. Our code is available at https://github.com/dragonlzm/EZAD

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