Leveraging Bottom-Up and Top-Down Attention for Few-Shot Object Detection
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| Format: | Preprint |
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2020
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| _version_ | 1866913566370562048 |
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| author | Chen, Xianyu Jiang, Ming Zhao, Qi |
| author_facet | Chen, Xianyu Jiang, Ming Zhao, Qi |
| contents | Few-shot object detection aims at detecting objects with few annotated examples, which remains a challenging research problem yet to be explored. Recent studies have shown the effectiveness of self-learned top-down attention mechanisms in object detection and other vision tasks. The top-down attention, however, is less effective at improving the performance of few-shot detectors. Due to the insufficient training data, object detectors cannot effectively generate attention maps for few-shot examples. To improve the performance and interpretability of few-shot object detectors, we propose an attentive few-shot object detection network (AttFDNet) that takes the advantages of both top-down and bottom-up attention. Being task-agnostic, the bottom-up attention serves as a prior that helps detect and localize naturally salient objects. We further address specific challenges in few-shot object detection by introducing two novel loss terms and a hybrid few-shot learning strategy. Experimental results and visualization demonstrate the complementary nature of the two types of attention and their roles in few-shot object detection. Codes are available at https://github.com/chenxy99/AttFDNet. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2007_12104 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Leveraging Bottom-Up and Top-Down Attention for Few-Shot Object Detection Chen, Xianyu Jiang, Ming Zhao, Qi Computer Vision and Pattern Recognition Few-shot object detection aims at detecting objects with few annotated examples, which remains a challenging research problem yet to be explored. Recent studies have shown the effectiveness of self-learned top-down attention mechanisms in object detection and other vision tasks. The top-down attention, however, is less effective at improving the performance of few-shot detectors. Due to the insufficient training data, object detectors cannot effectively generate attention maps for few-shot examples. To improve the performance and interpretability of few-shot object detectors, we propose an attentive few-shot object detection network (AttFDNet) that takes the advantages of both top-down and bottom-up attention. Being task-agnostic, the bottom-up attention serves as a prior that helps detect and localize naturally salient objects. We further address specific challenges in few-shot object detection by introducing two novel loss terms and a hybrid few-shot learning strategy. Experimental results and visualization demonstrate the complementary nature of the two types of attention and their roles in few-shot object detection. Codes are available at https://github.com/chenxy99/AttFDNet. |
| title | Leveraging Bottom-Up and Top-Down Attention for Few-Shot Object Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2007.12104 |