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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.16247 |
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| _version_ | 1866914928492806144 |
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| author | Xu, Yiran Zhong, Haoxiang Wu, Kai Li, Jialin Liu, Yong Wang, Chengjie Xia, Shu-Tao Liao, Hongen |
| author_facet | Xu, Yiran Zhong, Haoxiang Wu, Kai Li, Jialin Liu, Yong Wang, Chengjie Xia, Shu-Tao Liao, Hongen |
| contents | Object detectors have shown outstanding performance on various public datasets. However, annotating a new dataset for a new task is usually unavoidable in real, since 1) a single existing dataset usually does not contain all object categories needed; 2) using multiple datasets usually suffers from annotation incompletion and heterogeneous features. We propose a novel problem as "Annotation-incomplete Multi-dataset Detection", and develop an end-to-end multi-task learning architecture which can accurately detect all the object categories with multiple partially annotated datasets. Specifically, we propose an attention feature extractor which helps to mine the relations among different datasets. Besides, a knowledge amalgamation training strategy is incorporated to accommodate heterogeneous features from different sources. Extensive experiments on different object detection datasets demonstrate the effectiveness of our methods and an improvement of 2.17%, 2.10% in mAP can be achieved on COCO and VOC respectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_16247 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Anno-incomplete Multi-dataset Detection Xu, Yiran Zhong, Haoxiang Wu, Kai Li, Jialin Liu, Yong Wang, Chengjie Xia, Shu-Tao Liao, Hongen Computer Vision and Pattern Recognition Object detectors have shown outstanding performance on various public datasets. However, annotating a new dataset for a new task is usually unavoidable in real, since 1) a single existing dataset usually does not contain all object categories needed; 2) using multiple datasets usually suffers from annotation incompletion and heterogeneous features. We propose a novel problem as "Annotation-incomplete Multi-dataset Detection", and develop an end-to-end multi-task learning architecture which can accurately detect all the object categories with multiple partially annotated datasets. Specifically, we propose an attention feature extractor which helps to mine the relations among different datasets. Besides, a knowledge amalgamation training strategy is incorporated to accommodate heterogeneous features from different sources. Extensive experiments on different object detection datasets demonstrate the effectiveness of our methods and an improvement of 2.17%, 2.10% in mAP can be achieved on COCO and VOC respectively. |
| title | Anno-incomplete Multi-dataset Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.16247 |