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240216s2024 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2024.3364495
|2 doi
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|a pubmed25n1227.xml
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|a eng
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| 100 |
1 |
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|a Su, Binyi
|e verfasserin
|4 aut
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|a Towards Generalized Few-Shot Open-Set Object Detection
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|c 2024
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 16.02.2024
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|a published: Print-Electronic
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|a Citation Status Publisher
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|a Open-set object detection (OSOD) aims to detect the known categories and reject unknown objects in a dynamic world, which has achieved significant attention. However, previous approaches only consider this problem in data-abundant conditions, while neglecting the few-shot scenes. In this paper, we seek a solution for the generalized few-shot open-set object detection (G-FOOD), which aims to avoid detecting unknown classes as known classes with a high confidence score while maintaining the performance of few-shot detection. The main challenge for this task is that few training samples induce the model to overfit on the known classes, resulting in a poor open-set performance. We propose a new G-FOOD algorithm to tackle this issue, named Few-shOt Open-set Detector (FOOD), which contains a novel class weight sparsification classifier (CWSC) and a novel unknown decoupling learner (UDL). To prevent over-fitting, CWSC randomly sparses parts of the normalized weights for the logit prediction of all classes, and then decreases the co-adaptability between the class and its neighbors. Alongside, UDL decouples training the unknown class and enables the model to form a compact unknown decision boundary. Thus, the unknown objects can be identified with a confidence probability without any threshold, prototype, or generation. We compare our method with several state-of-the-art OSOD methods in few-shot scenes and observe that our method improves the F-score of unknown classes by 4.80%-9.08% across all shots in VOC-COCO dataset settings
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|a Journal Article
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|a Zhang, Hua
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Jingzhi
|e verfasserin
|4 aut
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| 700 |
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|a Zhou, Zhong
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g PP(2024) vom: 15. Feb.
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnas
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| 773 |
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|g volume:PP
|g year:2024
|g day:15
|g month:02
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|u http://dx.doi.org/10.1109/TIP.2024.3364495
|3 Volltext
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