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Weakly-supervised object localization with gradient-pyramid feature
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DOI：10.1007/s10489-022-03686-y.png)
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摘要
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As a basic task of computer vision task, object localization plays an important role in many computer vision based applications. Supervised methods employ manual location labels to learn to localize the objects directly, but incomplete or incorrectly assigned location labels affect localization accuracy, and the cost of manual labelling should also be extremely large. This paper proposes a weakly-supervised localization method based on a multi-scale gradient-pyramid feature, which employs the weighted gradient features on the multiple convolutional layers in order to generate a gradient-pyramid feature for object localization. Pairs of gradients and features from different layers are first extracted to compute the gradient features. Then, during the fusion of the gradient features through a pyramid model, the larger value is selected as the result of the fusion task without using the concatenated method. Finally, the multi-scale gradient-pyramid feature is obtained and used to have a more accurate object localization by using the region scaling operation. Our proposed method can be directly integrated into the pre-trained classification model to perform object localization without additional training. Experimental results on the ILSVRC 2016 dataset and CUB-200-2011 dataset show that the proposed method can achieve better object localization performance.
Keyword:
Weakly-supervised object localization
Multi-scale gradients and features
Pyramid model
期刊
IF:
3.5
论文数:
7.6K
被引数:
1.7W
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