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|a 10.1109/TIP.2023.3275538
|2 doi
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|a pubmed24n1189.xml
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|a DE-627
|b ger
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|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Zhou, Wujie
|e verfasserin
|4 aut
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| 245 |
1 |
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|a WaveNet
|b Wavelet Network With Knowledge Distillation for RGB-T Salient Object Detection
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| 264 |
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|c 2023
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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 Completed 28.05.2023
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|a Date Revised 28.05.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a In recent years, various neural network architectures for computer vision have been devised, such as the visual transformer and multilayer perceptron (MLP). A transformer based on an attention mechanism can outperform a traditional convolutional neural network. Compared with the convolutional neural network and transformer, the MLP introduces less inductive bias and achieves stronger generalization. In addition, a transformer shows an exponential increase in the inference, training, and debugging times. Considering a wave function representation, we propose the WaveNet architecture that adopts a novel vision task-oriented wavelet-based MLP for feature extraction to perform salient object detection in RGB (red-green-blue)-thermal infrared images. In addition, we apply knowledge distillation to a transformer as an advanced teacher network to acquire rich semantic and geometric information and guide WaveNet learning with this information. Following the shortest-path concept, we adopt the Kullback-Leibler distance as a regularization term for the RGB features to be as similar to the thermal infrared features as possible. The discrete wavelet transform allows for the examination of frequency-domain features in a local time domain and time-domain features in a local frequency domain. We apply this representation ability to perform cross-modality feature fusion. Specifically, we introduce a progressively cascaded sine-cosine module for cross-layer feature fusion and use low-level features to obtain clear boundaries of salient objects through the MLP. Results from extensive experiments indicate that the proposed WaveNet achieves impressive performance on benchmark RGB-thermal infrared datasets. The results and code are publicly available at https://github.com/nowander/WaveNet
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|a Journal Article
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| 700 |
1 |
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|a Sun, Fan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Jiang, Qiuping
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Cong, Runmin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Hwang, Jenq-Neng
|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 32(2023) vom: 16., Seite 3027-3039
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|x 1941-0042
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|g volume:32
|g year:2023
|g day:16
|g pages:3027-3039
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|u http://dx.doi.org/10.1109/TIP.2023.3275538
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