Decoupled Mixup for Generalized Visual Recognition
- URL: http://arxiv.org/abs/2210.14783v1
- Date: Wed, 26 Oct 2022 15:21:39 GMT
- Title: Decoupled Mixup for Generalized Visual Recognition
- Authors: Haozhe Liu, Wentian Zhang, Jinheng Xie, Haoqian Wu, Bing Li, Ziqi
Zhang, Yuexiang Li, Yawen Huang, Bernard Ghanem, Yefeng Zheng
- Abstract summary: We propose a novel "Decoupled-Mixup" method to train CNN models for visual recognition.
Our method decouples each image into discriminative and noise-prone regions, and then heterogeneously combines these regions to train CNN models.
Experiment results show the high generalization performance of our method on testing data that are composed of unseen contexts.
- Score: 71.13734761715472
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Convolutional neural networks (CNN) have demonstrated remarkable performance
when the training and testing data are from the same distribution. However,
such trained CNN models often largely degrade on testing data which is unseen
and Out-Of-the-Distribution (OOD). To address this issue, we propose a novel
"Decoupled-Mixup" method to train CNN models for OOD visual recognition.
Different from previous work combining pairs of images homogeneously, our
method decouples each image into discriminative and noise-prone regions, and
then heterogeneously combines these regions of image pairs to train CNN models.
Since the observation is that noise-prone regions such as textural and clutter
backgrounds are adverse to the generalization ability of CNN models during
training, we enhance features from discriminative regions and suppress
noise-prone ones when combining an image pair. To further improve the
generalization ability of trained models, we propose to disentangle
discriminative and noise-prone regions in frequency-based and context-based
fashions. Experiment results show the high generalization performance of our
method on testing data that are composed of unseen contexts, where our method
achieves 85.76\% top-1 accuracy in Track-1 and 79.92\% in Track-2 in the NICO
Challenge. The source code is available at
https://github.com/HaozheLiu-ST/NICOChallenge-OOD-Classification.
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