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Dual Domain-Adversarial Learning for Audio-Visual Saliency Prediction. (arXiv:2208.05220v2 [cs.CV] UPDATED)
Aug. 17, 2022, 1:12 a.m. | Yingzi Fan, Longfei Han, Yue Zhang, Lechao Cheng, Chen Xia, Di Hu
cs.CV updates on arXiv.org arxiv.org
Both visual and auditory information are valuable to determine the salient
regions in videos. Deep convolution neural networks (CNN) showcase strong
capacity in coping with the audio-visual saliency prediction task. Due to
various factors such as shooting scenes and weather, there often exists
moderate distribution discrepancy between source training data and target
testing data. The domain discrepancy induces to performance degradation on
target testing data for CNN models. This paper makes an early attempt to tackle
the unsupervised domain adaptation …
More from arxiv.org / cs.CV updates on arXiv.org
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