1 code implementation • 14 Nov 2022 • Jiaxin Ye, Xin-Cheng Wen, Yujie Wei, Yong Xu, KunHong Liu, Hongming Shan
Specifically, TIM-Net first employs temporal-aware blocks to learn temporal affective representation, then integrates complementary information from the past and the future to enrich contextual representations, and finally, fuses multiple time scale features for better adaptation to the emotional variation.
1 code implementation • 4 Aug 2023 • Jiaxin Ye, Yujie Wei, Xin-Cheng Wen, Chenglong Ma, Zhizhong Huang, KunHong Liu, Hongming Shan
On one hand, our contrastive emotion decoupling achieves decoupling learning via a contrastive decoupling loss to strengthen the separability of emotion-relevant features from corpus-specific ones.