DPCNet: Dual Path Multi-Excitation Collaborative Network for Facial Expression Representation Learning in Videos
Current works of facial expression learning in video consume significant computational resources to learn spatial channel feature representations and temporal relationships. To mitigate this issue, we propose a Dual Path multi-excitation Collaborative Network (DPCNet) to learn the critical information for facial expression representation from fewer keyframes in videos. Specifically, the DPCNet learns the important regions and keyframes from a tuple of four view-grouped frames by multi-excitation modules and produces dual-path representations of one video with consistency under two regularization strategies. A spatial-frame excitation module and a channel-temporal aggregation module are introduced consecutively to learn spatial-frame representation and generate complementary channel-temporal aggregation, respectively. Moreover, we design a multi-frame regularization loss to enforce the representation of multiple frames in the dual view to be semantically coherent. To obtain consistent prediction probabilities from the dual path, we further propose a dual path regularization loss, aiming to minimize the divergence between the distributions of two-path embeddings. Extensive experiments and ablation studies show that the DPCNet can significantly improve the performance of video-based FER and achieve state-of-the-art results on the large-scale DFEW dataset.
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