arXiv:2004.08195
공식 코드 · Papers with Code
The FaceChannel: A Light-weight Deep Neural Network for Facial Expression Recognition
원문 초록
Current state-of-the-art models for automatic FER are based on very deep neural networks that are difficult to train. This makes it challenging to adapt these models to changing conditions, a requirement from FER models given the subjective nature of affect perception and understanding. In this paper, we address this problem by formalizing the FaceChannel, a light-weight neural network that has much fewer parameters than common deep neural networks. We perform a series of experiments on different benchmark datasets to demonstrate how the FaceChannel achieves a comparable, if not better, performance, as compared to the current state-of-the-art in FER.
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Disentangled Source-Free Personalization for Facial Expression Recognition with Neutral Target Data
V-NAW: Video-based Noise-aware Adaptive Weighting for Facial Expression Recognition
Biased Heritage: How Datasets Shape Models in Facial Expression Recognition
Navigating Label Ambiguity for Facial Expression Recognition in the Wild
Unimodal and Multimodal Static Facial Expression Recognition for Virtual Reality Users with EmoHeVRDB
QCS: Feature Refining from Quadruplet Cross Similarity for Facial Expression Recognition
토론
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