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In the task of emotion recognition from videos, a key improvement has been tofocus on emotions over time rather than a single frame. There are manyarchitectures to address this task such as GRUs, LSTMs, Self-Attention,Transformers, and Temporal Convolutional Networks (TCNs). However, thesemethods suffer from high memory usage, large amounts of operations, or poorgradients. We propose a method known as Neighborhood Attention withConvolutions TCN (NAC-TCN) which incorporates the benefits of attention andTemporal Convolutional Networks while ensuring that causal relationships areunderstood which results in a reduction in computation and memory cost. Weaccomplish this by introducing a causal version of Dilated NeighborhoodAttention while incorporating it with convolutions. Our model achievescomparable, better, or state-of-the-art performance over TCNs, TCAN, LSTMs, andGRUs while requiring fewer parameters on standard emotion recognition datasets.We publish our code online for easy reproducibility and use in other projects.

NAC-TCN: Temporal Convolutional Networks with Causal Dilated Neighborhood Attention for Emotion Understanding