  Emotion decoding plays an important role in affective human-computer
interaction. However, previous studies ignored the dynamic real-world scenario,
where human experience a blend of multiple emotions which are incrementally
integrated into the model, leading to the multi-label class incremental
learning (MLCIL) problem. Existing methods have difficulty in solving MLCIL
issue due to notorious catastrophic forgetting caused by partial label problem
and inadequate label semantics mining. In this paper, we propose an augmented
emotional semantics learning framework for multi-label class incremental
emotion decoding. Specifically, we design an augmented emotional relation graph
module with label disambiguation to handle the past-missing partial label
problem. Then, we leverage domain knowledge from affective dimension space to
alleviate future-missing partial label problem by knowledge distillation.
Besides, an emotional semantics learning module is constructed with a graph
autoencoder to obtain emotion embeddings in order to guide the
semantic-specific feature decoupling for better multi-label learning. Extensive
experiments on three datasets show the superiority of our method for improving
emotion decoding performance and mitigating forgetting on MLCIL problem.
