Multi-label Class Incremental Emotion Decoding with Augmented Emotional Semantics Learning

TL;DR

This paper introduces an augmented emotional semantics learning framework to improve multi-label class incremental emotion decoding, effectively addressing catastrophic forgetting and partial label issues in dynamic real-world scenarios.

Contribution

The proposed framework incorporates an emotional relation graph, domain knowledge distillation, and a graph autoencoder to enhance multi-label emotion decoding in incremental learning settings.

Findings

Outperforms existing methods in emotion decoding accuracy

Reduces catastrophic forgetting in multi-label incremental learning

Effectively handles partial label problems

Abstract

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.