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.

提出了一种增强的情感语义学习框架，用于多标签类别增量情感解码，通过标签消歧和知识蒸馏解决了部分标签问题和遗忘问题，实验证明该方法在提高情感解码性能和减轻多标签类别增量学习中的遗忘问题方面优于现有方法。

增强情感语义学习的多标签增量情感解码

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

Prompt tuning has emerged as an effective rehearsal-free technique for
class-incremental learning (CIL) that learns a tiny set of task-specific
parameters (or prompts) to instruct a pre-trained transformer to learn on a
sequence of tasks. Albeit effective, prompt tuning methods do not lend well in
the multi-label class incremental learning (MLCIL) scenario (where an image
contains multiple foreground classes) due to the ambiguity in selecting the
correct prompt(s) corresponding to different foreground objects belonging to
multiple tasks. To circumvent this issue we propose to eliminate the prompt
selection mechanism by maintaining task-specific pathways, which allow us to
learn representations that do not interact with the ones from the other tasks.
Since independent pathways in truly incremental scenarios will result in an
explosion of computation due to the quadratically complex multi-head
self-attention (MSA) operation in prompt tuning, we propose to reduce the
original patch token embeddings into summarized tokens. Prompt tuning is then
applied to these fewer summarized tokens to compute the final representation.
Our proposed method Multi-Label class incremental learning via summarising
pAtch tokeN Embeddings (MULTI-LANE) enables learning disentangled task-specific
representations in MLCIL while ensuring fast inference. We conduct experiments
in common benchmarks and demonstrate that our MULTI-LANE achieves a new
state-of-the-art in MLCIL. Additionally, we show that MULTI-LANE is also
competitive in the CIL setting. Source code available at
this https URL

通过提出的多标签增量学习方法 MULTI-LANE，我们能够在不同前景对象属于多个任务时，通过维护任务特定路径来消除选择提示的机制，并通过总结的标记对这些减少后的标记应用提示调整以计算最终表示，这使得我们能够学习在多标签增量学习中解耦的任务特定表示，并确保快速推断。