Dynamic Prompt Adjustment for Multi-Label Class-Incremental Learning

TL;DR

This paper proposes a novel method for multi-label class-incremental learning using enhanced prompts, data replay, and prompt loss, effectively reducing catastrophic forgetting and improving performance on benchmark datasets.

Contribution

It introduces an integrated approach combining improved prompt adaptation, confidence-based sample replay, and prompt loss to address multi-label class-incremental learning challenges.

Findings

Significant performance improvements on multiple benchmarks.

Effective reduction of catastrophic forgetting.

Enhanced prompt information benefits multi-label classification.

Abstract

Significant advancements have been made in single label incremental learning (SLCIL),yet the more practical and challenging multi label class incremental learning (MLCIL) remains understudied. Recently,visual language models such as CLIP have achieved good results in classification tasks. However,directly using CLIP to solve MLCIL issue can lead to catastrophic forgetting. To tackle this issue, we integrate an improved data replay mechanism and prompt loss to curb knowledge forgetting. Specifically,our model enhances the prompt information to better adapt to multi-label classification tasks and employs confidence-based replay strategy to select representative samples. Moreover, the prompt loss significantly reduces the model's forgetting of previous knowledge. Experimental results demonstrate that our method has substantially improved the performance of MLCIL tasks across multiple benchmark datasets,validating its effectiveness.