Dynamic Prompt Adjustment for Multi-Label Class-Incremental Learning
This addresses a practical but understudied challenge in incremental learning for multi-label classification, though it appears incremental in approach.
The paper tackled the problem of catastrophic forgetting in multi-label class-incremental learning using visual language models like CLIP, achieving substantial performance improvements on benchmark datasets.
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.