TAIL: Text-Audio Incremental Learning
This addresses incremental learning for text-audio retrieval, an incremental improvement for multi-modal AI applications.
The paper tackles catastrophic forgetting in text-audio retrieval when introducing new datasets by proposing the TAIL task and PTAT method, which uses prompt tuning and distillation to reduce forgetting while requiring only 2.42% of parameters compared to full fine-tuning and achieving 4.46% higher performance.
Many studies combine text and audio to capture multi-modal information but they overlook the model's generalization ability on new datasets. Introducing new datasets may affect the feature space of the original dataset, leading to catastrophic forgetting. Meanwhile, large model parameters can significantly impact training performance. To address these limitations, we introduce a novel task called Text-Audio Incremental Learning (TAIL) task for text-audio retrieval, and propose a new method, PTAT, Prompt Tuning for Audio-Text incremental learning. This method utilizes prompt tuning to optimize the model parameters while incorporating an audio-text similarity and feature distillation module to effectively mitigate catastrophic forgetting. We benchmark our method and previous incremental learning methods on AudioCaps, Clotho, BBC Sound Effects and Audioset datasets, and our method outperforms previous methods significantly, particularly demonstrating stronger resistance to forgetting on older datasets. Compared to the full-parameters Finetune (Sequential) method, our model only requires 2.42\% of its parameters, achieving 4.46\% higher performance.