VideoAdviser: Video Knowledge Distillation for Multimodal Transfer Learning

TL;DR

VideoAdviser introduces a knowledge distillation approach that transfers multimodal video knowledge from a powerful teacher model to a simpler student model, improving efficiency and performance in multimodal tasks like sentiment analysis and retrieval.

Contribution

The paper proposes a novel video knowledge distillation method that enables efficient multimodal transfer learning by distilling knowledge from a CLIP-based teacher to a RoBERTa-based student model.

Findings

Up to 12.3% MAE improvement in sentiment analysis

3.4% mAP increase in audio-visual retrieval

Enhanced state-of-the-art performance without extra inference costs

Abstract

Multimodal transfer learning aims to transform pretrained representations of diverse modalities into a common domain space for effective multimodal fusion. However, conventional systems are typically built on the assumption that all modalities exist, and the lack of modalities always leads to poor inference performance. Furthermore, extracting pretrained embeddings for all modalities is computationally inefficient for inference. In this work, to achieve high efficiency-performance multimodal transfer learning, we propose VideoAdviser, a video knowledge distillation method to transfer multimodal knowledge of video-enhanced prompts from a multimodal fundamental model (teacher) to a specific modal fundamental model (student). With an intuition that the best learning performance comes with professional advisers and smart students, we use a CLIP-based teacher model to provide expressive multimodal knowledge supervision signals to a RoBERTa-based student model via optimizing a step-distillation objective loss -- first step: the teacher distills multimodal knowledge of video-enhanced prompts from classification logits to a regression logit -- second step: the multimodal knowledge is distilled from the regression logit of the teacher to the student. We evaluate our method in two challenging multimodal tasks: video-level sentiment analysis (MOSI and MOSEI datasets) and audio-visual retrieval (VEGAS dataset). The student (requiring only the text modality as input) achieves an MAE score improvement of up to 12.3% for MOSI and MOSEI. Our method further enhances the state-of-the-art method by 3.4% mAP score for VEGAS without additional computations for inference. These results suggest the strengths of our method for achieving high efficiency-performance multimodal transfer learning.