Zero-Shot and Few-Shot Video Question Answering with Multi-Modal Prompts

Recent vision-language models are driven by large-scale pretrained models. However, adapting pretrained models on limited data presents challenges such as overfitting, catastrophic forgetting, and the cross-modal gap between vision and language. We introduce a parameter-efficient method to address these challenges, combining multimodal prompt learning and a transformer-based mapping network, while keeping the pretrained models frozen. Our experiments on several video question answering benchmarks demonstrate the superiority of our approach in terms of performance and parameter efficiency on both zero-shot and few-shot settings. Our code is available at https://engindeniz.github.io/vitis.

1. Introduction

Recent vision-language models have shown remarkable progress, driven by transformer-based large-scale pre-trained models [10], [39], [9], [38], [17], [45], [44]. These models have been incorporated into video understanding methods, including video question answering (VideoQA), through multimodal fusion on large-scale multimodal datasets [41], [3], [60]. However, adapting pretrained models to video-language tasks on limited data is challenging. This is because of the gap between the visual and language modalities and, more importantly, because finetuning the entire model on limited data can lead to overfitting and forgetting previously acquired knowledge.