Zero-Shot and Few-Shot Video Question Answering with Multi-Modal Prompts
Deniz Engin Yannis Avrithis
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.