# Zero-Shot and Few-Shot Video Question Answering with Multi-Modal Prompts Source: [Zero-Shot and Few-Shot Video Question Answering with Multi-Modal Prompts]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:48:53.779Z Authors: Deniz Engin, Yannis Avrithis Published: 2023-09-27T18:00:09.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()