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

Published in

(2023)

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TL;DR

A parameter-efficient method is introduced, combining multimodal prompt learning and a transformer-based mapping network, while keeping the pretrained models frozen to address challenges such as overfitting, catastrophic forgetting, and the cross-modal gap between vision and language.

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.

Authors

Yannis Avrithis

4 papers

Deniz Engin

2 papers

References64 items

Vita-CLIP: Video and text adaptive CLIP via Multimodal Prompting

AutoAD: Movie Description in Context

Multimodal Prompting with Missing Modalities for Visual Recognition

VindLU: A Recipe for Effective Video-and-Language Pretraining

Fine-tuned CLIP Models are Efficient Video Learners

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

Published in

(2023)

External Links:

Generate Graph DownloadPDF

TL;DR

Abstract

Authors

Yannis Avrithis

4 papers

Deniz Engin

2 papers

References64 items

Vita-CLIP: Video and text adaptive CLIP via Multimodal Prompting

AutoAD: Movie Description in Context

Multimodal Prompting with Missing Modalities for Visual Recognition

VindLU: A Recipe for Effective Video-and-Language Pretraining

Fine-tuned CLIP Models are Efficient Video Learners

MaPLe: Multi-modal Prompt Learning

Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models

An Empirical Study of End-to-End Video-Language Transformers with Masked Visual Modeling

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Field of Study

Computer Science

Journal Information

Name

2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)

Page

3202-3211