Kernkonzepte
Utilizing Image Grids can enhance Video Question Answering with VLMs.
Zusammenfassung
This study introduces the concept of Image Grid Vision Language Model (IG-VLM) for video question answering. It compares the effectiveness of Image Grids with traditional methods across various benchmarks. The research highlights the benefits of using Image Grids to convey spatial and temporal information efficiently.
Introduction
Large Language Models (LLMs) have revolutionized reasoning capabilities.
Vision Language Models (VLMs) bridge visual data with LLMs for effective reasoning.
This study focuses on zero-shot Video Question Answering (VQA) using VLMs.
Method
IG-VLM converts videos into image grids for VLM processing.
The image grid format retains temporal information within a single image.
Prompts include grid guidance and reasoning guidance for VQA tasks.
Experiments
Extensive analysis across ten zero-shot VQA benchmarks.
IG-VLM outperforms existing methods in nine out of ten benchmarks.
Results show the effectiveness of Image Grids in enhancing VQA performance.
Related Works
Comparison of VideoLM and multi-stage foundation models for video modality bridging.
Various strategies explored for integrating video content into LLMs.
Analysis and Ablations Studies
Design analysis of image grids, including shape, ordering, and number of frames.
Ablation studies show the advantage of using Image Grids over single frames.
Prompt design impacts VQA performance, with reasoning guidance enhancing results.
Statistiken
Our code is are available at: https://github.com/imagegridworth/IG-VLM
Performance was normalized to a maximum score of 100.
Zitate
"Our straightforward approach outperforms the existing state-of-the-art methods in nine out of ten benchmarks." - Wonkyun Kim et al.