Summary of An Image Grid Can Be Worth a Video: Zero-shot Video Question Answering Using a Vlm, by Wonkyun Kim et al.
An Image Grid Can Be Worth a Video: Zero-shot Video Question Answering Using a VLM
by Wonkyun Kim, Changin Choi, Wonseok Lee, Wonjong Rhee
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: Recent advancements in Large Language Models (LLMs) have led to various strategies for bridging video modality. One approach involves Video Language Models (VideoLMs), which train a learnable interface with video data to connect advanced vision encoders with LLMs. Another strategy employs foundation models like VideoLMs and LLMs across multiple stages for modality bridging. This study proposes a novel strategy utilizing a single Vision Language Model (VLM). The key insight is that a video comprises a series of images, or frames, interwoven with temporal information. By transforming the video into an image grid, maintaining the appearance of a solitary image while retaining temporal information within the grid structure, the proposed Image Grid Vision Language Model (IG-VLM) can be applied directly without requiring any video-data training. Experimental results on ten zero-shot video question answering benchmarks reveal that IG-VLM surpasses existing methods in nine out of ten benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This study explores ways to understand videos better. Researchers found a new way to do this using just one special kind of computer program called a Vision Language Model (VLM). The key idea is that a video is like a series of still images, with information about what happens in each image and how they’re connected over time. By combining these images into a single picture, the VLM can be used to understand videos without needing any special training on videos. This new method was tested on 10 different challenges and performed better than other methods in 9 out of 10 cases. |
Keywords
* Artificial intelligence * Language model * Question answering * Zero shot
