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An Image Grid Can Be Worth a Video: Zero-shot Video Question Answering Using a VLM

Wonkyun Kim, Changin Choi, Wonseok Lee, Wonjong Rhee

arXiv:2403.18406v1cs.CVcs.AIcs.CLcs.LG

TL;DR

Video integration into LLMs has used learned VideoLM interfaces or multi-stage foundation-model pipelines, with no definitive strategy identified. IG-VLM turns sampled frames into one image grid for a single VLM, and the paper reports it surpasses existing methods in nine of ten benchmarks; the approach’s stated limitation is that it discards most video frames.

  • Problem

    Existing video-to-LLM strategies have not produced a definitive solution, and the paper identifies limits in video-token capacity and in how fully text descriptions convey video information.

    Source: §1 · p. 3

  • Method

    IG-VLM arranges sampled video frames into one image grid and sends it with a prompt to a single VLM, without video-data training.

    Source: §1 · p. 3, §3 · p. 5

  • Results

    IG-VLM surpasses existing methods in nine of ten benchmarks: five of five open-ended and four of five multiple-choice benchmarks when an appropriate VLM is selected.

    Source: §1 · p. 3

  • Takeaways & Limitations

    The reported results show IG-VLM excelling in both open-ended and multiple-choice VQA benchmark categories.

    Source: §4.3 · p. 9

    The image grid discards most video frames, a limitation identified for future exploration.

    Source: §7 · p. 15

Abstract

from arXiv · show

Stimulated by the sophisticated reasoning capabilities of recent Large Language Models (LLMs), a variety of strategies for bridging video modality have been devised. A prominent strategy involves Video Language Models (VideoLMs), which train a learnable interface with video data to connect advanced vision encoders with LLMs. Recently, an alternative strategy has surfaced, employing readily available foundation models, such as VideoLMs and LLMs, across multiple stages for modality bridging. In this study, we introduce a simple yet novel strategy where only a single Vision Language Model (VLM) is utilized. Our starting point is the plain insight that a video comprises a series of images, or frames, interwoven with temporal information. The essence of video comprehension lies in adeptly managing the temporal aspects along with the spatial details of each frame. Initially, we transform a video into a single composite image by arranging multiple frames in a grid layout. The resulting single image is termed as an image grid. This format, while maintaining the appearance of a solitary image, effectively retains temporal information within the grid structure. Therefore, the image grid approach enables direct application of a single high-performance VLM without necessitating any video-data training. Our extensive experimental analysis across ten zero-shot video question answering benchmarks, including five open-ended and five multiple-choice benchmarks, reveals that the proposed Image Grid Vision Language Model (IG-VLM) surpasses the existing methods in nine out of ten benchmarks.

1 Introduction

Video integration into LLMs has no definitive solution: existing approaches either train a VideoLM interface or pass descriptions from a VideoLM to a reasoning LLM. IG-VLM instead uses one VLM with a video represented as a frame grid, and the paper reports it surpasses existing methods in nine of ten benchmarks.

1 Introduction

  • VLMs link visual data with LLM reasoning, while the paper focuses on integrating video for zero-shot Video Question Answering.The introduction frames this work in the broader use of LLMs across modalities.Source: §1 · p. 2
  • Existing strategies either train a learnable interface with video data or pass VideoLM-generated descriptions to a reasoning LLM, and neither is established as definitive.The paper notes limited video-data availability and restricted video-token capacity for VideoLMs, and says text descriptions may not fully convey video information.Source: §1 · pp. 2–3
  • IG-VLM arranges sampled video frames in one composite image for a single VLM, avoiding video-data training and a multi-stage model pipeline.The approach is reported to outperform existing methods in nine of ten benchmarks: all five open-ended benchmarks and four of five multiple-choice benchmarks, when an appropriate VLM is selected.Source: §1 · p. 3

2 Related Works

The related work describes two main strategies for connecting video to LLMs: VideoLMs that use learned interfaces and multi-stage systems that convert video into text or programs. These strategies use different mechanisms to provide video information for LLM reasoning.

2.1 VideoLM

  • VideoLMs convert video content into video tokens through learned interfaces, including projection networks, inter-modality attention, or modality perceivers.Prior approaches also use temporal modeling, token compression, or question-relevant keyframe selection to manage long videos.Source: §2.1 · p. 4

2.2 Multi-stage Foundation Models

  • Multi-stage systems use off-the-shelf VideoLMs to turn video into text for an LLM, or use LLM-generated programs to analyze video and answer questions.The described systems include video captioning and program-based analysis.Source: §2.2 · p. 5

3 Method

IG-VLM represents video as a grid of sampled frames and gives that image to a single VLM with prompts. Its prompts explain the grid’s frame sequence and, for open-ended questions, guide reasoning.

3 Method

  • IG-VLM feeds a video-derived image grid and a prompt to one VLM; reasoning guidance is used only for open-ended questions.The prompt includes grid guidance, reasoning guidance, and the question.Source: §3 · p. 5

3.1 Converting Video into Image Grid

  • Six uniformly sampled frames are arranged in a 3 × 2 grid, ordered left to right across rows, to form a single image.The video is divided into six intervals, with the first frame in each interval selected.Source: §3.1 · p. 5, Figure 2 · p. 6

3.2 Prompt for Image Grid

  • Grid guidance identifies the image as an ordered sequence of video frames; reasoning guidance directs how to answer open-ended questions using that grid.The question is the third prompt component, and reasoning guidance is not used for multiple-choice questions.Source: §3 · p. 5, §3.2 · p. 6

4 Experiments

Experiments span open-ended and multiple-choice zero-shot VQA benchmarks, using GPT-assisted assessment for open-ended answers and accuracy for multiple-choice answers. IG-VLM outperforms prior results across the reported open-ended benchmarks with one stated exception, and achieves the best result in four of five multiple-choice benchmarks when the VLM is chosen for the benchmark.

4.1 Datasets and Metrics

4.2 Open-ended VQA

  • IG-VLM’s Text Generation average score exceeds prior best for every VLM, though performance varies across the five categories.It consistently improves on prior best in Correctness of Information and Consistency, while it trails three recent models in Detail Orientation.Source: §4.2 · pp. 7–8, Table 2 · p. 8

4.2 Open-ended VQA; 4.3 Multiple-choice VQA

  • 4 of 5 multiple-choice benchmarks have the best result with IG-VLM when an appropriate VLM is selected; open-ended results exceed prior best except GPT-4V on TGIF-QA.The reported comparisons cover four open-ended benchmarks and five multiple-choice benchmarks.Source: §4.2 · p. 7, Table 1 · p. 8, Table 3 · p. 9

5 Analysis and Ablations Studies

The analysis compares image-grid shape, frame ordering, and frame count, then tests image-grid and prompt-design choices. Square grids and six-frame grids perform best in the reported design experiments, while image grids and reasoning guidance improve performance in their tested settings.

5.1 Design analysis of image grid

5.2 Ablation study – single frame vs. image grid

  • Image grids consistently improve performance over a single random frame in all tested scenarios.The ablation covers two multiple-choice benchmarks and a text-generation benchmark; gains vary with the VLM.Source: §5.2 · p. 11, Table 4 · p. 12, Table 5 · p. 12

5.3 Ablation study – prompt design

  • Reasoning guidance in the prompt improves performance across the tested open-ended and text-generation benchmarks.Grid guidance helps on ActivityNet, and larger models gain more when the prompt describes the grid configuration.Source: §5.3 · p. 12, Table 6 · p. 13

6 Discussion

The discussion finds that model size does not uniformly predict IG-VLM performance, considers spatial-temporal trade-offs, and reports that multi-step prompting sometimes improves accuracy.

6.1 Size of LLM and VQA performance

  • LLaVA v1.6 34B outperformed GPT-4V in three of five open-ended and three of five multiple-choice VQA benchmarks, despite their size difference.Scaling LLaVA v1.6 showed potential gains, but the pattern did not hold uniformly across benchmarks.Source: §6.1 · p. 13

6.2 Spatial-temporal trade-offs of IG-VLM

  • Image-grid preprocessing may reduce resolution, alter aspect ratios, or crop frames, losing spatial detail and potentially affecting temporal information.The number and shape of grid frames and the VLM vision encoder’s resolution can all influence IG-VLM performance.Source: §6.2 · pp. 13–14

6.3 Comparing Single-Step vs. Multi-Step Prompting

  • Multi-step prompts improved accuracy in some cases, while the authors identify integrating multi-step reasoning with grid and reasoning guidance as a potential enhancement.Grid guidance was applied across both single-step and multi-step experiments for a fair comparison.Source: §6.3 · p. 14

7 Conclusions

IG-VLM sends multiple video frames to one VLM as an image grid, conveying spatial and temporal information in a single image. The authors identify discarded frames as a drawback and an area for future work.

  • IG-VLM uses an image grid to transmit multiple video frames to a VLM, conveying spatial and temporal information in one image format.The approach was validated through experiments across ten video question answering benchmarks.Source: §7 · pp. 14–15
  • The image grid discards most video frames, a limitation the authors leave for future exploration.Source: §7 · p. 15

A Successful Case Examples of VQA by IG-VLM

The section presents successful IG-VLM responses from open-ended and multiple-choice VQA, including examples where models answer perfectly and where their performance differs.

B Failure Case Examples of VQA by IG-VLM

The section presents inaccurate IG-VLM responses across open-ended and multiple-choice VQA. It identifies ambiguity, the six-frame limit, and incorrect object recognition as failure reasons.

C Experiment Corresponding to the Discussion on Multi-step Prompts

Table 8 reports experiments on multi-step reasoning prompts with IG-VLM, comparing manual prompt efficiency across ActivityNet-QA and NExT-QA.

C Experiment Corresponding to the Discussion on Multi-step Prompts

D Multi-step Prompts Templates

The appendix presents the multi-step prompt templates used in IG-VLM experiments, covering four named prompting methods.

D Multi-step Prompts Templates

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