Logo VideoNIAH

Needle In A Video Haystack: A Scalable Synthetic
Framework for Benchmarking Video MLLMs

Zijia Zhao1, Haoyu Lu2, Yuqi Huo3, Yifan Du2, Tongtian Yue1, Longteng Guo1, Bingning Wang3, Weipeng Chen3, Jing Liu1,
1CASIA, 2RUC, 3Baichuan
arXiv Code

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 Dataset

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 Leaderboard

Introduction

We propose VideoNIAH (Video Needle In A Haystack), a benchmark construction framework through synthetic video generation. VideoNIAH decouples test video content from their query-responses by inserting unrelated image/text 'needles' into original videos. It generates annotations solely from these needles, ensuring diversity in video sources and a variety of query-responses. Additionally, by inserting multiple needles, VideoNIAH rigorously evaluates the temporal understanding capabilities of models. VideoNIAH is a simple yet highly scalable benchmark construction framework, and we believe it will inspire future video benchmark works! We utilize VideoNIAH to compile a video benchmark VNBench, including tasks such as retrieval, ordering, and counting. VNBench contains 1350 samples in total. VNBench can efficiently evaluate the fine-grained understanding ability and spatio-temporal modeling ability of a video model, while also supporting the long-context evaluation.

Leaderboard

VNBench contains 3 task: Retrieval, Ordering and Counting. Each task is divided into 3 sub-tasks according to the needle type and task difficulty.

Video MLLMs Retrieval Ordering Counting Overall
E I-1 I-2 Avg. E I-1 I-2 Avg. E-1 E-2 I Avg.
Gemini 1.5 Pro * 100.0 96.0 76.0 90.7 90.7 95.3 32.7 72.9 60.7 7.3 42.0 36.7 66.7
Aria 100.0 100.0 49.3 83.1 88.7 96.0 58.0 80.9 54.7 11.3 38.7 34.9 66.3
GPT-4o * 100.0 98.0 87.3 95.3 88.4 86.6 45.2 73.4 36.8 0.0 36.1 24.5 64.4
Video-XL-7B 98.0 93.3 48.7 80.0 89.3 77.3 75.3 80.6 38.7 7.3 26.0 24.0 61.6
LongLLaVA-A13 100.0 100.0 73.3 91.1 37.5 35.3 34.8 35.9 36.0 23.7 28.0 29.2 52.1
LLaVA-OneVision-7B 88.7 87.3 55.3 77.1 70.0 50.0 37.3 52.4 41.3 8.7 27.3 25.8 51.8
GPT-4-Turbo * 100.0 99.3 82.0 93.7 42.6 22.8 23.0 29.5 37.6 0.0 32.4 23.3 48.9
Qwen2-VL-7B 98.0 76.0 33.3 69.1 16.0 12.7 8.7 12.4 26.0 9.3 24.7 20.0 33.9
ST-LLM 58.0 64.7 31.3 51.3 0.0 0.0 0.0 0.0 21.3 1.3 27.3 16.7 22.7
LLaVA-NeXT-Video 56.7 56.7 19.3 44.2 0.7 0.0 0.7 0.4 6.7 14.6 25.3 15.5 20.1
VideoChat2 43.4 40.0 14.6 32.7 0.0 0.0 1.3 0.4 3.3 0.7 8.0 4.0 12.4
Video-LLaVA 26.0 28.0 17.3 23.8 0.7 0.7 2.0 1.1 16.7 0.7 20.0 12.4 12.4
LLaMA-VID 28.0 28.0 19.3 25.1 0.7 0.0 0.0 0.2 4.0 2.7 14.7 7.1 10.8
Video-LLaMA2 1.2 26.0 6.0 11.1 0.0 0.0 0.0 0.0 2.0 4.7 0.7 2.4 4.5
VideoChatGPT 4.7 4.7 0.7 3.3 2.7 11.3 0.0 4.7 2.0 4.0 6.7 4.2 4.1

* indicates proprietary models

Benchmark

Data Examples

Analysis Results

Different Haystack Length

grade-lv

Task performance on different video durations. We divide all VNBench videos into 3 splits: short(10-30s), medium(30-60s) and long(60-180s).

NIAH Visualization on Different Models

We fix the video haystack and query-response pair in this position test on Retrieval-I-1 task, just modifying the haystack length and needle position.

Citation

@article{zhao2024videoniah,
      title={Needle In A Video Haystack: A Scalable  Synthetic Framework for Benchmarking Video MLLMs},
      author={Zhao, Zijia and Lu, Haoyu and Huo, Yuqi and Du, Yifan and Yue, Tongtian and Guo, Longteng and Wang, Bingning and Chen, Weipeng and Liu, Jing},
      journal={arXiv preprint},
      year={2024}
    }