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README.md

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+---
+license: cc-by-nc-sa-4.0
+extra_gated_prompt: >-
+  The LongVideoBench dataset contains links to web videos for data collection
+  purposes. LongVideoBench does not own the content linked within this dataset;
+  all rights and copyright belong to the respective channel owners. Ensuring
+  compliance with platform terms and conditions is the responsibility of these
+  source channels. By accessing this dataset, you acknowledge and agree to the
+  following terms:
+extra_gated_fields:
+  I understand that LongVideoBench does not own the videos in this dataset: checkbox
+  I understand that LongVideoBench is not the creator of the videos in this dataset: checkbox
+  I understand that, LongVideoBench may modify/delete its contents subject to the requirements of the creators or source platforms: checkbox
+  I agree to use this dataset for non-commercial use ONLY: checkbox
+  I agree with the data license (CC-BY-NC-SA 4-0) for this dataset: checkbox
+task_categories:
+- multiple-choice
+- visual-question-answering
+language:
+- en
+tags:
+- long video understanding
+- long context
+- multimodal
+- neurips 2024
+pretty_name: longvideobench
+---
+
+
+![](https://github.com/longvideobench/longvideobench.github.io/blob/main/logo.png?raw=true)
+
+
+# Dataset Card for LongVideoBench
+
+<!-- Provide a quick summary of the dataset. -->
+
+
+
+
+Large multimodal models (LMMs) are handling increasingly longer and more complex inputs. However, few public benchmarks are available to assess these advancements. To address this, we introduce LongVideoBench, a question-answering benchmark with video-language interleaved inputs up to an hour long. It comprises 3,763 web-collected videos with subtitles across diverse themes, designed to evaluate LMMs on long-term multimodal understanding.
+
+The main challenge that LongVideoBench targets is to accurately retrieve and reason over detailed information from lengthy inputs. We present a novel task called referring reasoning, where questions contain a referring query that references related video contexts, requiring the model to reason over these details.
+
+LongVideoBench includes 6,678 human-annotated multiple-choice questions across 17 categories, making it one of the most comprehensive benchmarks for long-form video understanding. Evaluations show significant challenges even for advanced proprietary models (e.g., GPT-4o, Gemini-1.5-Pro, GPT-4-Turbo), with open-source models performing worse. Performance improves only when models process more frames, establishing LongVideoBench as a valuable benchmark for future long-context LMMs.
+
+
+## Dataset Details
+
+### Dataset Description
+
+<!-- Provide a longer summary of what this dataset is. -->
+
+- **Curated by:** LongVideoBench Team
+- **Language(s) (NLP):** English
+- **License:** CC-BY-NC-SA 4.0
+
+### Dataset Sources [optional]
+
+<!-- Provide the basic links for the dataset. -->
+
+- **Repository:** [https://github.com/longvideobench/LongVideoBench](https://github.com/longvideobench/LongVideoBench)
+- **Homepage:** [https://longvideobench.github.io](https://longvideobench.github.io)
+- **Leaderboard:** [https://huggingface.co/spaces/longvideobench/LongVideoBench](https://huggingface.co/spaces/longvideobench/LongVideoBench)
+
+## Leaderboard (until Oct. 14, 2024)
+
+We rank models by Test Total Performance. 
+
+| Model | Test Total (5341) | Test 8s-15s | Test 15s-60s | Test 180s-600s | Test 900s-3600s | Val Total (1337) |
+| --- | --- | --- | --- | --- | --- | --- |
+| [GPT-4o (0513) (256)](https://platform.openai.com/docs/models/gpt-4o) | 66.7 | 71.6 | 76.8 | 66.7 | 61.6 | 66.7 |
+| [Aria (256)](https://huggingface.co/rhymes-ai/Aria) | 65.0 | 69.4 | 76.6 | 64.6 | 60.1 | 64.2 |
+| [LLaVA-Video-72B-Qwen2 (128)](https://huggingface.co/lmms-lab/LLaVA-Video-72B-Qwen2) | 64.9 | 72.4 | 77.4 | 63.9 | 59.3 | 63.9 |
+| [Gemini-1.5-Pro (0514) (256)](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/gemini-1.5-pro-001) | 64.4 | 70.2 | 75.3 | 65.0 | 59.1 | 64.0 |
+| [LLaVA-OneVision-QWen2-72B-OV (32)](https://huggingface.co/lmms-lab/llava-onevision-qwen2-72b-ov) | 63.2 | 74.3 | 77.4 | 61.6 | 56.5 | 61.3 |
+| [LLaVA-Video-7B-Qwen2 (128)](https://huggingface.co/lmms-lab/LLaVA-Video-7B-Qwen2) | 62.7 | 69.7 | 76.5 | 62.1 | 56.6 | 61.1 |
+| [Gemini-1.5-Flash (0514) (256)](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/gemini-1.5-flash-001) | 62.4 | 66.1 | 73.1 | 63.1 | 57.3 | 61.6 |
+| [GPT-4-Turbo (0409) (256)](https://platform.openai.com/docs/models/gpt-4-turbo-and-gpt-4) | 60.7 | 66.4 | 71.1 | 61.7 | 54.5 | 59.1 |
+| [InternVL2-40B (16)](https://huggingface.co/OpenGVLab/InternVL2-40B) | 60.6 | 71.4 | 76.6 | 57.5 | 54.4 | 59.3 |
+| [GPT-4o-mini (250)](https://platform.openai.com/docs/models/gpt-4o-mini) | 58.8 | 66.6 | 73.4 | 56.9 | 53.4 | 56.5 |
+| [MiniCPM-V-2.6 (64)](https://huggingface.co/openbmb/MiniCPM-V-2_6) | 57.7 | 62.5 | 69.1 | 54.9 | 49.8 | 54.9 |
+| [Qwen2-VL-7B (256)](https://huggingface.co/openbmb/MiniCPM-V-2_6) | 56.8 | 60.1 | 67.6 | 56.7 | 52.5 | 55.6 |
+| [Kangaroo (64)](https://huggingface.co/KangarooGroup/kangaroo) | 54.8 | 65.6 | 65.7 | 52.7 | 49.1 | 54.2 |
+| [PLLaVA-34B (32)](https://github.com/magic-research/PLLaVA) | 53.5 | 60.1 | 66.8 | 50.8 | 49.1 | 53.2 |
+| [InternVL-Chat-V1-5-26B (16)](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-5) | 51.7 | 61.3 | 62.7 | 49.5 | 46.6 | 51.2 |
+| [LLaVA-Next-Video-34B (32)](https://llava-vl.github.io/blog/2024-04-30-llava-next-video/) | 50.5 | 57.6 | 61.6 | 48.7 | 45.9 | 50.5 |
+| [Phi-3-Vision-Instruct (16)](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct) | 49.9 | 58.3 | 59.6 | 48.4 | 45.1 | 49.6 |
+| [Idefics2 (16)](https://huggingface.co/HuggingFaceM4/idefics2-8b) | 49.4 | 57.4 | 60.4 | 47.3 | 44.7 | 49.7 |
+| [Mantis-Idefics2 (16)](https://huggingface.co/TIGER-Lab/Mantis-8B-Idefics2) | 47.6 | 56.1 | 61.4 | 44.6 | 42.5 | 47.0 |
+| [LLaVA-Next-Mistral-7B (8)](https://huggingface.co/llava-hf/llava-v1.6-mistral-7b-hf) | 47.1 | 53.4 | 57.2 | 46.9 | 42.1 | 49.1 |
+| [PLLaVA-13B (32)](https://github.com/magic-research/PLLaVA) | 45.1 | 52.9 | 54.3 | 42.9 | 41.2 | 45.6 |
+| [InstructBLIP-T5-XXL (8)](https://github.com/salesforce/LAVIS/tree/main/projects/instructblip) | 43.8 | 48.1 | 50.1 | 44.5 | 40.0 | 43.3 |
+| [Mantis-BakLLaVA (16)](https://huggingface.co/TIGER-Lab/Mantis-bakllava-7b) | 43.7 | 51.3 | 52.7 | 41.1 | 40.1 | 43.7 |
+| [BLIP-2-T5-XXL (8)](https://github.com/salesforce/LAVIS/tree/main/projects/blip2) | 43.5 | 46.7 | 47.4 | 44.2 | 40.9 | 42.7 |
+| [LLaVA-Next-Video-M7B (32)](https://llava-vl.github.io/blog/2024-04-30-llava-next-video/) | 43.5 | 50.9 | 53.1 | 42.6 | 38.9 | 43.5 |
+| [LLaVA-1.5-13B (8)](https://huggingface.co/llava-hf/llava-1.5-13b-hf) | 43.1 | 49.0 | 51.1 | 41.8 | 39.6 | 43.4 |
+| [ShareGPT4Video (16)](https://github.com/InternLM/InternLM-XComposer/tree/main/projects/ShareGPT4Video) | 41.8 | 46.9 | 50.1 | 40.0 | 38.7 | 39.7 |
+| [VideoChat2 (Mistral-7B) (16)](https://github.com/OpenGVLab/Ask-Anything/tree/main/video_chat2) | 41.2 | 49.3 | 49.3 | 39.0 | 37.5 | 39.3 |
+| [LLaVA-1.5-7B (8)](https://huggingface.co/llava-hf/llava-1.5-7b-hf) | 40.4 | 45.0 | 47.4 | 40.1 | 37.0 | 40.3 |
+| [mPLUG-Owl2 (8)](https://github.com/X-PLUG/mPLUG-Owl/tree/main/mPLUG-Owl2) | 39.4 | 49.4 | 47.3 | 38.7 | 34.3 | 39.1 |
+| [PLLaVA-7B (32)](https://github.com/magic-research/PLLaVA) | 39.2 | 45.3 | 47.3 | 38.5 | 35.2 | 40.2 |
+| [VideoLLaVA (8)](https://github.com/PKU-YuanGroup/Video-LLaVA/) | 37.6 | 43.1 | 44.6 | 36.4 | 34.4 | 39.1 |
+| [VideoChat2 (Vicuna 7B) (16)](https://github.com/OpenGVLab/Ask-Anything/tree/main/video_chat2) | 35.1 | 38.1 | 40.5 | 33.5 | 33.6 | 36.0 |
+
+
+## Uses
+
+<!-- Address questions around how the dataset is intended to be used. -->
+
+1. Download the dataset via Hugging Face Client:
+
+```shell
+huggingface-cli download longvideobench/LongVideoBench --repo-type dataset --local-dir LongVideoBench --local-dir-use-symlinks False
+```
+
+2. Extract from the `.tar` files:
+
+```shell
+cat videos.tar.part.* > videos.tar
+tar -xvf videos.tar
+tar -xvf subtitles.tar
+```
+
+3. Use the [LongVideoBench] dataloader to load the data from raw MP4 files and subtitles:
+
+- (a) Install the dataloader:
+
+```shell
+git clone https://github.com/LongVideoBench/LongVideoBench.git
+cd LongVideoBench
+pip install -e .
+```
+- (b) Load the dataset in python scripts:
+
+```python
+from longvideobench import LongVideoBenchDataset
+
+# validation
+dataset = LongVideoBenchDataset(YOUR_DATA_PATH, "lvb_val.json", max_num_frames=64)
+
+# test
+dataset = LongVideoBenchDataset(YOUR_DATA_PATH, "lvb_test_wo_gt.json", max_num_frames=64)
+
+print(dataset[0]["inputs"]) # A list consisting of PIL.Image and strings.
+```
+
+The "inputs" are interleaved video frames and text subtitles, followed by questions and option prompts. You can then convert them to the format that your LMMs can accept.
+
+
+### Direct Use
+
+<!-- This section describes suitable use cases for the dataset. -->
+
+This dataset is meant to evaluate LMMs on video understanding and long-context understanding abilities.
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
+
+We do not advise to use this dataset for training.
+
+## Dataset Structure
+
+<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
+
+- `lvb_val.json`: Validation set annotations.
+
+- `lvb_test_wo_gt.json`: Test set annotations. Correct choice is not provided.
+
+- `videos.tar.*`: Links to Videos.
+
+- `subtitles.tar`: Links to Subtitles.
+
+
+## Dataset Card Contact
+
+haoning001@e.ntu.edu.sg
+
+
+```
+@misc{wu2024longvideobenchbenchmarklongcontextinterleaved,
+      title={LongVideoBench: A Benchmark for Long-context Interleaved Video-Language Understanding}, 
+      author={Haoning Wu and Dongxu Li and Bei Chen and Junnan Li},
+      year={2024},
+      eprint={2407.15754},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2407.15754}, 
+}
+```

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