--- license: cc-by-4.0 language: - en --- # Dataset Card for TimeIT TimeIT encompasses 6 longstanding timestamp-related video tasks and incorporates 12 specific datasets derived from different domains. ## Dataset Description - **Homepage: https://huggingface.co/datasets/ShuhuaiRen/TimeIT** - **Repository: https://huggingface.co/datasets/ShuhuaiRen/TimeIT** - **Paper: https://arxiv.org/abs/2312.02051** - **Leaderboard:** - **Point of Contact:** ## Dataset Statistics Our dataset compiles diverse tasks of time-sensitive long video understanding, including Dense Video Captioning, Video Grounding, Video Summarization, Video Highlight Detection, Step Localization, Transcribed Speech Generation. ### Instruction Statistics | Task | #Instructions | |-------------------------------|---------------| | Dense Video Captioning | 6 | | Temporal Video Grounding | 6 | | Video Summarization | 6 | | Video Highlight Detection | 6 | | Step Localization | 6 | | Transcribed Speech Generation | 6 | | Total | 36 | ### Task Statistics | Task | Description | #Train | |-------------------------------|----------------------------------------------------------------------------------------------------------------------|---------| | Dense Video Captioning | detects a series of events in the given video and outputs the corresponding timestamps and descriptions | 16,342 | | Temporal Video Grounding | predict a timestamp boundary including the start and end time in the video given a natural language query | 60,471 | | Video Summarization | create a compressed set of frames or clip shots to represent the most informative content of the given video | 75 | | Video Highlight Detection | identify the most exciting, impressive, or emotional moments that may not cover the full scope of the original video | 6,858 | | Step Localization | segment and describe significant steps in a long untrimmed video | 9,488 | | Transcribed Speech Generation | predict the speech content and its corresponding start and end timestamps based on visual signals in the video | 31,627 | | Total | - | 124861 | ### Detailed Dataset Statistics | Task | Dataset | #Train | |-------------------------------|------------------------|--------| | Dense Video Captioning | `ActivityNet Captions` | 10,009 | | | `ViTT` | 5,141 | | | `YouCook2` | 1,192 | | Temporal Video Grounding | `DiDeMo` | 33,002 | | | `QuerYD` | 14,602 | | | `HiREST_grounding` | 459 | | | `Charades-STA` | 12,408 | | Video Summarization | `TVSum` | 50 | | | `SumMe` | 25 | | Video Highlight Detection | `QVHighlights` | 6,858 | | Step Localization | `COIN` | 9,029 | | | `HiREST_step` | 459 | | Transcribed Speech Generation | `YT-Temporal` | 31,627 | ## Dataset Structure ### HuggingFace Login (Optional) ```python # OR run huggingface-cli login from huggingface_hub import login hf_token = "hf_xxx" # TODO: set a valid HuggingFace access token for loading datasets/models login(token=hf_token) ``` ### Data Loading ```python from datasets import load_dataset ds_name = "youcook2" # change the dataset name here dataset = load_dataset("ShuhuaiRen/TimeIT", ds_name) ``` ### Data Splits ```python from datasets import load_dataset ds_name = "youcook2" # change the dataset name here dataset = load_dataset("ShuhuaiRen/TimeIT", ds_name) train_set = dataset["train"] ``` ### Data Instances ```python from datasets import load_dataset from io import BytesIO from base64 import b64decode from PIL import Image ds_name = "youcook2" # change the dataset name here dataset = load_dataset("ShuhuaiRen/TimeIT", ds_name) train_set = dataset["train"] for train_instance in train_set: question = train_instance["question"] # str answer = train_instance["answer"] # str video_path = train_instance["video_path"] # str ``` ### Data Fields ```python import datasets features = datasets.Features( { "video_path": datasets.Value("string"), "question": datasets.Value("string"), "answer": datasets.Value("string"), } ) ``` ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data | Task | Dataset [Citation] | Source | |-------------------------------|----------------------------|--------------------------------------------------------------------------------| | Dense Video Captioning | `ActivityNet Captions` [1] | [Source](https://cs.stanford.edu/people/ranjaykrishna/densevid/) | | | `ViTT` [2] | [Source](https://github.com/google-research-datasets/Video-Timeline-Tags-ViTT) | | | `YouCook2` [3] | [Source](http://youcook2.eecs.umich.edu/) | | Temporal Video Grounding | `DiDeMo` [4] | [Source](https://github.com/LisaAnne/TemporalLanguageRelease) | | | `QuerYD` [5] | [Source](https://www.robots.ox.ac.uk/~vgg/data/queryd/) | | | `HiREST_grounding` [6] | [Source](https://hirest-cvpr2023.github.io/) | | | `Charades-STA` [7] | [Source](https://github.com/jiyanggao/TALL) | | Video Summarization | `TVSum` [8] | [Source](https://github.com/yalesong/tvsum) | | | `SumMe` [9] | [Source](http://classif.ai/dataset/ethz-cvl-video-summe/) | | Video Highlight Detection | `QVHighlights` [10] | [Source](https://github.com/jayleicn/moment_detr/tree/main/data) | | Step Localization | `COIN` [11] | [Source](https://coin-dataset.github.io/) | | | `HiREST_step` [6] | [Source](https://hirest-cvpr2023.github.io/) | | Transcribed Speech Generation | `YT-Temporal` [12] | [Source](https://rowanzellers.com/merlot/#data) | ### Annotations #### Annotation process To build high-quality multimodal instruction datasets, we rewrite various datasets into multimodal-to-text dialog format. The annotation process includes four steps: - (1) **Stage I: Instruction Writing**: writing instructions for each task; - (2) **Stage II: Data Format Unification**: structuring images and texts into a unified schema; - (3) **Stage III: Quality Check**: checking the overall dataset quality; - (4) **Stage IV: Key Datasets Translation**: building multilingual sets. #### Who are the annotators? Three authors of this work are employed as human annotators, each of whom is a graduate student familiar with relevant literature. ## Additional Information ### Licensing Information The content of original dataset follows their original license. We suggest that for the task with Unknown/Custom license, the user can check the original project or contact the dataset owner for detailed license information. Our annotated instruction data is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). ### Citation Information ```bibtex @article{Ren2023TimeChatAT, title={TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding}, author={Shuhuai Ren and Linli Yao and Shicheng Li and Xu Sun and Lu Hou}, journal={ArXiv}, year={2023}, volume={abs/2312.02051}, } ``` ### Contributions TimeIT is a video-centric instruction-tuning dataset involving timestamps. designed to enable the development of general-purpose video agents. ## References - [1] Dense-Captioning Events in Videos - [2] Multimodal Pretraining for Dense Video Captioning - [3] Towards Automatic Learning of Procedures from Web Instructional Videos - [4] Localizing Moments in Video with Natural Language - [5] QuerYD: A video dataset with high-quality text and audio narrations - [6] Hierarchical Video-Moment Retrieval and Step-Captioning - [7] TALL: Temporal Activity Localization via Language Query - [8] TVSum: Summarizing Web Videos Using Titles - [9] Creating Summaries from User Videos - [10] QVHighlights: Detecting Moments and Highlights in Videos via Natural Language Queries - [11] COIN: A Large-scale Dataset for Comprehensive Instructional Video Analysis - [12] MERLOT: Multimodal Neural Script Knowledge Models