--- license: mit --- # 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 | | | Temporal Video Grounding | | | Video Summarization | | | Video Highlight Detection | | | Step Localization | | | Transcribed Speech Generation | | | Total | | ### Task Statistics | Task | Description | #Train | #Val | #Test | |-------------------------------|----------------------------------------------------------------------------------------------------------------------|---------|---------|---------| | Dense Video Captioning | detects a series of events in the given video and outputs the corresponding timestamps and descriptions | | Temporal Video Grounding | predict a timestamp boundary including the start and end time in the video given a natural language query | | Video Summarization | create a compressed set of frames or clip shots to represent the most informative content of the given video | | Video Highlight Detection | identify the most exciting, impressive, or emotional moments that may not cover the full scope of the original video | | Step Localization | segment and describe significant steps in a long untrimmed video | | Transcribed Speech Generation | predict the speech content and its corresponding start and end timestamps based on visual signals in the video | | Total | - | ### Detailed Dataset Statistics | Task | Dataset | #Train | #Val | #Test | |-------------------------------|------------------------|---------|--------|-------| | Dense Video Captioning | `ActivityNet Captions` | | | | | | `ViTT` | 97,765 | 13,965 | 0 | | | `YouCook2` | 14,575 | 2,487 | 2,489 | | Temporal Video Grounding | `DiDeMo` | 30,000 | 2,000 | 0 | | | `QuerYD` | 118,312 | 27,550 | 0 | | | `HiREST_grounding` | 30,000 | 50,000 | 0 | | | `Charades-STA` | 30,000 | 5,000 | 5,000 | | Video Summarization | `TVSum` | 30,000 | 30,000 | 0 | | | `SumMe` | 13,568 | 1,024 | 1,024 | | Video Highlight Detection | `QVHighlights` | 9,009 | 5,046 | 0 | | Step Localization | `COIN` | 30,000 | 2,000 | 0 | | | `HiREST_step` | 29,372 | 2,000 | 0 | | Transcribed Speech Generation | `YT-Temporal` | 5,000 | 4,315 | 4,350 | ## 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["QA"][0]['q'] # str answer = train_instance["QA"][0]['a'] # str video_path = train_instance["video"] # str ``` ### Data Fields ```python import datasets features = datasets.Features( { "instruction": datasets.Value("string"), "inputs": datasets.Value("string"), "image_base64_str": [datasets.Value("string")], "outputs": datasets.Value("string"), } ) ``` ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data | Task | Dataset [Citation] | Source | |---------------------------|----------------------------------|------------------------------------------------------------------------------------| | Image Captioning | `coco` [1] | [Source](https://cocodataset.org/#home) | | | `textcap` [2] | [Source](https://textvqa.org/textcaps/) | | | `image-paragraph-captioning` [3] | [Source](https://cs.stanford.edu/people/ranjaykrishna/im2p/index.html) | | Classification | `coco-goi` [1] | [Source](https://cocodataset.org/#home) | | | `coco-text` [4] | [Source](https://bgshih.github.io/cocotext/) | | | `imagenet` [5] | [Source](https://www.image-net.org/) | | | `coco-itm` [1] | [Source](https://cocodataset.org/#home) | | | `snli-ve` [6] | [Source](https://github.com/necla-ml/SNLI-VE) | | | `mocheg` [7] | [Source](https://github.com/VT-NLP/Mocheg) | | | `iqa` [8] | [Source](https://github.com/icbcbicc/IQA-Dataset) | | Visual Question Answering | `vqa-v2` [9] | [Source](https://visualqa.org/) | | | `shapes` [10] | [Source](https://github.com/ronghanghu/n2nmn) | | | `docvqa` [11] | [Source](https://www.docvqa.org/) | | | `ocr-vqa` [12] | [Source](https://ocr-vqa.github.io/) | | | `st-vqa` [13] | [Source](https://rrc.cvc.uab.es/?ch=11) | | | `text-vqa` [14] | [Source](https://textvqa.org/) | | | `gqa` [15] | [Source](https://cs.stanford.edu/people/dorarad/gqa/about.html) | | Knowledgeable Visual QA | `okvqa` [16] | [Source](https://okvqa.allenai.org/) | | | `a-okvqa` [17] | [Source](https://allenai.org/project/a-okvqa/home) | | | `science-qa` [18] | [Source](https://scienceqa.github.io/) | | | `viquae` [19] | [Source](https://github.com/PaulLerner/ViQuAE) | | Reasoning | `clevr` [20] | [Source](https://cs.stanford.edu/people/jcjohns/clevr/) | | | `nlvr` [21] | [Source](https://lil.nlp.cornell.edu/nlvr/) | | | `vcr` [22] | [Source](https://visualcommonsense.com/) | | | `visual-mrc` [23] | [Source](https://github.com/nttmdlab-nlp/VisualMRC) | | | `winoground` [24] | [Source](https://huggingface.co/datasets/facebook/winoground) | | Generation | `vist` [25] | [Source](https://visionandlanguage.net/VIST/) | | | `visual-dialog` [26] | [Source](https://visualdialog.org/) | | | `multi30k` [27] | [Source](https://github.com/multi30k/dataset) | | Chinese | `fm-iqa` [28] | [Source](https://paperswithcode.com/dataset/fm-iqa) | | | `coco-cn` [29] | [Source](https://github.com/li-xirong/coco-cn) | | | `flickr8k-cn` [30] | [Source](https://github.com/li-xirong/flickr8kcn) | | | `chinese-food` [31] | [Source](https://sites.google.com/view/chinesefoodnet) | | | `mmchat` [32] | [Source](https://github.com/silverriver/MMChat) | | Video | `ss` [33] | [Source](https://developer.qualcomm.com/software/ai-datasets/something-something) | | | `ivqa` [34] | [Source](https://antoyang.github.io/just-ask.html) | | | `msvd-qa` [35] | [Source](https://paperswithcode.com/dataset/msvd) | | | `activitynet-qa` [36] | [Source](https://github.com/MILVLG/activitynet-qa) | | | `msrvtt` [35] | [Source](https://paperswithcode.com/dataset/msr-vtt) | | | `msrvtt-qa` [37] | [Source](https://paperswithcode.com/sota/visual-question-answering-on-msrvtt-qa-1) | ### 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] Microsoft COCO: Common Objects in Context - [2] TextCaps: a dataset for image captioning with reading comprehension - [3] A Hierarchical Approach for Generating Descriptive Image Paragraphs - [4] COCO-Text: Dataset and benchmark for text detection and recognition in natural images - [5] Imagenet large scale visual recognition challenge - [6] E-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language Tasks - [7] End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Models - [8] Quantifying visual image quality: A Bayesian view - [9] Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering - [10] Neural Module Networks - [11] DocVQA: A dataset for vqa on document images - [12] OCR-VQA: Visual Question Answering by Reading Text in Images - [13] Scene Text Visual Question Answering - [14] Towards VQA Models That Can Read - [15] GQA: A new dataset for real-world visual reasoning and compositional question answering - [16] OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge - [17] A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge - [18] Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering - [19] ViQuAE: a dataset for knowledge-based visual question answering about named entities - [20] CLEVR: A diagnostic dataset for compositional language and elementary visual reasoning - [21] A Corpus of Natural Language for Visual Reasoning - [22] From recognition to cognition: Visual Commonsense Reasoning - [23] VisualMRC: Machine reading comprehension on document images - [24] WinoGround: Probing vision and language models for visio-linguistic compositionality - [25] Visual Storytelling - [26] Visual Dialog - [27] Multi30k: Multilingual english-german image descriptions - [28] Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question - [29] COCO-CN for cross-lingual image tagging, captioning, and retrieval - [30] Adding Chinese Captions to Images - [31] ChineseFoodNet: A large-scale image dataset for chinese food recognition - [32] MMChat: Multi-Modal Chat Dataset on Social Media - [33] The "Something Something" Video Database for Learning and Evaluating Visual Common Sense - [34] Just Ask: Learning to answer questions from millions of narrated videos - [35] Video Question Answering via Gradually Refined Attention over Appearance and Motion - [36] ActivityNet-qa: A dataset for understanding complex web videos via question answering - [37] MSR-VTT: A large video description dataset for bridging video and language