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README.md

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+
+# 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