Hugging Face's logo

Datasets:
ShuhuaiRen
/
TimeIT

Languages:
English
ArXiv:
License:
Dataset card Files
xet
Community
TimeIT / README.md
ShuhuaiRen's picture
ShuhuaiRen
Upload README.md
63566b7
preview code
|
raw
history blame
15.9 kB
 ---
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