metadata

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)

# 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

from datasets import load_dataset

ds_name = "youcook2"  # change the dataset name here
dataset = load_dataset("ShuhuaiRen/TimeIT", ds_name)

Data Splits

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

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

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
	`textcap` [2]	Source
	`image-paragraph-captioning` [3]	Source
Classification	`coco-goi` [1]	Source
	`coco-text` [4]	Source
	`imagenet` [5]	Source
	`coco-itm` [1]	Source
	`snli-ve` [6]	Source
	`mocheg` [7]	Source
	`iqa` [8]	Source
Visual Question Answering	`vqa-v2` [9]	Source
	`shapes` [10]	Source
	`docvqa` [11]	Source
	`ocr-vqa` [12]	Source
	`st-vqa` [13]	Source
	`text-vqa` [14]	Source
	`gqa` [15]	Source
Knowledgeable Visual QA	`okvqa` [16]	Source
	`a-okvqa` [17]	Source
	`science-qa` [18]	Source
	`viquae` [19]	Source
Reasoning	`clevr` [20]	Source
	`nlvr` [21]	Source
	`vcr` [22]	Source
	`visual-mrc` [23]	Source
	`winoground` [24]	Source
Generation	`vist` [25]	Source
	`visual-dialog` [26]	Source
	`multi30k` [27]	Source
Chinese	`fm-iqa` [28]	Source
	`coco-cn` [29]	Source
	`flickr8k-cn` [30]	Source
	`chinese-food` [31]	Source
	`mmchat` [32]	Source
Video	`ss` [33]	Source
	`ivqa` [34]	Source
	`msvd-qa` [35]	Source
	`activitynet-qa` [36]	Source
	`msrvtt` [35]	Source
	`msrvtt-qa` [37]	Source

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.

Citation Information

@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