Dataset Card for TimeIT

TimeIT encompasses 6 longstanding timestamp-related video tasks and incorporates 12 specific datasets derived from different domains.

[NOTE]: Please refer to DATA.md for more details on downloading and processing video data.

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)

# 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

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

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
	ViTT [2]	Source
	YouCook2 [3]	Source
Temporal Video Grounding	DiDeMo [4]	Source
	QuerYD [5]	Source
	HiREST_grounding [6]	Source
	Charades-STA [7]	Source
Video Summarization	TVSum [8]	Source
	SumMe [9]	Source
Video Highlight Detection	QVHighlights [10]	Source
Step Localization	COIN [11]	Source
	HiREST_step [6]	Source
Transcribed Speech Generation	YT-Temporal [12]	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{Ren2023TimeChat,
  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