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VidSTG-Dataset

This repository provides the dataset introduced by the paper "Where Does It Exist: Spatio-Temporal Video Grounding for Multi-Form Sentences".

Introduction

The VidSTG dataset is constructed based on video relation dataset VidOR. VidOR contains 7,000, 835 and 2,165 videos for training, validation and testing, respectively. Since box annotations of testing videos are unavailable yet, we omit testing videos, split 10% training videos as our validation data and regard original validation videos as the testing data. The concrete annotation process can be found in our paper "Where Does It Exist: Spatio-Temporal Video Grounding for Multi-Form Sentences".

Data

The original VidOR dataset contains the raw videos and dense bounding box annotations for each <subject, predicate, object> triplet. These data can be downloaded from the official website.

We only privide the video partition files and sentence annotation files here. The train_files.json, val_files.json and test_files.json contain the video ids in the training, validation and testing sets. And train_annotations.json, val_annotations.json and test_annotations.json contain the sentence annotations.

Annotation Structure

Each annotation is organized by

{
"vid": "5159741010",                            # Video ID
"frame_count": 219,                             # Number of frames
"fps": 29.97002997002997, 
"width": 1920, 
"height": 1080, 
"subject/objects": [                            # List of subject/objects
    {
        "tid": 0,                               # Subject/object ID
        "category": "bicycle"
    }, 
    ...
], 
"used_segment":                                 # Input segment 
{
    "begin_fid": 0,                             # Begin frame ID
    "end_fid": 210                              # End frame ID
}, 
"used_relation":                                # Annotated relation
{
    "subject_tid": 0,                           # Subject ID
    "object_tid": 1,                            # Object ID 
    "predicate": "in_front_of", 
    "begin_fid": 13,                            # Begin frame ID
    "end_fid": 45                               # End frame ID
}, 
"temporal_gt":                                  # Temporal ground truth
{
    "begin_fid": 13,                            # Begin frame ID
    "end_fid": 45                               # End frame ID
}, 
"captions":                                     # Declarative sentences
[
    {
    "description": "there is a red ...",        
    "type": person,                              
    "target_id": 1,                             # Target ID of the queried object
    }
],
"questions":                                    # Interrogative sentences
[
    {
    "description": "there is a red ...",        
    "type": object,                              
    "target_id": 0,                             # Target ID of the queried object
    }, 
    ...
]
}

Citing Our Paper

If you find this dataset useful in your research, please consider citing our paper and the original paper about the VidOR dataset:

@inproceedings{zhang2020does,
    title={Where Does It Exist: Spatio-Temporal Video Grounding for Multi-Form Sentences},
    author={Zhang, Zhu and Zhao, Zhou and Zhao, Yang and Wang, Qi and Liu, Huasheng and Gao, Lianli},
    booktitle={CVPR},
    year={2020}
}

@inproceedings{shang2019annotating,
    title={Annotating Objects and Relations in User-Generated Videos},
    author={Shang, Xindi and Di, Donglin and Xiao, Junbin and Cao, Yu and Yang, Xun and Chua, Tat-Seng},
    booktitle={Proceedings of the 2019 on International Conference on Multimedia Retrieval},
    pages={279--287},
    year={2019},
    organization={ACM}
}