LV-VIS: Large-Vocabulary Video Instance Segmentation dataset

📄[arXiv]   📄[ICCV(Oral)]   🔥[Dataset Download]   🔥[Evaluation Server]

This repo is the official implementation of Towards Open Vocabulary Video Instance Segmentation (ICCV2023 oral)

News

We are working on the final revision of the annotations. The Codalab set will be released in November.

Towards Open Vocabulary Video Instance Segmentation

Haochen Wang¹, Cilin Yan², Shuai Wang ¹, Xiaolong Jiang ³, Xu Tang³, Yao Hu³, Weidi Xie ⁴,Efstratios Gavves ¹

¹University of Amsterdam, ²Beihang University, ³Xiaohongshu Inc, ⁴ Shanghai Jiao Tong University.

LV-VIS dataset

LV-VIS is a dataset/benchmark for Open-Vocabulary Video Instance Segmentation. It contains a total of 4,828 videos with pixel-level segmentation masks for 26,099 objects from 1,196 unique categories.

LV-VIS is licensed under a CC BY-NC-SA 4.0 License. The data of LV-VIS is released for non-commercial research purpose only.

Dataset Download

	Videos	Annotations	Annotations (oracle)	Submission Example
Training	Download	Download	-	-
Validation	Download	Download	Download	-
Test	Download	-	-	Download

Dataset Structure

## JPEGImages

|- train
  |- 00000
    |- 00000.jpg
    |- 00001.jpg
       ...
  |- 00001
    |- 00000.jpg
    |- 00001.jpg
       ...
    ...
|- val
    ...
|- test
    ...

## Annotations
train_instances.json
val_instances.json
image_val_instances.json

The annotation files have the same formation as Youtube-VIS 2019.

Annotation Tool

We used this platform for the annotation of LV-VIS. This platform is a smart video segmentation annotation tool based on Lableme, SAM, and STCN. See segment-anything-annotator.

Baseline

We provide our baseline OV2Seg code for LV-VIS. Please check Baseline.md for more details.

TODO

• Training and inference code of OV2Seg
• Leaderboard for Val/test set

NOTE:

• We haven't decided to release the annotation file for the test set yet. Please be patient.
• The training set is not exhaustively annotated.
• If you find mistakes in the annotations, please contact us (h.wang3@uva.nl). We will update the annotations.

Cite

@inproceedings{wang2023towards,
  title={Towards Open-Vocabulary Video Instance Segmentation},
  author={Wang, Haochen and Yan, Cilin and Wang, Shuai and Jiang, Xiaolong and Tang, XU and Hu, Yao and Xie, Weidi and Gavves, Efstratios},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  year={2023}
}

Acknowledgement

This repo is built based on Mask2Former and Detic, thanks for those excellent projects.