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Awesome-Referring-Video-Object-Segmentation / Tracking Awesome

Welcome to starts ⭐ & comments 💹 & sharing 😀 !!

- 2021.12.12: Recent papers (from 2021) 
- welcome to add if any information misses. 😎

Introduction

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Referring video object segmentation aims at segmenting an object in video with language expressions.

Unlike the previous video object segmentation, the task exploits a different type of supervision, language expressions, to identify and segment an object referred by the given language expressions in a video. A detailed explanation of the new task can be found in the following paper.

Impressive Works Related to Referring Video Object Segmentation (RVOS)

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  • Decoupling Multimodal Transformers for Referring Video Object Segmentation TCSVT23

  • Unified Multi-Modality Video Object Segmentation Using Reinforcement Learning TCSVT23

  • Referring Video Segmentation with (Optional) Arbitrary Modality as Query for Fusion ArXiV

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  • Multi-Level Representation Learning with Semantic Alignment for Referring Video Object Segmentation [CVPR 2022]

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Modeling Motion with Multi-Modal Features for Text-Based Video Segmentation [CVPR 2022]:https://arxiv.org/pdf/2204.02547.pdf image

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Cross-modal progressive comprehension for referring segmentation:https://arxiv.org/abs/2105.07175 image

Impressive Works Related to Referring Image Segmentation (Rerfer-image-segmentation)

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Impressive Works Related to Referring Multi-Object Tracking (RMOT)

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Benchmark

The 3rd Large-scale Video Object Segmentation - Track 3: Referring Video Object Segmentation

Datasets

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Refer-YouTube-VOS-datasets

  • YouTube-VOS:
wget https://github.com/JerryX1110/awesome-rvos/blob/main/down_YTVOS_w_refer.py
python down_YTVOS_w_refer.py

Folder structure:

${current_path}/
└── refer_youtube_vos/ 
    ├── train/
    │   ├── JPEGImages/
    │   │   └── */ (video folders)
    │   │       └── *.jpg (frame image files) 
    │   └── Annotations/
    │       └── */ (video folders)
    │           └── *.png (mask annotation files) 
    ├── valid/
    │   └── JPEGImages/
    │       └── */ (video folders)
    │           └── *.jpg (frame image files) 
    └── meta_expressions/
        ├── train/
        │   └── meta_expressions.json  (text annotations)
        └── valid/
            └── meta_expressions.json  (text annotations)
  • A2D-Sentences:

REPO:https://web.eecs.umich.edu/~jjcorso/r/a2d/

paper:https://arxiv.org/abs/1803.07485

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Citation:

@misc{gavrilyuk2018actor,
      title={Actor and Action Video Segmentation from a Sentence}, 
      author={Kirill Gavrilyuk and Amir Ghodrati and Zhenyang Li and Cees G. M. Snoek},
      year={2018},
      eprint={1803.07485},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

License: The dataset may not be republished in any form without the written consent of the authors.

README Dataset and Annotation (version 1.0, 1.9GB, tar.bz) Evaluation Toolkit (version 1.0, tar.bz)

mkdir a2d_sentences
cd a2d_sentences
wget https://web.eecs.umich.edu/~jjcorso/bigshare/A2D_main_1_0.tar.bz
tar jxvf A2D_main_1_0.tar.bz
mkdir text_annotations

cd text_annotations
wget https://kgavrilyuk.github.io/actor_action/a2d_annotation.txt
wget https://kgavrilyuk.github.io/actor_action/a2d_missed_videos.txt
wget https://github.com/JerryX1110/awesome-rvos/blob/main/down_a2d_annotation_with_instances.py
python down_a2d_annotation_with_instances.py
unzip a2d_annotation_with_instances.zip
#rm a2d_annotation_with_instances.zip
cd ..

cd ..

Folder structure:

${current_path}/
└── a2d_sentences/ 
    ├── Release/
    │   ├── videoset.csv  (videos metadata file)
    │   └── CLIPS320/
    │       └── *.mp4     (video files)
    └── text_annotations/
        ├── a2d_annotation.txt  (actual text annotations)
        ├── a2d_missed_videos.txt
        └── a2d_annotation_with_instances/ 
            └── */ (video folders)
                └── *.h5 (annotations files)

Citation:

@inproceedings{YaXuCaCVPR2017,
  author = {Yan, Y. and Xu, C. and Cai, D. and {\bf Corso}, {\bf J. J.}},
  booktitle = {{Proceedings of IEEE Conference on Computer Vision and Pattern Recognition}},
  tags = {computer vision, activity recognition, video understanding, semantic segmentation},
  title = {Weakly Supervised Actor-Action Segmentation via Robust Multi-Task Ranking},
  year = {2017}
}
@inproceedings{XuCoCVPR2016,
  author = {Xu, C. and {\bf Corso}, {\bf J. J.}},
  booktitle = {{Proceedings of IEEE Conference on Computer Vision and Pattern Recognition}},
  datadownload = {http://web.eecs.umich.edu/~jjcorso/r/a2d},
  tags = {computer vision, activity recognition, video understanding, semantic segmentation},
  title = {Actor-Action Semantic Segmentation with Grouping-Process Models},
  year = {2016}
}
@inproceedings{XuHsXiCVPR2015,
  author = {Xu, C. and Hsieh, S.-H. and Xiong, C. and {\bf Corso}, {\bf J. J.}},
  booktitle = {{Proceedings of IEEE Conference on Computer Vision and Pattern Recognition}},
  datadownload = {http://web.eecs.umich.edu/~jjcorso/r/a2d},
  poster = {http://web.eecs.umich.edu/~jjcorso/pubs/xu_corso_CVPR2015_A2D_poster.pdf},
  tags = {computer vision, activity recognition, video understanding, semantic segmentation},
  title = {Can Humans Fly? {Action} Understanding with Multiple Classes of Actors},
  url = {http://web.eecs.umich.edu/~jjcorso/pubs/xu_corso_CVPR2015_A2D.pdf},
  year = {2015}
}

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downloading_script

mkdir jhmdb_sentences
cd jhmdb_sentences
wget http://files.is.tue.mpg.de/jhmdb/Rename_Images.tar.gz
wget https://kgavrilyuk.github.io/actor_action/jhmdb_annotation.txt
wget http://files.is.tue.mpg.de/jhmdb/puppet_mask.zip
tar -xzvf  Rename_Images.tar.gz
unzip puppet_mask.zip
cd ..

Folder structure:

${current_path}/
└── jhmdb_sentences/ 
    ├── Rename_Images/  (frame images)
    │   └── */ (action dirs)
    ├── puppet_mask/  (mask annotations)
    │   └── */ (action dirs)
    └── jhmdb_annotation.txt  (text annotations)

Citation:

@inproceedings{Jhuang:ICCV:2013,
title = {Towards understanding action recognition},
author = {H. Jhuang and J. Gall and S. Zuffi and C. Schmid and M. J. Black},
booktitle = {International Conf. on Computer Vision (ICCV)},
month = Dec,
pages = {3192-3199},
year = {2013}
}

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