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Caffe Implementation (v1) for

Learning Unsupervised Video Object Segmentation through Visual Attention (CVPR19)

Paying Attention to Video Object Pattern Understanding (PAMI20)



2019/10: Results on DAVIS17 val and test sets are added (instance-level video object segmentation)


  1. Please install our modified caffe first.

  2. Download the model weight from and put dvap_agos.caffemodel into the 'model' folder.

  3. Then edit paths in './test/' and './test/davis_bs3.txt'.

  4. After you get the original results from the network, you can use './test/' to process them.

  5. Our results on DAVIS16, FBMS and Youtube can be find at 'seg_results.rar'. Saliency results can be found at

  6. Human eye fixation data for DAVIS16, Youtube and Segtrackv2 can be found at

  7. Our model, segmentation and saliency results, and fixation data can also be found at Baidu Pan , extaction code: 6prn

If you find our project helpful, your citations are highly appreciated:

author = {Wang, Wenguan and Song, Hongmei and Zhao, Shuyang and Shen, Jianbing and Zhao, Sanyuan and Hoi, Steven Chu Hong and Ling, Haibin},
title = {Learning Unsupervised Video Object Segmentation through Visual Attention},
booktitle = {CVPR},
year = {2019}

author = {Song, Hongmei and Wang, Wenguan and Zhao, Sanyuan and Shen, Jianbing and Lam, Kin-Man},
title = {Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection},
booktitle = {ECCV},
year = {2018}

Other related projects/papers:

Zero-shot Video Object Segmentation via Attentive Graph Neural Networks (ICCV2019 Oral)

See More, Know More: Unsupervised Video Object Segmentation With Co-Attention Siamese Networks (CVPR19)

Saliency-Aware Geodesic Video Object Segmentation (CVPR15)

======================================================================== This repository is no longer maintained.