- Paper: http://www.roboticsproceedings.org/rss18/p048.pdf
- Preprint: https://arxiv.org/pdf/2011.06252.pdf
- Video demonstration: https://youtu.be/SxJcsoQw7KI
- Data: http://irvlab.cs.umn.edu/resources/usod-dataset
- Project page: http://irvlab.cs.umn.edu/visual-attention-modeling/svam
- Jointly accommodate bottom-up and top-down learning in two branches sharing the same encoding layers
- Incorporates four spatial attention modules (SAMs) along these learning pathways
- Exploits coarse-level and fine-level semantic features for SOD at four stages of abstractions
- The bottom-up pipeline (SVAM-Net_Light) performs abstract saliency prediction at fast rates
- The top-down pipeline ensures fine-grained saliency estimation by aresidual refinement module (RRM)
- Pretrained weights can be downloaded from this Google-Drive link
- Provides SOTA performance for SOD on underwater imagery
- Exhibits significantly better generalization performance than existing solutions
- Achieves fast end-to-end inference
- The end-to-end SVAM-Net : 20.07 FPS in GTX-1080, 4.5 FPS on Jetson Xavier
- Decoupled SVAM-Net_Light: 86.15 FPS in GTX-1080, 21.77 FPS on Jetson Xavier
- A new challenging test set for benchmark evaluation of underwater SOD models
- Contains 300 natural underwater images and ground truth labels
- Can be downloaded from: http://irvlab.cs.umn.edu/resources/usod-dataset
- Evaluation code: https://github.com/xahidbuffon/SOD-Evaluation-Tool-Python
- Evaluation data can be found in this Google-Drive link
@inproceedings{islam2022svam,
author={Islam, Md Jahidul and Wang, Ruobing and Sattar, Junaed},
title={{SVAM: Saliency-guided Visual Attention Modeling
by Autonomous Underwater Robots}},
booktitle={Robotics: Science and Systems (RSS)},
year={2022},
address={NY, USA}
}
- https://github.com/CaitinZhao/cvpr2019_Pyramid-Feature-Attention-Network-for-Saliency-detection
- https://github.com/Ugness/PiCANet-Implementation
- https://github.com/wenguanwang/SODsurvey
- https://github.com/wenguanwang/PAGE-Net
- https://github.com/backseason/PoolNet
- https://github.com/wenguanwang/ASNet
- https://github.com/NathanUA/BASNet
- https://github.com/wuzhe71/CPD
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