CPDR: Towards Highly-Efficient Salient Object Detection via Crossed Post-decoder Refinement


Yijie Li (Northwestern University, Northwestern University), Hewei Wang (Apple), Aggelos Katsaggelos (Northwestern University)
The 35th British Machine Vision Conference
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Abstract

Most of the current salient object detection approaches use deeper networks with large backbones to produce more accurate predictions, which results in a significant increase in computational complexity. A great number of network designs follow the pure UNet and Feature Pyramid Network (FPN) architecture which has limited feature extraction and aggregation ability which motivated us to design a lightweight post-decoder refinement module, the crossed post-decoder refinement (CPDR) to enhance the feature representation of a standard FPN or U-Net framework. Specifically, we introduce the Attention Down Sample Fusion (ADF), which employs channel attention mechanisms with attention maps generated by high-level representation to refine the low-level features, and Attention Up Sample Fusion (AUF), leveraging the low-level information to guide the high-level features through spatial attention. Additionally, we proposed the Dual Attention Cross Fusion (DACF) upon ADFs and AUFs, which reduces the number of parameters while maintaining the performance. Experiments on five benchmark datasets demonstrate that our method outperforms previous state-of-the-art approaches.

Citation

@inproceedings{Li_2024_BMVC,
author    = {Yijie Li and Hewei Wang and Aggelos Katsaggelos},
title     = {CPDR: Towards Highly-Efficient Salient Object Detection via Crossed Post-decoder Refinement},
booktitle = {35th British Machine Vision Conference 2024, {BMVC} 2024, Glasgow, UK, November 25-28, 2024},
publisher = {BMVA},
year      = {2024},
url       = {https://papers.bmvc2024.org/0630.pdf}
}


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