Feature Specific Progressive Improvement for Salient Object Detection

The authors: Xianheng Wang, Zhaobin Liu, Veronica Liesaputra, Zhiyi Huang

The manuscript was originally submitted to Pattern Recognition on June 10, 2021, and was finally accepted on September 27, 2023. The code is currently being organized and will be uploaded gradually.

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Requirements

Python 3.9.7

Tensorflow 2.7.0

Numpy 1.22.4

OpenCV-Python 4.5.4

Introduction

The overall architecture of the proposed PiNet is shown below. The main rationales behind our design are level-specific feature extraction and progressive refinement of saliency. The paper could be found in Link1 (PR) or Link2 (Researchgate).

Saliency maps

PiNet-V (VGG-16 as the backbone): Google Drive

PiNet-R (ResNet-50 as the backbone): Google Drive

PiNet-B3 (EfficientNet-B3 as the backbone): Google Drive

PiNet-B4 (EfficientNet-B4 as the backbone): Google Drive

Quantitative comparisons with SOTA models

Citation

Please cite this work if it is helpful.

title = {Feature specific progressive improvement for salient object detection},
journal = {Pattern Recognition},
volume = {147},
pages = {110085},
year = {2024},
issn = {0031-3203},
doi = {https://doi.org/10.1016/j.patcog.2023.110085},
url = {https://www.sciencedirect.com/science/article/pii/S0031320323007823},
author = {Xianheng Wang and Zhaobin Liu and Veronica Liesaputra and Zhiyi Huang},
keywords = {Salient object detection, Fully convolutional neural network, Level-specific feature extraction, Progressive refinement of saliency},
}