One-stage Progressive Dichotomous Segmentation

Jing Zhu (Samsung Research America),* Karim Ahmed (Samsung Research America), Wenbo Li (Samsung Research America), Yilin Shen (Samsung Research America), Hongxia Jin (Samsung Research America)
The 34th British Machine Vision Conference


Dichotomous segmentation is a challenging task that involves recognizing foreground objects in high-resolution images with varying characteristics. Existing methods often miss important details of the object or require a long processing time due to multi-stage process. In this paper, we propose a one-stage effective model that can distinguish objects in dichotomous segmentation with low computation cost. Unlike most methods that use two separate branches to first obtain coarse results from low-resolution images and then refine them with the high-resolution information, our method can directly process high-resolution inputs with simple operations. We introduce convolutional attentions into the feature extractor to effectively capture multi-scale features. These features are then used to generate high-quality results with a specifically designed progressive decoder. The experimental results demonstrate that our method achieves superior performance on the DIS5K dichotomous segmentation dataset with fewer model parameters and computational operations.



author    = {Jing Zhu and Karim Ahmed and Wenbo Li and Yilin Shen and Hongxia Jin},
title     = {One-stage Progressive Dichotomous Segmentation},
booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023},
publisher = {BMVA},
year      = {2023},
url       = {}

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