CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement

• URL: http://arxiv.org/abs/2005.02551v1
• Date: Wed, 6 May 2020 01:38:03 GMT
• Title: CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement
• Authors: Ho Kei Cheng (HKUST), Jihoon Chung (HKUST), Yu-Wing Tai (Tencent), Chi-Keung Tang (HKUST)
• Abstract summary: State-of-the-art semantic segmentation methods were almost exclusively trained on images within a fixed resolution range. We propose a novel approach to address the high-resolution segmentation problem without using any high-resolution training data.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: State-of-the-art semantic segmentation methods were almost exclusively trained on images within a fixed resolution range. These segmentations are inaccurate for very high-resolution images since using bicubic upsampling of low-resolution segmentation does not adequately capture high-resolution details along object boundaries. In this paper, we propose a novel approach to address the high-resolution segmentation problem without using any high-resolution training data. The key insight is our CascadePSP network which refines and corrects local boundaries whenever possible. Although our network is trained with low-resolution segmentation data, our method is applicable to any resolution even for very high-resolution images larger than 4K. We present quantitative and qualitative studies on different datasets to show that CascadePSP can reveal pixel-accurate segmentation boundaries using our novel refinement module without any finetuning. Thus, our method can be regarded as class-agnostic. Finally, we demonstrate the application of our model to scene parsing in multi-class segmentation.

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