CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement
arXiv:2005.02551
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.
arXiv commentAccepted to CVPR2020. Project page: https://github.com/hkchengrex/CascadePSP
References in corpus (8)
- Rethinking Atrous Convolution for Semantic Image Segmentation
- Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
- Conditional Random Fields as Recurrent Neural Networks
- ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
- ParseNet: Looking Wider to See Better
- DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
- Convolutional Feature Masking for Joint Object and Stuff Segmentation
- The Importance of Skip Connections in Biomedical Image Segmentation