SegFix: Model-Agnostic Boundary Refinement for Segmentation

• URL: http://arxiv.org/abs/2007.04269v4
• Date: Thu, 27 Aug 2020 09:45:58 GMT
• Title: SegFix: Model-Agnostic Boundary Refinement for Segmentation
• Authors: Yuhui Yuan, Jingyi Xie, Xilin Chen, Jingdong Wang
• Abstract summary: We present a model-agnostic post-processing scheme to improve the boundary quality for the segmentation result that is generated by any existing segmentation model. Motivated by the empirical observation that the label predictions of interior pixels are more reliable, we propose to replace the originally unreliable predictions of boundary pixels by the predictions of interior pixels.
• Score: 75.58050758615316
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a model-agnostic post-processing scheme to improve the boundary quality for the segmentation result that is generated by any existing segmentation model. Motivated by the empirical observation that the label predictions of interior pixels are more reliable, we propose to replace the originally unreliable predictions of boundary pixels by the predictions of interior pixels. Our approach processes only the input image through two steps: (i) localize the boundary pixels and (ii) identify the corresponding interior pixel for each boundary pixel. We build the correspondence by learning a direction away from the boundary pixel to an interior pixel. Our method requires no prior information of the segmentation models and achieves nearly real-time speed. We empirically verify that our SegFix consistently reduces the boundary errors for segmentation results generated from various state-of-the-art models on Cityscapes, ADE20K and GTA5. Code is available at: https://github.com/openseg-group/openseg.pytorch.

Related papers

• Adaptive Patching for High-resolution Image Segmentation with Transformers [9.525013089622183]
  Attention-based models are proliferating in the space of image analytics, including segmentation. Standard method of feeding images to transformer encoders is to divide the images into patches and then feed the patches to the model as a linear sequence of tokens. For high-resolution images, e.g. microscopic pathology images, the quadratic compute and memory cost prohibits the use of an attention-based model, if we are to use smaller patch sizes that are favorable in segmentation. We take inspiration from Adapative Mesh Refinement (AMR) methods in HPC by adaptively patching the images, as a pre-processing step, based
  arXiv  Detail & Related papers  (2024-04-15T12:06:00Z)
• PNM: Pixel Null Model for General Image Segmentation [17.971090313814447]
  We present a prior model that weights each pixel according to its probability of being correctly classified by a random segmenter. Experiments on semantic, instance, and panoptic segmentation tasks over three datasets confirm that PNM consistently improves the segmentation quality. We propose a new metric, textitPNM IoU, which perceives the boundary sharpness and better reflects the model segmentation performance in error-prone regions.
  arXiv  Detail & Related papers  (2022-03-13T15:17:41Z)
• PSGR: Pixel-wise Sparse Graph Reasoning for COVID-19 Pneumonia Segmentation in CT Images [83.26057031236965]
  We propose a pixel-wise sparse graph reasoning (PSGR) module to enhance the modeling of long-range dependencies for COVID-19 infected region segmentation in CT images. The PSGR module avoids imprecise pixel-to-node projections and preserves the inherent information of each pixel for global reasoning. The solution has been evaluated against four widely-used segmentation models on three public datasets.
  arXiv  Detail & Related papers  (2021-08-09T04:58:23Z)
• BoundarySqueeze: Image Segmentation as Boundary Squeezing [104.43159799559464]
  We propose a novel method for fine-grained high-quality image segmentation of both objects and scenes. Inspired by dilation and erosion from morphological image processing techniques, we treat the pixel level segmentation problems as squeezing object boundary. Our method yields large gains on COCO, Cityscapes, for both instance and semantic segmentation and outperforms previous state-of-the-art PointRend in both accuracy and speed under the same setting.
  arXiv  Detail & Related papers  (2021-05-25T04:58:51Z)
• Look Closer to Segment Better: Boundary Patch Refinement for Instance Segmentation [51.59290734837372]
  We propose a conceptually simple yet effective post-processing refinement framework to improve the boundary quality. The proposed BPR framework yields significant improvements over the Mask R-CNN baseline on Cityscapes benchmark. By applying the BPR framework to the PolyTransform + SegFix baseline, we reached 1st place on the Cityscapes leaderboard.
  arXiv  Detail & Related papers  (2021-04-12T07:10:48Z)
• AINet: Association Implantation for Superpixel Segmentation [82.21559299694555]
  We propose a novel textbfAssociation textbfImplantation (AI) module to enable the network to explicitly capture the relations between the pixel and its surrounding grids. Our method could not only achieve state-of-the-art performance but maintain satisfactory inference efficiency.
  arXiv  Detail & Related papers  (2021-01-26T10:40:13Z)
• Boundary-Aware Geometric Encoding for Semantic Segmentation of Point Clouds [45.270215729464056]
  Boundary information plays a significant role in 2D image segmentation, while usually being ignored in 3D point cloud segmentation. We propose a Boundary Prediction Module (BPM) to predict boundary points. Based on the predicted boundary, a boundary-aware Geometric. GEM is designed to encode geometric information and aggregate features with discrimination in a neighborhood.
  arXiv  Detail & Related papers  (2021-01-07T05:38:19Z)
• Superpixel Segmentation Based on Spatially Constrained Subspace Clustering [57.76302397774641]
  We consider each representative region with independent semantic information as a subspace, and formulate superpixel segmentation as a subspace clustering problem. We show that a simple integration of superpixel segmentation with the conventional subspace clustering does not effectively work due to the spatial correlation of the pixels. We propose a novel convex locality-constrained subspace clustering model that is able to constrain the spatial adjacent pixels with similar attributes to be clustered into a superpixel.
  arXiv  Detail & Related papers  (2020-12-11T06:18:36Z)
• Unsupervised Community Detection with a Potts Model Hamiltonian, an Efficient Algorithmic Solution, and Applications in Digital Pathology [1.6506888719932784]
  We propose a fast statistical down-sampling of input image pixels based on the respective color features, and a new iterative method to minimize the Potts model energy considering pixel to segment relationship. We demonstrate the application of our method in medical microscopy image segmentation; particularly, in segmenting renal glomerular micro-environment in renal pathology.
  arXiv  Detail & Related papers  (2020-02-05T01:20:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.