Applying Eigencontours to PolarMask-Based Instance Segmentation

• URL: http://arxiv.org/abs/2208.11258v1
• Date: Wed, 24 Aug 2022 01:33:18 GMT
• Title: Applying Eigencontours to PolarMask-Based Instance Segmentation
• Authors: Wonhui Park, Dongkwon Jin, Chang-Su Kim
• Abstract summary: Eigencontours are the first data-driven contour descriptors based on singular value decomposition. In this report, we incorporate eigencontours into the PolarMask network for instance segmentation.
• Score: 40.13463458124477
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Eigencontours are the first data-driven contour descriptors based on singular value decomposition. Based on the implementation of ESE-Seg, eigencontours were applied to the instance segmentation task successfully. In this report, we incorporate eigencontours into the PolarMask network for instance segmentation. Experimental results demonstrate that the proposed algorithm yields better results than PolarMask on two instance segmentation datasets of COCO2017 and SBD. Also, we analyze the characteristics of eigencontours qualitatively. Our codes are available at https://github.com/dnjs3594/Eigencontours.

Related papers

• SegRefiner: Towards Model-Agnostic Segmentation Refinement with Discrete Diffusion Process [102.18226145874007]
  We propose a model-agnostic solution called SegRefiner to enhance the quality of object masks produced by different segmentation models. SegRefiner takes coarse masks as inputs and refines them using a discrete diffusion process. It consistently improves both the segmentation metrics and boundary metrics across different types of coarse masks.
  arXiv  Detail & Related papers  (2023-12-19T18:53:47Z)
• Ensembling Instance and Semantic Segmentation for Panoptic Segmentation [0.0]
  Methods first performs instance segmentation and semantic segmentation separately, then combines the two to generate panoptic segmentation results. We add several expert models of Mask R-CNN in instance segmentation to tackle the data imbalance problem in the training data. In semantic segmentation, we trained several models with various backbones and use an ensemble strategy which further boosts the segmentation results.
  arXiv  Detail & Related papers  (2023-04-20T14:02:01Z)
• Eigencontours: Novel Contour Descriptors Based on Low-Rank Approximation [40.13463458124477]
  We decompose a contour matrix into eigencontours via the best rank-M approximation. We represent an object boundary by a linear combination of the M eigencontours. The proposed algorithm yields meaningful performances on instance segmentation datasets.
  arXiv  Detail & Related papers  (2022-03-29T06:14:38Z)
• CPSeg: Cluster-free Panoptic Segmentation of 3D LiDAR Point Clouds [2.891413712995641]
  We propose a novel real-time end-to-end panoptic segmentation network for LiDAR point clouds, called CPSeg. CPSeg comprises a shared encoder, a dual decoder, a task-aware attention module (TAM) and a cluster-free instance segmentation head.
  arXiv  Detail & Related papers  (2021-11-02T16:44:06Z)
• SOLO: A Simple Framework for Instance Segmentation [84.00519148562606]
  "instance categories" assigns categories to each pixel within an instance according to the instance's location. "SOLO" is a simple, direct, and fast framework for instance segmentation with strong performance. Our approach achieves state-of-the-art results for instance segmentation in terms of both speed and accuracy.
  arXiv  Detail & Related papers  (2021-06-30T09:56:54Z)
• PolarMask++: Enhanced Polar Representation for Single-Shot Instance Segmentation and Beyond [47.518550130850755]
  PolarMask reformulates the instance segmentation problem as predicting the contours of objects in the polar coordinate. Two modules are carefully designed (i.e. soft polar centerness and polar IoU loss) to sample high-quality center examples. PolarMask is fully convolutional and can be easily embedded into most off-the-shelf detection methods.
  arXiv  Detail & Related papers  (2021-05-05T16:55:53Z)
• PointINS: Point-based Instance Segmentation [117.38579097923052]
  Mask representation in instance segmentation with Point-of-Interest (PoI) features is challenging because learning a high-dimensional mask feature for each instance requires a heavy computing burden. We propose an instance-aware convolution, which decomposes this mask representation learning task into two tractable modules. Along with instance-aware convolution, we propose PointINS, a simple and practical instance segmentation approach.
  arXiv  Detail & Related papers  (2020-03-13T08:24:58Z)
• Conditional Convolutions for Instance Segmentation [109.2706837177222]
  We propose a simple yet effective instance segmentation framework, termed CondInst. We employ dynamic instance-aware networks, conditioned on instances. We demonstrate a simpler instance segmentation method that can achieve improved performance in both accuracy and inference speed.
  arXiv  Detail & Related papers  (2020-03-12T08:42:36Z)

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.