Recurrent Generic Contour-based Instance Segmentation with Progressive Learning

• URL: http://arxiv.org/abs/2301.08898v3
• Date: Mon, 22 Jan 2024 03:01:28 GMT
• Title: Recurrent Generic Contour-based Instance Segmentation with Progressive Learning
• Authors: Hao Feng, Keyi Zhou, Wengang Zhou, Yufei Yin, Jiajun Deng, Qi Sun, Houqiang Li
• Abstract summary: We propose a novel deep network architecture, i.e., PolySnake, for generic contour-based instance segmentation. Motivated by the classic Snake algorithm, the proposed PolySnake achieves superior and robust segmentation performance.
• Score: 111.31166268300817
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contour-based instance segmentation has been actively studied, thanks to its flexibility and elegance in processing visual objects within complex backgrounds. In this work, we propose a novel deep network architecture, i.e., PolySnake, for generic contour-based instance segmentation. Motivated by the classic Snake algorithm, the proposed PolySnake achieves superior and robust segmentation performance with an iterative and progressive contour refinement strategy. Technically, PolySnake introduces a recurrent update operator to estimate the object contour iteratively. It maintains a single estimate of the contour that is progressively deformed toward the object boundary. At each iteration, PolySnake builds a semantic-rich representation for the current contour and feeds it to the recurrent operator for further contour adjustment. Through the iterative refinements, the contour progressively converges to a stable status that tightly encloses the object instance. Beyond the scope of general instance segmentation, extensive experiments are conducted to validate the effectiveness and generalizability of our PolySnake in two additional specific task scenarios, including scene text detection and lane detection. The results demonstrate that the proposed PolySnake outperforms the existing advanced methods on several multiple prevalent benchmarks across the three tasks. The codes and pre-trained models are available at https://github.com/fh2019ustc/PolySnake

Related papers

• P2PFormer: A Primitive-to-polygon Method for Regular Building Contour Extraction from Remote Sensing Images [5.589842901102337]
  Existing methods struggle with irregular contours, rounded corners, and redundancy points. We introduce a novel, streamlined pipeline that generates regular building contours without post-processing. P2PFormer achieves new state-of-the-art performance on the WHU, CrowdAI, and WHU-Mix datasets.
  arXiv  Detail & Related papers  (2024-06-05T04:38:45Z)
• Human as Points: Explicit Point-based 3D Human Reconstruction from Single-view RGB Images [78.56114271538061]
  We introduce an explicit point-based human reconstruction framework called HaP. Our approach is featured by fully-explicit point cloud estimation, manipulation, generation, and refinement in the 3D geometric space. Our results may indicate a paradigm rollback to the fully-explicit and geometry-centric algorithm design.
  arXiv  Detail & Related papers  (2023-11-06T05:52:29Z)
• SRFormer: Text Detection Transformer with Incorporated Segmentation and Regression [6.74412860849373]
  We propose SRFormer, a unified DETR-based model with amalgamated and Regression. Our empirical analysis indicates that favorable segmentation predictions can be obtained at the initial decoder layers. Our method's exceptional robustness, superior training and data efficiency, as well as its state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-08-21T07:34:31Z)
• Semantics-Aware Dynamic Localization and Refinement for Referring Image Segmentation [102.25240608024063]
  Referring image segments an image from a language expression. We develop an algorithm that shifts from being localization-centric to segmentation-language. Compared to its counterparts, our method is more versatile yet effective.
  arXiv  Detail & Related papers  (2023-03-11T08:42:40Z)
• Generative Category-Level Shape and Pose Estimation with Semantic Primitives [27.692997522812615]
  We propose a novel framework for category-level object shape and pose estimation from a single RGB-D image. To handle the intra-category variation, we adopt a semantic primitive representation that encodes diverse shapes into a unified latent space. We show that the proposed method achieves SOTA pose estimation performance and better generalization in the real-world dataset.
  arXiv  Detail & Related papers  (2022-10-03T17:51:54Z)
• ContourRender: Detecting Arbitrary Contour Shape For Instance Segmentation In One Pass [48.57232627854642]
  We argue that the difficulty in regressing the contour points in one pass is mainly due to the ambiguity when discretizing a smooth contour into a polygon. To address the ambiguity, we propose a novel differentiable rendering-based approach named textbfContourRender. It first predicts a contour generated by an invertible shape signature, and then optimize the contour with the more stable silhouette by converting it to a contour mesh and rendering the mesh to a 2D map.
  arXiv  Detail & Related papers  (2021-06-07T07:23:03Z)
• DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution [136.7261709896713]
  We propose a data-driven approach that generates the appropriate convolution kernels to apply in response to the nature of the instances. The proposed method achieves promising results on both ScanetNetV2 and S3DIS. It also improves inference speed by more than 25% over the current state-of-the-art.
  arXiv  Detail & Related papers  (2020-11-26T14:56:57Z)
• CellSegmenter: unsupervised representation learning and instance segmentation of modular images [0.0]
  We introduce a structured deep generative model and an amortized inference framework for unsupervised representation learning and instance segmentation tasks. The proposed inference algorithm is convolutional and parallelized, without any recurrent mechanisms. We show segmentation results obtained for a cell nuclei imaging dataset, demonstrating the ability of our method to provide high-quality segmentations.
  arXiv  Detail & Related papers  (2020-11-25T02:10:58Z)
• Deep Shells: Unsupervised Shape Correspondence with Optimal Transport [52.646396621449]
  We propose a novel unsupervised learning approach to 3D shape correspondence. We show that the proposed method significantly improves over the state-of-the-art on multiple datasets.
  arXiv  Detail & Related papers  (2020-10-28T22:24:07Z)

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.