Interactive Segmentation as Gaussian Process Classification

• URL: http://arxiv.org/abs/2302.14578v1
• Date: Tue, 28 Feb 2023 14:01:01 GMT
• Title: Interactive Segmentation as Gaussian Process Classification
• Authors: Minghao Zhou, Hong Wang, Qian Zhao, Yuexiang Li, Yawen Huang, Deyu Meng, Yefeng Zheng
• Abstract summary: Click-based interactive segmentation (IS) aims to extract the target objects under user interaction. Most of the current deep learning (DL)-based methods mainly follow the general pipelines of semantic segmentation. We propose to formulate the IS task as a Gaussian process (GP)-based pixel-wise binary classification model on each image.
• Score: 58.44673380545409
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Click-based interactive segmentation (IS) aims to extract the target objects under user interaction. For this task, most of the current deep learning (DL)-based methods mainly follow the general pipelines of semantic segmentation. Albeit achieving promising performance, they do not fully and explicitly utilize and propagate the click information, inevitably leading to unsatisfactory segmentation results, even at clicked points. Against this issue, in this paper, we propose to formulate the IS task as a Gaussian process (GP)-based pixel-wise binary classification model on each image. To solve this model, we utilize amortized variational inference to approximate the intractable GP posterior in a data-driven manner and then decouple the approximated GP posterior into double space forms for efficient sampling with linear complexity. Then, we correspondingly construct a GP classification framework, named GPCIS, which is integrated with the deep kernel learning mechanism for more flexibility. The main specificities of the proposed GPCIS lie in: 1) Under the explicit guidance of the derived GP posterior, the information contained in clicks can be finely propagated to the entire image and then boost the segmentation; 2) The accuracy of predictions at clicks has good theoretical support. These merits of GPCIS as well as its good generality and high efficiency are substantiated by comprehensive experiments on several benchmarks, as compared with representative methods both quantitatively and qualitatively.

Related papers

• Resolution-Aware Design of Atrous Rates for Semantic Segmentation Networks [7.58745191859815]
  DeepLab is a widely used deep neural network for semantic segmentation, whose success is attributed to its parallel architecture called atrous spatial pyramid pooling (ASPP) fixed values of atrous rates are used for the ASPP module, which restricts the size of its field of view. This study proposes practical guidelines for obtaining an optimal atrous rate.
  arXiv  Detail & Related papers  (2023-07-26T13:11:48Z)
• FCN-Transformer Feature Fusion for Polyp Segmentation [12.62213319797323]
  Colonoscopy is widely recognised as the gold standard procedure for the early detection of colorectal cancer. The manual segmentation of polyps in colonoscopy images is time-consuming. The use of deep learning for automation of polyp segmentation has become important.
  arXiv  Detail & Related papers  (2022-08-17T15:31:06Z)
• Inducing Gaussian Process Networks [80.40892394020797]
  We propose inducing Gaussian process networks (IGN), a simple framework for simultaneously learning the feature space as well as the inducing points. The inducing points, in particular, are learned directly in the feature space, enabling a seamless representation of complex structured domains. We report on experimental results for real-world data sets showing that IGNs provide significant advances over state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-04-21T05:27:09Z)
• Incremental Ensemble Gaussian Processes [53.3291389385672]
  We propose an incremental ensemble (IE-) GP framework, where an EGP meta-learner employs an it ensemble of GP learners, each having a unique kernel belonging to a prescribed kernel dictionary. With each GP expert leveraging the random feature-based approximation to perform online prediction and model update with it scalability, the EGP meta-learner capitalizes on data-adaptive weights to synthesize the per-expert predictions. The novel IE-GP is generalized to accommodate time-varying functions by modeling structured dynamics at the EGP meta-learner and within each GP learner.
  arXiv  Detail & Related papers  (2021-10-13T15:11:25Z)
• Conditional Deep Gaussian Processes: empirical Bayes hyperdata learning [6.599344783327054]
  We propose a conditional Deep Gaussian Process (DGP) in which the intermediate GPs in hierarchical composition are supported by the hyperdata. We show the equivalence with the deep kernel learning in the limit of dense hyperdata in latent space. Preliminary extrapolation results demonstrate expressive power of the proposed model compared with GP kernel composition, DGP variational inference, and deep kernel learning.
  arXiv  Detail & Related papers  (2021-10-01T17:50:48Z)
• Boosting Few-shot Semantic Segmentation with Transformers [81.43459055197435]
  TRansformer-based Few-shot Semantic segmentation method (TRFS) Our model consists of two modules: Global Enhancement Module (GEM) and Local Enhancement Module (LEM)
  arXiv  Detail & Related papers  (2021-08-04T20:09:21Z)
• Deep Gaussian Processes for Few-Shot Segmentation [66.08463078545306]
  Few-shot segmentation is a challenging task, requiring the extraction of a generalizable representation from only a few annotated samples. We propose a few-shot learner formulation based on Gaussian process (GP) regression. Our approach sets a new state-of-the-art for 5-shot segmentation, with mIoU scores of 68.1 and 49.8 on PASCAL-5i and COCO-20i, respectively.
  arXiv  Detail & Related papers  (2021-03-30T17:56:32Z)
• GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning [23.83961717568121]
  GP-Tree is a novel method for multi-class classification with Gaussian processes and deep kernel learning. We develop a tree-based hierarchical model in which each internal node fits a GP to the data. Our method scales well with both the number of classes and data size.
  arXiv  Detail & Related papers  (2021-02-15T22:16:27Z)
• Inter-domain Deep Gaussian Processes [45.28237107466283]
  We propose an extension of inter-domain shallow GPs that combines the advantages of inter-domain and deep Gaussian processes (DGPs) We demonstrate how to leverage existing approximate inference methods to perform simple and scalable approximate inference using inter-domain features in DGPs.
  arXiv  Detail & Related papers  (2020-11-01T04:03:35Z)
• Boundary-assisted Region Proposal Networks for Nucleus Segmentation [89.69059532088129]
  Machine learning models cannot perform well because of large amount of crowded nuclei. We devise a Boundary-assisted Region Proposal Network (BRP-Net) that achieves robust instance-level nucleus segmentation.
  arXiv  Detail & Related papers  (2020-06-04T08:26:38Z)

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.