Semantic Segmentation by Improved Generative Adversarial Networks

• URL: http://arxiv.org/abs/2104.09917v1
• Date: Tue, 20 Apr 2021 11:59:29 GMT
• Title: Semantic Segmentation by Improved Generative Adversarial Networks
• Authors: ZengShun Zhaoa (1), Yulong Wang (1), Ke Liu (1), Haoran Yang (1), Qian Sun (1), Heng Qiao (2) ((1) Shandong University of Science and Technology,(2) University of Florida)
• Abstract summary: We introduce Convolutional CRFs (ConvCRFs) as an effective improvement solution for the image semantic segmentation task. Our method not only learns an end-to-end mapping from input image to corresponding output image, but also learns a loss function to train this mapping.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While most existing segmentation methods usually combined the powerful feature extraction capabilities of CNNs with Conditional Random Fields (CRFs) post-processing, the result always limited by the fault of CRFs . Due to the notoriously slow calculation speeds and poor efficiency of CRFs, in recent years, CRFs post-processing has been gradually eliminated. In this paper, an improved Generative Adversarial Networks (GANs) for image semantic segmentation task (semantic segmentation by GANs, Seg-GAN) is proposed to facilitate further segmentation research. In addition, we introduce Convolutional CRFs (ConvCRFs) as an effective improvement solution for the image semantic segmentation task. Towards the goal of differentiating the segmentation results from the ground truth distribution and improving the details of the output images, the proposed discriminator network is specially designed in a full convolutional manner combined with cascaded ConvCRFs. Besides, the adversarial loss aggressively encourages the output image to be close to the distribution of the ground truth. Our method not only learns an end-to-end mapping from input image to corresponding output image, but also learns a loss function to train this mapping. The experiments show that our method achieves better performance than state-of-the-art methods.

Related papers

• LeRF: Learning Resampling Function for Adaptive and Efficient Image Interpolation [64.34935748707673]
  Recent deep neural networks (DNNs) have made impressive progress in performance by introducing learned data priors. We propose a novel method of Learning Resampling (termed LeRF) which takes advantage of both the structural priors learned by DNNs and the locally continuous assumption. LeRF assigns spatially varying resampling functions to input image pixels and learns to predict the shapes of these resampling functions with a neural network.
  arXiv  Detail & Related papers  (2024-07-13T16:09:45Z)
• Misalignment-Robust Frequency Distribution Loss for Image Transformation [51.0462138717502]
  This paper aims to address a common challenge in deep learning-based image transformation methods, such as image enhancement and super-resolution. We introduce a novel and simple Frequency Distribution Loss (FDL) for computing distribution distance within the frequency domain. Our method is empirically proven effective as a training constraint due to the thoughtful utilization of global information in the frequency domain.
  arXiv  Detail & Related papers  (2024-02-28T09:27:41Z)
• Efficient fine-grained road segmentation using superpixel-based CNN and CRF models [0.0]
  We propose a novel approach to utilise the advantages of CNNs for the task of road segmentation at reasonable computational effort. The proposed system obtained comparable performance among the top performing algorithms on the KITTI road benchmark.
  arXiv  Detail & Related papers  (2022-06-22T12:38:30Z)
• Continuous Conditional Random Field Convolution for Point Cloud Segmentation [12.154944192318936]
  conditional random fields (CRFs) are usually formulated as discrete models in label space to encourage label consistency. In this paper, we reconsider the CRF in feature space for point cloud segmentation because it can capture the structure of features well. Experiments on various point cloud benchmarks demonstrate the effectiveness and robustness of the proposed method.
  arXiv  Detail & Related papers  (2021-10-12T15:35:38Z)
• RSI-Net: Two-Stream Deep Neural Network Integrating GCN and Atrous CNN for Semantic Segmentation of High-resolution Remote Sensing Images [3.468780866037609]
  Two-stream deep neural network for semantic segmentation of remote sensing images (RSI-Net) is proposed in this paper. Experiments are implemented on the Vaihingen, Potsdam and Gaofen RSI datasets. Results demonstrate the superior performance of RSI-Net in terms of overall accuracy, F1 score and kappa coefficient when compared with six state-of-the-art RSI semantic segmentation methods.
  arXiv  Detail & Related papers  (2021-09-19T15:57:20Z)
• 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)
• Self-paced and self-consistent co-training for semi-supervised image segmentation [23.100800154116627]
  Deep co-training has been proposed as an effective approach for image segmentation when annotated data is scarce. In this paper, we improve existing approaches for semi-supervised segmentation with a self-paced and self-consistent co-training method.
  arXiv  Detail & Related papers  (2020-10-31T17:41:03Z)
• Image Segmentation Using Hybrid Representations [2.414172101538764]
  We introduce an end-to-end U-Net based network called DU-Net for medical image segmentation. SC are translation invariant and Lipschitz continuous to deformations which help DU-Net outperform other conventional CNN counterparts. The proposed method shows remarkable improvement over the basic U-Net with performance competitive to state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-04-15T13:07:35Z)
• Attentive CutMix: An Enhanced Data Augmentation Approach for Deep Learning Based Image Classification [58.20132466198622]
  We propose Attentive CutMix, a naturally enhanced augmentation strategy based on CutMix. In each training iteration, we choose the most descriptive regions based on the intermediate attention maps from a feature extractor. Our proposed method is simple yet effective, easy to implement and can boost the baseline significantly.
  arXiv  Detail & Related papers  (2020-03-29T15:01:05Z)
• Weakly-Supervised Semantic Segmentation by Iterative Affinity Learning [86.45526827323954]
  Weakly-supervised semantic segmentation is a challenging task as no pixel-wise label information is provided for training. We propose an iterative algorithm to learn such pairwise relations. We show that the proposed algorithm performs favorably against the state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-02-19T10:32:03Z)
• Image Fine-grained Inpainting [89.17316318927621]
  We present a one-stage model that utilizes dense combinations of dilated convolutions to obtain larger and more effective receptive fields. To better train this efficient generator, except for frequently-used VGG feature matching loss, we design a novel self-guided regression loss. We also employ a discriminator with local and global branches to ensure local-global contents consistency.
  arXiv  Detail & Related papers  (2020-02-07T03:45:25Z)

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.