Lookahead Adversarial Learning for Near Real-Time Semantic Segmentation

• URL: http://arxiv.org/abs/2006.11227v3
• Date: Thu, 21 Jan 2021 15:00:09 GMT
• Title: Lookahead Adversarial Learning for Near Real-Time Semantic Segmentation
• Authors: Hadi Jamali-Rad, Attila Szabo
• Abstract summary: We build a conditional adversarial network with a state-of-the-art segmentation model (DeepLabv3+) at its core. We focus on semantic segmentation models that run fast at inference for near real-time field applications.
• Score: 2.538209532048867
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic segmentation is one of the most fundamental problems in computer vision with significant impact on a wide variety of applications. Adversarial learning is shown to be an effective approach for improving semantic segmentation quality by enforcing higher-level pixel correlations and structural information. However, state-of-the-art semantic segmentation models cannot be easily plugged into an adversarial setting because they are not designed to accommodate convergence and stability issues in adversarial networks. We bridge this gap by building a conditional adversarial network with a state-of-the-art segmentation model (DeepLabv3+) at its core. To battle the stability issues, we introduce a novel lookahead adversarial learning (LoAd) approach with an embedded label map aggregation module. We focus on semantic segmentation models that run fast at inference for near real-time field applications. Through extensive experimentation, we demonstrate that the proposed solution can alleviate divergence issues in an adversarial semantic segmentation setting and results in considerable performance improvements (+5% in some classes) on the baseline for three standard datasets.

Related papers

• Joint semi-supervised and contrastive learning enables zero-shot domain-adaptation and multi-domain segmentation [1.5393913074555419]
  SegCLR is a versatile framework designed to segment volumetric images across different domains. We demonstrate the superior performance of SegCLR through a comprehensive evaluation.
  arXiv  Detail & Related papers  (2024-05-08T18:10:59Z)
• Separating common from salient patterns with Contrastive Representation Learning [2.250968907999846]
  Contrastive Analysis aims at separating common factors of variation between two datasets. Current models based on Variational Auto-Encoders have shown poor performance in learning semantically-expressive representations. We propose to leverage the ability of Contrastive Learning to learn semantically expressive representations well adapted for Contrastive Analysis.
  arXiv  Detail & Related papers  (2024-02-19T08:17:13Z)
• Learning to Generate Training Datasets for Robust Semantic Segmentation [37.9308918593436]
  We propose a novel approach to improve the robustness of semantic segmentation techniques. We design Robusta, a novel conditional generative adversarial network to generate realistic and plausible perturbed images. Our results suggest that this approach could be valuable in safety-critical applications.
  arXiv  Detail & Related papers  (2023-08-01T10:02:26Z)
• Learning Context-aware Classifier for Semantic Segmentation [88.88198210948426]
  In this paper, contextual hints are exploited via learning a context-aware classifier. Our method is model-agnostic and can be easily applied to generic segmentation models. With only negligible additional parameters and +2% inference time, decent performance gain has been achieved on both small and large models.
  arXiv  Detail & Related papers  (2023-03-21T07:00:35Z)
• A Threefold Review on Deep Semantic Segmentation: Efficiency-oriented, Temporal and Depth-aware design [77.34726150561087]
  We conduct a survey on the most relevant and recent advances in Deep Semantic in the context of vision for autonomous vehicles. Our main objective is to provide a comprehensive discussion on the main methods, advantages, limitations, results and challenges faced from each perspective.
  arXiv  Detail & Related papers  (2023-03-08T01:29:55Z)
• Self-Ensembling GAN for Cross-Domain Semantic Segmentation [107.27377745720243]
  This paper proposes a self-ensembling generative adversarial network (SE-GAN) exploiting cross-domain data for semantic segmentation. In SE-GAN, a teacher network and a student network constitute a self-ensembling model for generating semantic segmentation maps, which together with a discriminator, forms a GAN. Despite its simplicity, we find SE-GAN can significantly boost the performance of adversarial training and enhance the stability of the model.
  arXiv  Detail & Related papers  (2021-12-15T09:50:25Z)
• Semi-supervised Domain Adaptive Structure Learning [72.01544419893628]
  Semi-supervised domain adaptation (SSDA) is a challenging problem requiring methods to overcome both 1) overfitting towards poorly annotated data and 2) distribution shift across domains. We introduce an adaptive structure learning method to regularize the cooperation of SSL and DA.
  arXiv  Detail & Related papers  (2021-12-12T06:11:16Z)
• Learning Robust Representation for Clustering through Locality Preserving Variational Discriminative Network [16.259673823482665]
  Variational Deep Embedding achieves great success in various clustering tasks. VaDE suffers from two problems: 1) it is fragile to the input noise; 2) it ignores the locality information between the neighboring data points. We propose a joint learning framework that improves VaDE with a robust embedding discriminator and a local structure constraint.
  arXiv  Detail & Related papers  (2020-12-25T02:31:55Z)
• Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation [169.82760468633236]
  We propose to build the pixel-level cycle association between source and target pixel pairs. Our method can be trained end-to-end in one stage and introduces no additional parameters.
  arXiv  Detail & Related papers  (2020-10-31T00:11:36Z)
• Towards Adaptive Semantic Segmentation by Progressive Feature Refinement [16.40758125170239]
  We propose an innovative progressive feature refinement framework, along with domain adversarial learning to boost the transferability of segmentation networks. As a result, the segmentation models trained with source domain images can be transferred to a target domain without significant performance degradation.
  arXiv  Detail & Related papers  (2020-09-30T04:17:48Z)
• Affinity Space Adaptation for Semantic Segmentation Across Domains [57.31113934195595]
  In this paper, we address the problem of unsupervised domain adaptation (UDA) in semantic segmentation. Motivated by the fact that source and target domain have invariant semantic structures, we propose to exploit such invariance across domains. We develop two affinity space adaptation strategies: affinity space cleaning and adversarial affinity space alignment.
  arXiv  Detail & Related papers  (2020-09-26T10:28:11Z)

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.