MRFP: Learning Generalizable Semantic Segmentation from Sim-2-Real with Multi-Resolution Feature Perturbation

• URL: http://arxiv.org/abs/2311.18331v2
• Date: Thu, 28 Mar 2024 13:27:33 GMT
• Title: MRFP: Learning Generalizable Semantic Segmentation from Sim-2-Real with Multi-Resolution Feature Perturbation
• Authors: Sumanth Udupa, Prajwal Gurunath, Aniruddh Sikdar, Suresh Sundaram,
• Abstract summary: We propose a novel MultiResolution Feature Perturbation (MRFP) technique to randomize domain-specific fine-grained features and perturb style of coarse features. MRFP helps state-of-the-art deep neural networks to learn robust domain invariant features for simulation-to-real semantic segmentation.
• Score: 2.0293118701268154
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Deep neural networks have shown exemplary performance on semantic scene understanding tasks on source domains, but due to the absence of style diversity during training, enhancing performance on unseen target domains using only single source domain data remains a challenging task. Generation of simulated data is a feasible alternative to retrieving large style-diverse real-world datasets as it is a cumbersome and budget-intensive process. However, the large domain-specfic inconsistencies between simulated and real-world data pose a significant generalization challenge in semantic segmentation. In this work, to alleviate this problem, we propose a novel MultiResolution Feature Perturbation (MRFP) technique to randomize domain-specific fine-grained features and perturb style of coarse features. Our experimental results on various urban-scene segmentation datasets clearly indicate that, along with the perturbation of style-information, perturbation of fine-feature components is paramount to learn domain invariant robust feature maps for semantic segmentation models. MRFP is a simple and computationally efficient, transferable module with no additional learnable parameters or objective functions, that helps state-of-the-art deep neural networks to learn robust domain invariant features for simulation-to-real semantic segmentation.

Related papers

• FANet: Feature Amplification Network for Semantic Segmentation in Cluttered Background [9.970265640589966]
  Existing deep learning approaches leave out the semantic cues that are crucial in semantic segmentation present in complex scenarios. We propose a feature amplification network (FANet) as a backbone network that incorporates semantic information using a novel feature enhancement module at multi-stages. Our experimental results demonstrate the state-of-the-art performance compared to existing methods.
  arXiv  Detail & Related papers  (2024-07-12T15:57:52Z)
• StyDeSty: Min-Max Stylization and Destylization for Single Domain Generalization [85.18995948334592]
  Single domain generalization (single DG) aims at learning a robust model generalizable to unseen domains from only one training domain. State-of-the-art approaches have mostly relied on data augmentations, such as adversarial perturbation and style enhancement, to synthesize new data. We propose emphStyDeSty, which explicitly accounts for the alignment of the source and pseudo domains in the process of data augmentation.
  arXiv  Detail & Related papers  (2024-06-01T02:41:34Z)
• Selective Domain-Invariant Feature for Generalizable Deepfake Detection [21.671221284842847]
  We propose a novel framework which reduces the sensitivity to face forgery by fusing content features and styles. Both qualitative and quantitative results in existing benchmarks and proposals demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2024-03-19T13:09:19Z)
• A Novel Cross-Perturbation for Single Domain Generalization [54.612933105967606]
  Single domain generalization aims to enhance the ability of the model to generalize to unknown domains when trained on a single source domain. The limited diversity in the training data hampers the learning of domain-invariant features, resulting in compromised generalization performance. We propose CPerb, a simple yet effective cross-perturbation method to enhance the diversity of the training data.
  arXiv  Detail & Related papers  (2023-08-02T03:16:12Z)
• Generalized Semantic Segmentation by Self-Supervised Source Domain Projection and Multi-Level Contrastive Learning [79.0660895390689]
  Deep networks trained on the source domain show degraded performance when tested on unseen target domain data. We propose a Domain Projection and Contrastive Learning (DPCL) approach for generalized semantic segmentation.
  arXiv  Detail & Related papers  (2023-03-03T13:07:14Z)
• One-Shot Domain Adaptive and Generalizable Semantic Segmentation with Class-Aware Cross-Domain Transformers [96.51828911883456]
  Unsupervised sim-to-real domain adaptation (UDA) for semantic segmentation aims to improve the real-world test performance of a model trained on simulated data. Traditional UDA often assumes that there are abundant unlabeled real-world data samples available during training for the adaptation. We explore the one-shot unsupervised sim-to-real domain adaptation (OSUDA) and generalization problem, where only one real-world data sample is available.
  arXiv  Detail & Related papers  (2022-12-14T15:54:15Z)
• Unsupervised domain adaptation semantic segmentation of high-resolution remote sensing imagery with invariant domain-level context memory [10.210120085157161]
  This study proposes a novel unsupervised domain adaptation semantic segmentation network (MemoryAdaptNet) for the semantic segmentation of HRS imagery. MemoryAdaptNet constructs an output space adversarial learning scheme to bridge the domain distribution discrepancy between source domain and target domain. Experiments under three cross-domain tasks indicate that our proposed MemoryAdaptNet is remarkably superior to the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-08-16T12:35:57Z)
• Consistency and Diversity induced Human Motion Segmentation [231.36289425663702]
  We propose a novel Consistency and Diversity induced human Motion (CDMS) algorithm. Our model factorizes the source and target data into distinct multi-layer feature spaces. A multi-mutual learning strategy is carried out to reduce the domain gap between the source and target data.
  arXiv  Detail & Related papers  (2022-02-10T06:23:56Z)
• TDACNN: Target-domain-free Domain Adaptation Convolutional Neural Network for Drift Compensation in Gas Sensors [6.451060076703026]
  In this paper, deep learning based on a target-domain-free domain adaptation convolutional neural network (TDACNN) is proposed. The main concept is that CNNs extract not only the domain-specific features of samples but also the domain-invariant features underlying both the source and target domains. Experiments on two datasets drift under different settings demonstrate the superiority of TDACNN compared with several state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-10-14T16:30:17Z)
• 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)
• Spatial Attention Pyramid Network for Unsupervised Domain Adaptation [66.75008386980869]
  Unsupervised domain adaptation is critical in various computer vision tasks. We design a new spatial attention pyramid network for unsupervised domain adaptation. Our method performs favorably against the state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2020-03-29T09:03:23Z)

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.