Amplitude Spectrum Transformation for Open Compound Domain Adaptive Semantic Segmentation

• URL: http://arxiv.org/abs/2202.04287v1
• Date: Wed, 9 Feb 2022 05:40:34 GMT
• Title: Amplitude Spectrum Transformation for Open Compound Domain Adaptive Semantic Segmentation
• Authors: Jogendra Nath Kundu, Akshay Kulkarni, Suvaansh Bhambri, Varun Jampani, R. Venkatesh Babu
• Abstract summary: Open compound domain adaptation (OCDA) has emerged as a practical adaptation setting. We propose a novel feature space Amplitude Spectrum Transformation (AST)
• Score: 62.68759523116924
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Open compound domain adaptation (OCDA) has emerged as a practical adaptation setting which considers a single labeled source domain against a compound of multi-modal unlabeled target data in order to generalize better on novel unseen domains. We hypothesize that an improved disentanglement of domain-related and task-related factors of dense intermediate layer features can greatly aid OCDA. Prior-arts attempt this indirectly by employing adversarial domain discriminators on the spatial CNN output. However, we find that latent features derived from the Fourier-based amplitude spectrum of deep CNN features hold a more tractable mapping with domain discrimination. Motivated by this, we propose a novel feature space Amplitude Spectrum Transformation (AST). During adaptation, we employ the AST auto-encoder for two purposes. First, carefully mined source-target instance pairs undergo a simulation of cross-domain feature stylization (AST-Sim) at a particular layer by altering the AST-latent. Second, AST operating at a later layer is tasked to normalize (AST-Norm) the domain content by fixing its latent to a mean prototype. Our simplified adaptation technique is not only clustering-free but also free from complex adversarial alignment. We achieve leading performance against the prior arts on the OCDA scene segmentation benchmarks.

Related papers

• Language-Guided Instance-Aware Domain-Adaptive Panoptic Segmentation [44.501770535446624]
  Key challenge in panoptic domain adaptation is reducing the domain gap between a labeled source and an unlabeled target domain. We focus on incorporating instance-level adaptation via a novel cross-domain mixing strategy IMix. We present an end-to-end model incorporating these two mechanisms called LIDAPS, achieving state-of-the-art results on all popular panoptic UDA benchmarks.
  arXiv  Detail & Related papers  (2024-04-04T20:42:49Z)
• CDA: Contrastive-adversarial Domain Adaptation [11.354043674822451]
  We propose a two-stage model for domain adaptation called textbfContrastive-adversarial textbfDomain textbfAdaptation textbf(CDA). While the adversarial component facilitates domain-level alignment, two-stage contrastive learning exploits class information to achieve higher intra-class compactness across domains.
  arXiv  Detail & Related papers  (2023-01-10T07:43:21Z)
• Domain-Agnostic Prior for Transfer Semantic Segmentation [197.9378107222422]
  Unsupervised domain adaptation (UDA) is an important topic in the computer vision community. We present a mechanism that regularizes cross-domain representation learning with a domain-agnostic prior (DAP) Our research reveals that UDA benefits much from better proxies, possibly from other data modalities.
  arXiv  Detail & Related papers  (2022-04-06T09:13:25Z)
• Exploring Sequence Feature Alignment for Domain Adaptive Detection Transformers [141.70707071815653]
  We propose a novel Sequence Feature Alignment (SFA) method that is specially designed for the adaptation of detection transformers. SFA consists of a domain query-based feature alignment (DQFA) module and a token-wise feature alignment (TDA) module. Experiments on three challenging benchmarks show that SFA outperforms state-of-the-art domain adaptive object detection methods.
  arXiv  Detail & Related papers  (2021-07-27T07:17:12Z)
• AFAN: Augmented Feature Alignment Network for Cross-Domain Object Detection [90.18752912204778]
  Unsupervised domain adaptation for object detection is a challenging problem with many real-world applications. We propose a novel augmented feature alignment network (AFAN) which integrates intermediate domain image generation and domain-adversarial training. Our approach significantly outperforms the state-of-the-art methods on standard benchmarks for both similar and dissimilar domain adaptations.
  arXiv  Detail & Related papers  (2021-06-10T05:01:20Z)
• Semi-Supervised Domain Adaptation via Adaptive and Progressive Feature Alignment [32.77436219094282]
  SSDAS employs a few labeled target samples as anchors for adaptive and progressive feature alignment between labeled source samples and unlabeled target samples. In addition, we replace the dissimilar source features by high-confidence target features continuously during the iterative training process. Extensive experiments show the proposed SSDAS greatly outperforms a number of baselines.
  arXiv  Detail & Related papers  (2021-06-05T09:12:50Z)
• Domain Conditioned Adaptation Network [90.63261870610211]
  We propose a Domain Conditioned Adaptation Network (DCAN) to excite distinct convolutional channels with a domain conditioned channel attention mechanism. This is the first work to explore the domain-wise convolutional channel activation for deep DA networks.
  arXiv  Detail & Related papers  (2020-05-14T04:23:24Z)
• Bi-Directional Generation for Unsupervised Domain Adaptation [61.73001005378002]
  Unsupervised domain adaptation facilitates the unlabeled target domain relying on well-established source domain information. Conventional methods forcefully reducing the domain discrepancy in the latent space will result in the destruction of intrinsic data structure. We propose a Bi-Directional Generation domain adaptation model with consistent classifiers interpolating two intermediate domains to bridge source and target domains.
  arXiv  Detail & Related papers  (2020-02-12T09:45:39Z)

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.