GLAD: Global-Local View Alignment and Background Debiasing for Unsupervised Video Domain Adaptation with Large Domain Gap

• URL: http://arxiv.org/abs/2311.12467v2
• Date: Wed, 22 Nov 2023 06:01:46 GMT
• Title: GLAD: Global-Local View Alignment and Background Debiasing for Unsupervised Video Domain Adaptation with Large Domain Gap
• Authors: Hyogun Lee, Kyungho Bae, Seong Jong Ha, Yumin Ko, Gyeong-Moon Park, Jinwoo Choi
• Abstract summary: We tackle the challenging problem of unsupervised video domain adaptation (UVDA) for action recognition. We introduce a novel UVDA scenario, denoted as Kinetics->BABEL, with a more considerable domain gap in terms of both temporal dynamics and background shifts. We empirically validate that the proposed method shows significant improvement over the existing methods on the Kinetics->BABEL dataset with a large domain gap.
• Score: 9.284539958686368
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this work, we tackle the challenging problem of unsupervised video domain adaptation (UVDA) for action recognition. We specifically focus on scenarios with a substantial domain gap, in contrast to existing works primarily deal with small domain gaps between labeled source domains and unlabeled target domains. To establish a more realistic setting, we introduce a novel UVDA scenario, denoted as Kinetics->BABEL, with a more considerable domain gap in terms of both temporal dynamics and background shifts. To tackle the temporal shift, i.e., action duration difference between the source and target domains, we propose a global-local view alignment approach. To mitigate the background shift, we propose to learn temporal order sensitive representations by temporal order learning and background invariant representations by background augmentation. We empirically validate that the proposed method shows significant improvement over the existing methods on the Kinetics->BABEL dataset with a large domain gap. The code is available at https://github.com/KHUVLL/GLAD.

Related papers

• Contrastive Domain Adaptation for Time-Series via Temporal Mixup [14.723714504015483]
  We propose a novel lightweight contrastive domain adaptation framework called CoTMix for time-series data. Specifically, we propose a novel temporal mixup strategy to generate two intermediate augmented views for the source and target domains. Our approach can significantly outperform all state-of-the-art UDA methods.
  arXiv  Detail & Related papers  (2022-12-03T06:53:38Z)
• An End-to-end Supervised Domain Adaptation Framework for Cross-Domain Change Detection [29.70695339406896]
  We propose an end-to-end Supervised Domain Adaptation framework for cross-domain Change Detection. Our SDACD presents collaborative adaptations from both image and feature perspectives with supervised learning. Our framework pushes several representative baseline models up to new State-Of-The-Art records.
  arXiv  Detail & Related papers  (2022-04-01T01:35:30Z)
• Self-supervised Autoregressive Domain Adaptation for Time Series Data [9.75443057146649]
  Unsupervised domain adaptation (UDA) has successfully addressed the domain shift problem for visual applications. These approaches may have limited performance for time series data due to the following reasons. We propose a Self-supervised Autoregressive Domain Adaptation (SLARDA) framework to address these limitations.
  arXiv  Detail & Related papers  (2021-11-29T08:17:23Z)
• PIT: Position-Invariant Transform for Cross-FoV Domain Adaptation [53.428312630479816]
  We observe that the Field of View (FoV) gap induces noticeable instance appearance differences between the source and target domains. Motivated by the observations, we propose the textbfPosition-Invariant Transform (PIT) to better align images in different domains.
  arXiv  Detail & Related papers  (2021-08-16T15:16:47Z)
• 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)
• 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)
• 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)
• 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)
• Mind the Gap: Enlarging the Domain Gap in Open Set Domain Adaptation [65.38975706997088]
  Open set domain adaptation (OSDA) assumes the presence of unknown classes in the target domain. We show that existing state-of-the-art methods suffer a considerable performance drop in the presence of larger domain gaps. We propose a novel framework to specifically address the larger domain gaps.
  arXiv  Detail & Related papers  (2020-03-08T14:20:24Z)

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.