Domain adaptive pose estimation via multi-level alignment

• URL: http://arxiv.org/abs/2404.14885v2
• Date: Thu, 25 Apr 2024 07:38:25 GMT
• Title: Domain adaptive pose estimation via multi-level alignment
• Authors: Yugan Chen, Lin Zhao, Yalong Xu, Honglei Zu, Xiaoqi An, Guangyu Li,
• Abstract summary: Domain adaptive pose estimation aims to enable deep models trained on source domain (synthesized) datasets produce similar results on the target domain (real-world) datasets. We propose a multi-level domain adaptation aproach, which aligns different domains at the image, feature, and pose levels. Experimental results demonstrate that significant imrovement can be achieved by the proposed multi-level alignment method in pose estimation.
• Score: 7.107028574274364
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Domain adaptive pose estimation aims to enable deep models trained on source domain (synthesized) datasets produce similar results on the target domain (real-world) datasets. The existing methods have made significant progress by conducting image-level or feature-level alignment. However, only aligning at a single level is not sufficient to fully bridge the domain gap and achieve excellent domain adaptive results. In this paper, we propose a multi-level domain adaptation aproach, which aligns different domains at the image, feature, and pose levels. Specifically, we first utilize image style transer to ensure that images from the source and target domains have a similar distribution. Subsequently, at the feature level, we employ adversarial training to make the features from the source and target domains preserve domain-invariant characeristics as much as possible. Finally, at the pose level, a self-supervised approach is utilized to enable the model to learn diverse knowledge, implicitly addressing the domain gap. Experimental results demonstrate that significant imrovement can be achieved by the proposed multi-level alignment method in pose estimation, which outperforms previous state-of-the-art in human pose by up to 2.4% and animal pose estimation by up to 3.1% for dogs and 1.4% for sheep.

Related papers

• Exploring selective image matching methods for zero-shot and few-sample unsupervised domain adaptation of urban canopy prediction [1.2277343096128712]
  Methods for adapting a trained UNet which predicts canopy cover and height to a new geographic setting using remotely sensed data. We find that the selective aligned data-based image matching methods produce promising results in a zero-shot setting. The best performing methods were pixel distribution adaptation and fourier domain adaptation on the canopy cover and height tasks respectively.
  arXiv  Detail & Related papers  (2024-04-16T14:52:15Z)
• I2F: A Unified Image-to-Feature Approach for Domain Adaptive Semantic Segmentation [55.633859439375044]
  Unsupervised domain adaptation (UDA) for semantic segmentation is a promising task freeing people from heavy annotation work. Key idea to tackle this problem is to perform both image-level and feature-level adaptation jointly. This paper proposes a novel UDA pipeline for semantic segmentation that unifies image-level and feature-level adaptation.
  arXiv  Detail & Related papers  (2023-01-03T15:19:48Z)
• A Unified Framework for Domain Adaptive Pose Estimation [70.54942818742]
  We propose a unified framework that generalizes well on various domain adaptive pose estimation problems. Our method outperforms existing baselines on human pose estimation by up to 4.5 percent points (pp), hand pose estimation by up to 7.4 pp, and animal pose estimation by up to 4.8 pp for dogs and 3.3 pp for sheep.
  arXiv  Detail & Related papers  (2022-04-01T02:47:31Z)
• Gradual Domain Adaptation via Self-Training of Auxiliary Models [50.63206102072175]
  Domain adaptation becomes more challenging with increasing gaps between source and target domains. We propose self-training of auxiliary models (AuxSelfTrain) that learns models for intermediate domains. Experiments on benchmark datasets of unsupervised and semi-supervised domain adaptation verify its efficacy.
  arXiv  Detail & Related papers  (2021-06-18T03:15:25Z)
• FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation [26.929772844572213]
  We introduce a fixed ratio-based mixup to augment multiple intermediate domains between the source and target domain. We train the source-dominant model and the target-dominant model that have complementary characteristics. Through our proposed methods, the models gradually transfer domain knowledge from the source to the target domain.
  arXiv  Detail & Related papers  (2020-11-18T11:58:19Z)
• Domain Adaptation for Semantic Parsing [68.81787666086554]
  We propose a novel semantic for domain adaptation, where we have much fewer annotated data in the target domain compared to the source domain. Our semantic benefits from a two-stage coarse-to-fine framework, thus can provide different and accurate treatments for the two stages. Experiments on a benchmark dataset show that our method consistently outperforms several popular domain adaptation strategies.
  arXiv  Detail & Related papers  (2020-06-23T14:47:41Z)
• 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)
• Unsupervised Domain Adaptive Object Detection using Forward-Backward Cyclic Adaptation [13.163271874039191]
  We present a novel approach to perform the unsupervised domain adaptation for object detection through forward-backward cyclic (FBC) training. Recent adversarial training based domain adaptation methods have shown their effectiveness on minimizing domain discrepancy via marginal feature distributions alignment. We propose Forward-Backward Cyclic Adaptation, which iteratively computes adaptation from source to target via backward hopping and from target to source via forward passing.
  arXiv  Detail & Related papers  (2020-02-03T06:24:58Z)
• CrDoCo: Pixel-level Domain Transfer with Cross-Domain Consistency [119.45667331836583]
  Unsupervised domain adaptation algorithms aim to transfer the knowledge learned from one domain to another. We present a novel pixel-wise adversarial domain adaptation algorithm.
  arXiv  Detail & Related papers  (2020-01-09T19:00:35Z)

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.