Unsupervised Domain Adaptive Salient Object Detection Through Uncertainty-Aware Pseudo-Label Learning

• URL: http://arxiv.org/abs/2202.13170v1
• Date: Sat, 26 Feb 2022 16:03:55 GMT
• Title: Unsupervised Domain Adaptive Salient Object Detection Through Uncertainty-Aware Pseudo-Label Learning
• Authors: Pengxiang Yan, Ziyi Wu, Mengmeng Liu, Kun Zeng, Liang Lin, Guanbin Li
• Abstract summary: We propose to learn saliency from synthetic but clean labels, which naturally has higher pixel-labeling quality without the effort of manual annotations. We show that our proposed method outperforms the existing state-of-the-art deep unsupervised SOD methods on several benchmark datasets.
• Score: 104.00026716576546
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in deep learning significantly boost the performance of salient object detection (SOD) at the expense of labeling larger-scale per-pixel annotations. To relieve the burden of labor-intensive labeling, deep unsupervised SOD methods have been proposed to exploit noisy labels generated by handcrafted saliency methods. However, it is still difficult to learn accurate saliency details from rough noisy labels. In this paper, we propose to learn saliency from synthetic but clean labels, which naturally has higher pixel-labeling quality without the effort of manual annotations. Specifically, we first construct a novel synthetic SOD dataset by a simple copy-paste strategy. Considering the large appearance differences between the synthetic and real-world scenarios, directly training with synthetic data will lead to performance degradation on real-world scenarios. To mitigate this problem, we propose a novel unsupervised domain adaptive SOD method to adapt between these two domains by uncertainty-aware self-training. Experimental results show that our proposed method outperforms the existing state-of-the-art deep unsupervised SOD methods on several benchmark datasets, and is even comparable to fully-supervised ones.

Related papers

• A Discrepancy Aware Framework for Robust Anomaly Detection [51.710249807397695]
  We present a Discrepancy Aware Framework (DAF), which demonstrates robust performance consistently with simple and cheap strategies. Our method leverages an appearance-agnostic cue to guide the decoder in identifying defects, thereby alleviating its reliance on synthetic appearance. Under the simple synthesis strategies, it outperforms existing methods by a large margin. Furthermore, it also achieves the state-of-the-art localization performance.
  arXiv  Detail & Related papers  (2023-10-11T15:21:40Z)
• All Points Matter: Entropy-Regularized Distribution Alignment for Weakly-supervised 3D Segmentation [67.30502812804271]
  Pseudo-labels are widely employed in weakly supervised 3D segmentation tasks where only sparse ground-truth labels are available for learning. We propose a novel learning strategy to regularize the generated pseudo-labels and effectively narrow the gaps between pseudo-labels and model predictions.
  arXiv  Detail & Related papers  (2023-05-25T08:19:31Z)
• Hard-aware Instance Adaptive Self-training for Unsupervised Cross-domain Semantic Segmentation [18.807921765977415]
  We propose a hard-aware instance adaptive self-training framework for UDA on the task of semantic segmentation. We develop a novel pseudo-label generation strategy with an instance adaptive selector. Experiments on GTA5 to Cityscapes, SYNTHIA to Cityscapes, and Cityscapes to Oxford RobotCar demonstrate the superior performance of our approach.
  arXiv  Detail & Related papers  (2023-02-14T11:52:26Z)
• Complementing Semi-Supervised Learning with Uncertainty Quantification [6.612035830987296]
  We propose a novel unsupervised uncertainty-aware objective that relies on aleatoric and epistemic uncertainty quantification. Our results outperform the state-of-the-art results on complex datasets such as CIFAR-100 and Mini-ImageNet.
  arXiv  Detail & Related papers  (2022-07-22T00:15:02Z)
• Texture-guided Saliency Distilling for Unsupervised Salient Object Detection [67.10779270290305]
  We propose a novel USOD method to mine rich and accurate saliency knowledge from both easy and hard samples. Our method achieves state-of-the-art USOD performance on RGB, RGB-D, RGB-T, and video SOD benchmarks.
  arXiv  Detail & Related papers  (2022-07-13T02:01:07Z)
• Towards Harnessing Feature Embedding for Robust Learning with Noisy Labels [44.133307197696446]
  The memorization effect of deep neural networks (DNNs) plays a pivotal role in recent label noise learning methods. We propose a novel feature embedding-based method for deep learning with label noise, termed LabEl NoiseDilution (LEND)
  arXiv  Detail & Related papers  (2022-06-27T02:45:09Z)
• STRUDEL: Self-Training with Uncertainty Dependent Label Refinement across Domains [4.812718493682454]
  We propose an unsupervised domain adaptation (UDA) approach for white matter hyperintensity (WMH) segmentation. We propose to predict the uncertainty of pseudo labels and integrate it in the training process with an uncertainty-guided loss function to highlight labels with high certainty. Our results on WMH segmentation across datasets demonstrate the significant improvement of STRUDEL with respect to standard self-training.
  arXiv  Detail & Related papers  (2021-04-23T13:46:26Z)
• Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model [80.91927573604438]
  This paper proposes a simple yet universal probabilistic model, which explicitly relates noisy labels to their instances. Experiments on datasets with both synthetic and real-world label noise verify that the proposed method yields significant improvements on robustness.
  arXiv  Detail & Related papers  (2021-01-14T05:43:51Z)
• A Free Lunch for Unsupervised Domain Adaptive Object Detection without Source Data [69.091485888121]
  Unsupervised domain adaptation assumes that source and target domain data are freely available and usually trained together to reduce the domain gap. We propose a source data-free domain adaptive object detection (SFOD) framework via modeling it into a problem of learning with noisy labels.
  arXiv  Detail & Related papers  (2020-12-10T01:42:35Z)
• ESL: Entropy-guided Self-supervised Learning for Domain Adaptation in Semantic Segmentation [35.03150829133562]
  We propose Entropy-guided Self-supervised Learning, leveraging entropy as the confidence indicator for producing more accurate pseudo-labels. On different UDA benchmarks, ESL consistently outperforms strong SSL baselines and achieves state-of-the-art results.
  arXiv  Detail & Related papers  (2020-06-15T18:10:09Z)

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.