2PCNet: Two-Phase Consistency Training for Day-to-Night Unsupervised Domain Adaptive Object Detection

• URL: http://arxiv.org/abs/2303.13853v1
• Date: Fri, 24 Mar 2023 08:22:41 GMT
• Title: 2PCNet: Two-Phase Consistency Training for Day-to-Night Unsupervised Domain Adaptive Object Detection
• Authors: Mikhail Kennerley, Jian-Gang Wang, Bharadwaj Veeravalli, Robby T. Tan
• Abstract summary: This paper proposes a two-phase consistency unsupervised domain adaptation network, 2PCNet, to address these issues. Experiments on publicly available datasets demonstrate that our method achieves superior results to state-of-the-art methods by 20%.
• Score: 30.114398123450236
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Object detection at night is a challenging problem due to the absence of night image annotations. Despite several domain adaptation methods, achieving high-precision results remains an issue. False-positive error propagation is still observed in methods using the well-established student-teacher framework, particularly for small-scale and low-light objects. This paper proposes a two-phase consistency unsupervised domain adaptation network, 2PCNet, to address these issues. The network employs high-confidence bounding-box predictions from the teacher in the first phase and appends them to the student's region proposals for the teacher to re-evaluate in the second phase, resulting in a combination of high and low confidence pseudo-labels. The night images and pseudo-labels are scaled-down before being used as input to the student, providing stronger small-scale pseudo-labels. To address errors that arise from low-light regions and other night-related attributes in images, we propose a night-specific augmentation pipeline called NightAug. This pipeline involves applying random augmentations, such as glare, blur, and noise, to daytime images. Experiments on publicly available datasets demonstrate that our method achieves superior results to state-of-the-art methods by 20\%, and to supervised models trained directly on the target data.

Related papers

• TrajSSL: Trajectory-Enhanced Semi-Supervised 3D Object Detection [59.498894868956306]
  Pseudo-labeling approaches to semi-supervised learning adopt a teacher-student framework. We leverage pre-trained motion-forecasting models to generate object trajectories on pseudo-labeled data. Our approach improves pseudo-label quality in two distinct manners.
  arXiv  Detail & Related papers  (2024-09-17T05:35:00Z)
• Domain-Adaptive 2D Human Pose Estimation via Dual Teachers in Extremely Low-Light Conditions [65.0109231252639]
  Recent studies on low-light pose estimation require the use of paired well-lit and low-light images with ground truths for training. Our primary novelty lies in leveraging two complementary-teacher networks to generate more reliable pseudo labels. Our method achieves 6.8% (2.4 AP) improvement over the state-of-the-art (SOTA) method.
  arXiv  Detail & Related papers  (2024-07-22T08:09:14Z)
• Learning Camouflaged Object Detection from Noisy Pseudo Label [60.9005578956798]
  This paper introduces the first weakly semi-supervised Camouflaged Object Detection (COD) method. It aims for budget-efficient and high-precision camouflaged object segmentation with an extremely limited number of fully labeled images. We propose a noise correction loss that facilitates the model's learning of correct pixels in the early learning stage. When using only 20% of fully labeled data, our method shows superior performance over the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-07-18T04:53:51Z)
• Semi-Supervised 2D Human Pose Estimation Driven by Position Inconsistency Pseudo Label Correction Module [74.80776648785897]
  The previous method ignored two problems: (i) When conducting interactive training between large model and lightweight model, the pseudo label of lightweight model will be used to guide large models. We propose a semi-supervised 2D human pose estimation framework driven by a position inconsistency pseudo label correction module (SSPCM) To further improve the performance of the student model, we use the semi-supervised Cut-Occlude based on pseudo keypoint perception to generate more hard and effective samples.
  arXiv  Detail & Related papers  (2023-03-08T02:57:05Z)
• Domain Adaptive Hand Keypoint and Pixel Localization in the Wild [40.71379707068579]
  We aim to improve the performance of regressing hand keypoints and segmenting pixel-level hand masks under new imaging conditions. Our method improves by 4% the multi-task score on HO3D compared to the latest adversarial adaptation method.
  arXiv  Detail & Related papers  (2022-03-16T01:32:21Z)
• Activation to Saliency: Forming High-Quality Labels for Unsupervised Salient Object Detection [54.92703325989853]
  We propose a two-stage Activation-to-Saliency (A2S) framework that effectively generates high-quality saliency cues. No human annotations are involved in our framework during the whole training process. Our framework reports significant performance compared with existing USOD methods.
  arXiv  Detail & Related papers  (2021-12-07T11:54:06Z)
• Seeing BDD100K in dark: Single-Stage Night-time Object Detection via Continual Fourier Contrastive Learning [3.4012007729454816]
  Night-time object detection has been studied only sparsely, that too, via non-uniform evaluation protocols among the limited available papers. In this paper, we bridge these 3 gaps:. Lack of an uniform evaluation protocol (using a single-stage detector, due to its efficacy, and efficiency);. A choice of dataset for benchmarking night-time object detection, and. A novel method to address the limitations of current alternatives.
  arXiv  Detail & Related papers  (2021-12-06T09:28:45Z)
• W2WNet: a two-module probabilistic Convolutional Neural Network with embedded data cleansing functionality [2.695466667982714]
  Wise2WipedNet (W2WNet) is a new two- module Convolutional Neural Network. A Wise module exploits Bayesian inference to identify and discard spurious images during the training. A Wiped module takes care of the final classification while broadcasting information on the prediction confidence at inference time.
  arXiv  Detail & Related papers  (2021-03-24T11:28:59Z)
• An Empirical Study of the Collapsing Problem in Semi-Supervised 2D Human Pose Estimation [80.02124918255059]
  Semi-supervised learning aims to boost the accuracy of a model by exploring unlabeled images. We learn two networks to mutually teach each other. The more reliable predictions on easy images in each network are used to teach the other network to learn about the corresponding hard images.
  arXiv  Detail & Related papers  (2020-11-25T03:29:52Z)
• Extreme Consistency: Overcoming Annotation Scarcity and Domain Shifts [2.707399740070757]
  Supervised learning has proved effective for medical image analysis. It can utilize only the small labeled portion of data. It fails to leverage the large amounts of unlabeled data that is often available in medical image datasets.
  arXiv  Detail & Related papers  (2020-04-15T15:32:01Z)

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.