Contrastive Learning and Cycle Consistency-based Transductive Transfer Learning for Target Annotation

• URL: http://arxiv.org/abs/2401.12340v1
• Date: Mon, 22 Jan 2024 20:08:57 GMT
• Title: Contrastive Learning and Cycle Consistency-based Transductive Transfer Learning for Target Annotation
• Authors: Shoaib Meraj Sami, Md Mahedi Hasan, Nasser M. Nasrabadi, Raghuveer Rao
• Abstract summary: We propose a hybrid contrastive learning base unpaired domain translation (H-CUT) network that achieves a significantly lower FID score. It incorporates both attention and entropy to emphasize the domain-specific region, a noisy feature mixup module to generate high variational synthetic negative patches, and a modulated noise contrastive estimation (MoNCE) loss to reweight all negative patches. The proposed C3TTL framework is effective in annotating civilian and military vehicles, as well as ship targets.
• Score: 11.883617702526193
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Annotating automatic target recognition (ATR) is a highly challenging task, primarily due to the unavailability of labeled data in the target domain. Hence, it is essential to construct an optimal target domain classifier by utilizing the labeled information of the source domain images. The transductive transfer learning (TTL) method that incorporates a CycleGAN-based unpaired domain translation network has been previously proposed in the literature for effective ATR annotation. Although this method demonstrates great potential for ATR, it severely suffers from lower annotation performance, higher Fr\'echet Inception Distance (FID) score, and the presence of visual artifacts in the synthetic images. To address these issues, we propose a hybrid contrastive learning base unpaired domain translation (H-CUT) network that achieves a significantly lower FID score. It incorporates both attention and entropy to emphasize the domain-specific region, a noisy feature mixup module to generate high variational synthetic negative patches, and a modulated noise contrastive estimation (MoNCE) loss to reweight all negative patches using optimal transport for better performance. Our proposed contrastive learning and cycle-consistency-based TTL (C3TTL) framework consists of two H-CUT networks and two classifiers. It simultaneously optimizes cycle-consistency, MoNCE, and identity losses. In C3TTL, two H-CUT networks have been employed through a bijection mapping to feed the reconstructed source domain images into a pretrained classifier to guide the optimal target domain classifier. Extensive experimental analysis conducted on three ATR datasets demonstrates that the proposed C3TTL method is effective in annotating civilian and military vehicles, as well as ship targets.

Related papers

• Progressive Multi-Level Alignments for Semi-Supervised Domain Adaptation SAR Target Recognition Using Simulated Data [3.1951121258423334]
  We develop an instance-prototype alignment (AIPA) strategy to push the source domain instances close to the corresponding target prototypes. We also develop an instance-prototype alignment (AIPA) strategy to push the source domain instances close to the corresponding target prototypes.
  arXiv  Detail & Related papers  (2024-11-07T13:53:13Z)
• TPN: Transferable Proto-Learning Network towards Few-shot Document-Level Relation Extraction [9.4094500796859]
  Few-shot document-level relation extraction suffers from poor performance due to cross-domain transferability of NOTA relation representation. We introduce a Transferable Proto-Learning Network (TPN) to address the challenging issue. TPN comprises three core components: Hybrid hierarchically encodes semantic content of input text combined with attention information to enhance the relation representations.
  arXiv  Detail & Related papers  (2024-10-01T05:37:31Z)
• Unsupervised Cross-Domain Image Retrieval via Prototypical Optimal Transport [24.141959555741696]
  Unsupervised cross-domain image retrieval aims to retrieve images sharing the same category across diverse domains without relying on labeled data. Prior approaches have typically decomposed the UCIR problem into two distinct tasks: intra-domain representation learning and cross-domain feature alignment. This paper introduces ProtoOT, a novel Optimal Transport formulation explicitly tailored for UCIR.
  arXiv  Detail & Related papers  (2024-02-28T15:31:45Z)
• Adaptive Betweenness Clustering for Semi-Supervised Domain Adaptation [108.40945109477886]
  We propose a novel SSDA approach named Graph-based Adaptive Betweenness Clustering (G-ABC) for achieving categorical domain alignment. Our method outperforms previous state-of-the-art SSDA approaches, demonstrating the superiority of the proposed G-ABC algorithm.
  arXiv  Detail & Related papers  (2024-01-21T09:57:56Z)
• Class Anchor Margin Loss for Content-Based Image Retrieval [97.81742911657497]
  We propose a novel repeller-attractor loss that falls in the metric learning paradigm, yet directly optimize for the L2 metric without the need of generating pairs. We evaluate the proposed objective in the context of few-shot and full-set training on the CBIR task, by using both convolutional and transformer architectures.
  arXiv  Detail & Related papers  (2023-06-01T12:53:10Z)
• QuadFormer: Quadruple Transformer for Unsupervised Domain Adaptation in Power Line Segmentation of Aerial Images [12.840195641761323]
  We propose a novel framework designed for domain adaptive semantic segmentation. The hierarchical quadruple transformer combines cross-attention and self-attention mechanisms to adapt transferable context. We present two datasets - ARPLSyn and ARPLReal - to further advance research in unsupervised domain adaptive powerline segmentation.
  arXiv  Detail & Related papers  (2022-11-29T03:15:27Z)
• Frequency Spectrum Augmentation Consistency for Domain Adaptive Object Detection [107.52026281057343]
  We introduce a Frequency Spectrum Augmentation Consistency (FSAC) framework with four different low-frequency filter operations. In the first stage, we utilize all the original and augmented source data to train an object detector. In the second stage, augmented source and target data with pseudo labels are adopted to perform the self-training for prediction consistency.
  arXiv  Detail & Related papers  (2021-12-16T04:07:01Z)
• Semi-supervised Domain Adaptive Structure Learning [72.01544419893628]
  Semi-supervised domain adaptation (SSDA) is a challenging problem requiring methods to overcome both 1) overfitting towards poorly annotated data and 2) distribution shift across domains. We introduce an adaptive structure learning method to regularize the cooperation of SSL and DA.
  arXiv  Detail & Related papers  (2021-12-12T06:11:16Z)
• Dispensed Transformer Network for Unsupervised Domain Adaptation [21.256375606219073]
  A novel unsupervised domain adaptation (UDA) method named dispensed Transformer network (DTNet) is introduced in this paper. Our proposed network achieves the best performance in comparison with several state-of-the-art techniques.
  arXiv  Detail & Related papers  (2021-10-28T08:27:44Z)
• Stagewise Unsupervised Domain Adaptation with Adversarial Self-Training for Road Segmentation of Remote Sensing Images [93.50240389540252]
  Road segmentation from remote sensing images is a challenging task with wide ranges of application potentials. We propose a novel stagewise domain adaptation model called RoadDA to address the domain shift (DS) issue in this field. Experiment results on two benchmarks demonstrate that RoadDA can efficiently reduce the domain gap and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-08-28T09:29:14Z)
• Towards Accurate Knowledge Transfer via Target-awareness Representation Disentanglement [56.40587594647692]
  We propose a novel transfer learning algorithm, introducing the idea of Target-awareness REpresentation Disentanglement (TRED) TRED disentangles the relevant knowledge with respect to the target task from the original source model and used as a regularizer during fine-tuning the target model. Experiments on various real world datasets show that our method stably improves the standard fine-tuning by more than 2% in average.
  arXiv  Detail & Related papers  (2020-10-16T17:45:08Z)

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.