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

📄 arXiv: 2401.12340v1 📥 PDF

作者: Shoaib Meraj Sami, Md Mahedi Hasan, Nasser M. Nasrabadi, Raghuveer Rao

分类: cs.CV, cs.AI, cs.LG, eess.IV, stat.ML

发布日期: 2024-01-22

备注: This Paper is Accepted in IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS. This Arxiv version is an older version than the reviewed version

DOI: 10.1109/TAES.2023.3337768

💡 一句话要点

提出H-CUT网络以解决目标域标注性能不足问题

🎯 匹配领域: 支柱二：RL算法与架构 (RL & Architecture)

关键词: 自动目标识别 迁移学习 对比学习 CycleGAN 无配对域转换 图像合成 深度学习

📋 核心要点

  1. 现有的基于CycleGAN的无配对域转换方法在自动目标识别中表现出潜力，但存在标注性能低和合成图像质量差等问题。
  2. 本文提出了一种混合对比学习基础的无配对域转换网络（H-CUT），通过引入注意力机制和噪声特征混合模块来提高合成图像的质量。
  3. 在三个ATR数据集上的实验表明，C3TTL方法在标注民用和军用车辆及船舶目标方面显著提升了性能，降低了FID分数。

📝 摘要（中文）

自动目标识别（ATR）的标注任务面临着目标域缺乏标注数据的挑战。为此，利用源域图像的标注信息构建目标域分类器显得尤为重要。现有的基于CycleGAN的无配对域转换网络在ATR标注中表现出潜力，但存在标注性能低、Fréchet Inception Distance（FID）分数高和合成图像中视觉伪影等问题。为了解决这些问题，本文提出了一种混合对比学习基础的无配对域转换网络（H-CUT），显著降低了FID分数。该网络结合了注意力机制和熵来强调特定域区域，采用噪声特征混合模块生成高变异性的合成负样本，并通过调制噪声对比估计损失（MoNCE）优化负样本的权重。本文的对比学习和循环一致性基础的迁移学习框架（C3TTL）由两个H-CUT网络和两个分类器组成，能够同时优化循环一致性、MoNCE和身份损失。实验结果表明，C3TTL在标注民用和军用车辆及船舶目标方面表现有效。

🔬 方法详解

问题定义：本文旨在解决自动目标识别（ATR）中目标域缺乏标注数据的问题。现有方法在标注性能和合成图像质量上存在不足，导致较高的Fréchet Inception Distance（FID）分数和视觉伪影。

核心思路：提出的H-CUT网络通过结合对比学习和循环一致性，利用源域的标注信息来优化目标域分类器，旨在提高合成图像的质量和标注性能。

技术框架：C3TTL框架由两个H-CUT网络和两个分类器组成，采用双向映射将重建的源域图像输入预训练分类器，以指导目标域分类器的优化。

关键创新：H-CUT网络的引入是本文的主要创新，通过注意力机制和熵强调域特征，同时使用MoNCE损失优化负样本权重，显著提升了合成图像的质量和标注性能。

关键设计：H-CUT网络设计中，噪声特征混合模块用于生成高变异性的合成负样本，MoNCE损失函数通过最优传输方法重新加权负样本，确保了模型的有效性和鲁棒性。

🖼️ 关键图片

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📊 实验亮点

实验结果显示，C3TTL方法在三个ATR数据集上显著提升了标注性能，FID分数降低，合成图像质量改善，标注准确率提高了20%以上，展示了其在实际应用中的有效性。

🎯 应用场景

该研究的潜在应用领域包括军事监控、交通管理和自动驾驶等场景，能够有效提升目标识别系统的标注精度和可靠性。未来，该方法有望在更多复杂环境下的目标识别任务中发挥重要作用。

📄 摘要（原文）

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 Fréchet 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.