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首页> 外文会议>IEEE International Conference on Multimedia and Expo >Ocean: A Dual Learning Approach For Generalized Zero-Shot Sketch-Based Image Retrieval
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Ocean: A Dual Learning Approach For Generalized Zero-Shot Sketch-Based Image Retrieval

机译：海洋：双重学习基于零镜头草图的图像检索的双重学习方法

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摘要

Sketch-Based Image Retrieval (SBIR) is an emerging research area with many real-world applications. Recent studies have approached this research task under the more challenging zero-shot learning setting (ZS-SBIR), which assume classes in the target domain are unseen during the training stage. Many of the existing ZS-SBIR studies transferred the learned cross-modal (i.e., sketch and image) representations from the source domain to the target domain by leveraging side information in semantic embeddings. However, these ZS-SBIR methods are not able to generalize well to a more realistic setting to retrieve images from seen and unseen classes. To address the limitation of existing methods, we propose the cOmmon Conditional Encoder Adversarial Network (OCEAN) to perform generalized zero-shot sketch-based image retrieval (GZS-SBIR). The OCEAN model utilizes a dual learning framework to cyclically map the sketch and image features to a common semantic space, and project semantic features back to the relevant visual space by adversarial training. We conduct experiments on two publicly available datasets and demonstrate that our proposed model outperformed the state-of-the-arts baselines in both ZS-SBIR and GZS-SBIR tasks.
机译：基于草图的图像检索（SBIR）是一个新兴的研究领域，具有许多实际应用。最近的研究已经在更具挑战性的零击学习设置（ZS-SBIR）下进行了这项研究任务，该假设假定在训练阶段看不到目标领域的课程。许多现有的ZS-SBIR研究都是通过利用语义嵌入中的辅助信息，将学习到的跨模态（即，草图和图像）表示形式从源域转移到目标域。但是，这些ZS-SBIR方法无法很好地推广到更实际的设置中，以从可见和不可见的类中检索图像。为了解决现有方法的局限性，我们建议使用cOmmon条件编码器对抗网络（OCEAN）来执行基于零镜头的广义基于图像的图像检索（GZS-SBIR）。 OCEAN模型利用双重学习框架将草图和图像特征周期性地映射到公共语义空间，并通过对抗训练将语义特征投影回相关的视觉空间。我们在两个公开可用的数据集上进行了实验，并证明了我们提出的模型在ZS-SBIR和GZS-SBIR任务中均优于最新基准。

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