In this work, we investigate the problem of learning knowledge from the massive community-contributed images with rich weakly-supervised context information, which can benefit multiple image understanding tasks simultaneously, such as social image tag refinement and assignment, content-based image retrieval, tag-based image retrieval and tag expansion. Towards this end, we propose a Deep Collaborative Embedding (DCE) model to uncover a unified latent space for images and tags. The proposed method incorporates the end-to-end learning and collaborative factor analysis in one unified framework for the optimal compatibility of representation learning and latent space discovery. A nonnegative and discrete refined tagging matrix is learned to guide the end-to-end learning. To collaboratively explore the rich context information of social images, the proposed method integrates the weakly-supervised image-tag correlation, image correlation and tag correlation simultaneously and seamlessly. The proposed model is also extended to embed new tags in the uncovered space. To verify the effectiveness of the proposed method, extensive experiments are conducted on two widely-used social image benchmarks for multiple social image understanding tasks. The encouraging performance of the proposed method over the state-of-the-art approaches demonstrates its superiority.

中文翻译：

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深度协作嵌入，有助于理解社会形象

在这项工作中，我们研究了从大量社区贡献的图像中学习知识的问题，这些图像包含丰富的弱监督上下文信息，可以同时使多个图像理解任务受益，例如社交图像标签的精炼和分配，基于内容的图像检索，基于标签的图像检索和标签扩展。为此，我们提出了一种深度协作嵌入（DCE）模型，以发现图像和标签的统一潜在空间。所提出的方法在一个统一的框架中结合了端到端的学习和协作因素分析，以实现表示学习和潜在空间发现的最佳兼容性。学习了一个非负且离散的精炼标签矩阵，以指导端到端学习。为了共同探索社交图像的丰富背景信息，所提出的方法将弱监督的图像标签相关性，图像相关性和标签相关性同时无缝地集成在一起。提议的模型也被扩展为在未覆盖的空间中嵌入新标签。为了验证所提出方法的有效性，针对两个用于广泛社会形象理解任务的广泛使用的社会形象基准进行了广泛的实验。相对于最新方法，所提出方法的令人鼓舞的性能证明了它的优越性。在两个广泛使用的社交图像基准测试中进行了广泛的实验，以应对多种社交图像理解任务。所提出的方法在最新方法方面的令人鼓舞的性能证明了它的优越性。在两个广泛使用的社交图像基准测试中进行了广泛的实验，以应对多种社交图像理解任务。所提出的方法在最新方法方面的令人鼓舞的性能证明了它的优越性。