用于图像检索的弱监督深度超球面量化
Weakly Supervised Deep Hyperspherical Quantization for Image Retrieval
AI总结：
本文提出首个弱监督深度量化方法WSDHQ，利用词嵌入和标签相关性图增强弱标签语义，并在超球面上联合学习语义保持嵌入与量化器，显著提升弱监督图像检索性能。
摘要：
深度量化方法在大规模图像检索中已展现出高效率。然而，当前模型严重依赖真实标注信息，阻碍了量化技术在标签稀缺场景中的应用。一种更现实的需求是从海量上传图像中学习，这些图像带有业余用户提供的非正式标签。尽管这类粗略标签不能明确揭示真实类别，但它们实际上包含有用的语义信息，可用于监督深度量化。为此，我们提出弱监督深度超球面量化（WSDHQ），这是首个从弱标签图像中学习深度量化的工作。具体而言：1）我们使用词嵌入来表示标签，并基于标签相关性图增强其语义信息。2）为了更好地在量化编码中保留语义信息并减少量化误差，我们通过精心设计的融合层和定制的损失函数，在超球面上联合学习语义保持嵌入和有监督量化器。大量实验表明，WSDHQ在弱监督紧凑编码上能够取得最先进的性能。代码见 https://github.com/gimpong/AAAI21-WSDHQ。
英文摘要：
Deep quantization methods have shown high efficiency on large-scale image retrieval. However, current models heavily rely on ground-truth information, hindering the application of quantization in label-hungry scenarios. A more realistic demand is to learn from inexhaustible uploaded images that are associated with informal tags provided by amateur users. Though such sketchy tags do not obviously reveal the labels, they actually contain useful semantic information for supervising deep quantization. To this end, we propose Weakly-Supervised Deep Hyperspherical Quantization (WSDHQ), which is the first work to learn deep quantization from weakly tagged images. Specifically, 1) we use word embeddings to represent the tags and enhance their semantic information based on a tag correlation graph. 2) To better preserve semantic information in quantization codes and reduce quantization error, we jointly learn semantics-preserving embeddings and supervised quantizer on hypersphere by employing a well-designed fusion layer and tailor-made loss functions. Extensive experiments show that WSDHQ can achieve state-of-art performance on weakly-supervised compact coding. Code is available at https://github.com/gimpong/AAAI21-WSDHQ.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
