Abstract
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 this https URL.
Abstract (translated)
深度量化方法在大型图像检索任务上表现出了高效性。然而，当前的模型在很大程度上依赖于真实数据，这阻碍了在有标签的场景中应用量化。一个更现实的需求是学习来自非正式标签的不可用上传图像，这些图像与业余用户提供的标签相关。尽管这些标签并不明显地揭示了标签，但它们实际上包含有关深度量化的有用语义信息。为此，我们提出了弱监督深度超球量化（WSDHQ），这是第一个从弱标签图像中学习深度量化的工作。具体来说，1）我们使用词向量来表示标签，并根据标签相关图增强其语义信息。2）为了更好地保留语义信息在量化代码中，并减少量化误差，我们通过采用设计巧妙的融合层和定制损失函数，在超球上共同学习和语义保持嵌入。大量实验证明，WSDHQ可以在弱监督的紧凑编码上实现最先进的性能。代码可在此处下载：https://url.cn/xyz6hU6
URL
https://arxiv.org/abs/2404.04998
