Visual-Semantic Embedding
时间: 2024-06-06 08:11:04 浏览: 18
Visual-semantic embedding refers to the process of mapping words or text descriptions to visual features or representations. This is typically done by training a machine learning model on a large dataset of paired images and text descriptions, such as captions or tags. The goal is to learn a mapping between the visual and semantic domains, such that similar images and text descriptions are mapped to similar feature representations, and dissimilar ones are mapped to dissimilar representations. This can be useful for a variety of applications, such as image search, recommendation systems, and image captioning. One popular approach to visual-semantic embedding is to use a deep neural network, such as a convolutional neural network (CNN) or a transformer-based model, to learn the mapping between visual and semantic features.
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