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Cross-modal retrieval, which aims to solve the problem that the query and the retrieved results are from different modality, becomes more and more essential with the development of the Internet. In this paper, we mainly focus on the exploration of high-level semantic representation of image and text for cross-modal matching. Deep convolutional image features and Fisher Vector with neural word embeddings...
Cross-media retrieval aims to integrate and analyze the features of various modalities (e.g., text, image and video) to mine their potential semantic information. In this paper, we propose a novel cross-media retrieval framework, which performs coupled feature mapping and correlation mining successively. Our method first learns two projection matrices to map the multimodal features into a common category...
Many successful systems for scene recognition transform low-level descriptors into complex representations. This process consists of the two steps: 1) feature coding, which performs a pointwise transformation of the descriptors into a representation adapted to the task, and 2) image pooling, which summarizes the coded features. Even though these two steps have been paid so much attention, but there...
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