In EDDH, the discriminative capability of hash codes is enhanced by a distribution-based continuous semantic-visual similarity matrix, where not only the margin between the positive pairs and negative pairs is expanded, but also the visual linkages between image pairs is considered. Specifically, ...
Image searchImage clusterSocial imageImage conceptsConcept relevanceFlickrTagPartially due to the short and ambiguous keyword queries, many image search engines group search results into conceptual image clusters to minimize the chance of completely missing user search intent....
to seeking out the texts (images) that are corresponding to a given image (text) query. The most fundamental challenge for achieving accurate VSM lies in that visual and textual representations are inconsistent, which brings about a major obstacle to effectively measure cross-modal similarity. ...
Different from most existing methods, the proposed approach employs the deep visual-semantic embedding model to directly compute the similarity between the query and video thumbnails by mapping them into a common latent semantic space, where even unseen query thumbnail pairs ca...
(Extended Data Fig.1e). Instead, the boundary angle gradually and slightly shifted, as reflected by a significantly higher similarity between consecutive days compared to periods spaced more than 20 d apart (Extended Data Fig.1f). This implies that the mismatch between the trained and the ...
Importantly for our purposes, DNNs for vision do not include semantic knowledge about objects. They typically use an output layer consisting of object labels (e.g. BANANA, MOTORCYCLE), which do not capture information about the kinds of semantic commonality or semantic similarity which are an esse...
We compute thecosine similaritybetween the query embedding and all embeddings in the database (which could be images or text). This determines how closely the query matches existing items. 4.Retrieval or Generation If it’s asearch task, the top-k most similar items are retrieved and returned...
where [x]+ = max (0,x),Sa,pis the similarity of anchorxaand positive inputxp, andSa,nis the similarity of anchorxaand negative inputxn.λis the margin that lets the negative pairs away from each other. Therefore, we define our triplet ranking loss as ...
使用点积相似性(dot-product similarity)和铰链损失函数(hinge rank loss)结合来作为该模型的损失函数,这可以使得在图像模型的输出和该图像对应的正确的标签的向量表示之间的点积相似性,要比不正确的其他标签的向量与该图像的相似性高。( - - 说得有点绕,就是为了预测得更准确的意思) 该损失函数的公式和部分符号...
Finally, a loss function is elaborately designed to simultaneously consider the label loss of each image and similarity loss of pairs of images. Experimental results on two remote sensing datasets demonstrate that the proposed method achieves the state-of-art retrieval and classification performance. ...