Click-through-based cross-view learning for image search

By Yingwei Pan, Ting Yao, Tao Mei, Houqiang Li, Chong-Wah Ngo, Yong Rui. 2014

84 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Image-Based Recommendations on Styles and Substitutes (2015, background): “The system shares these characteristics with more common forms of recommender system, but does so on the basis of the appearance of the object, rather than metadata, reviews, or similar.”
Jointly Modeling Embedding and Translation to Bridge Video and Language (2015, background): “We assume that a low-dimensional embedding exists for the representation of video and sentence, which is widely used in image search [18, 35] and image reranking [17].”
Comparative Deep Learning of Hybrid Representations for Image Recommendations (2016, methodology): “One feasible solution that has been studied is to utilize the users’ information as constraints to refine the image representations, making them consistent with both semantic labels and user provided hints [19, 23, 33].”
Cross-Modal Similarity Learning: A Low Rank Bilinear Formulation (2014, methodology): “Beyond of these methods, there are some other algorithms to deal with the problem, such as the generalized multiview analysis (GMA) (Sharma et al. 2012), the weakly paired maximum covariance analysis (WMCA) (Lampert and Kr¨omer 2010), the deep neural network (Ngiam et al. 2011; Srivastava and…”
Exploring Object Relation in Mean Teacher for Cross-Domain Detection (2019, methodology): “Thus, inspired from graph structure exploitation [31, 32, 51, 52] in computer vision tasks, we devise a novel graph-structured regularization, i.”
Deep Multimodal Distance Metric Learning Using Click Constraints for Image Ranking (2017, methodology): “[46] proposed a novel subspace learning method based on click data for image retrieval.”
A Decade Survey of Content Based Image Retrieval Using Deep Learning (2020, background): “learning for image search [92], distance metric learning for image ranking [93] and deep structure-preserving embeddings with visual attention [94].”
Multi-task deep visual-semantic embedding for video thumbnail selection (2015, background): “For instance, Pan et al . propose a click-through-based cross view learning strategy, which directly calculate the multi-view distance between a textual query and an image by learning a latent common subspace with the ability in computing the query-image relevance [16].”
Learning Consistent Feature Representation for Cross-Modal Multimedia Retrieval (2015, methodology): “In this paper, we focus on the cross-modal matching problem and apply it to the cross media retrieval, which has been actively studied in recent years [9], [10], [23], [28], [30], [33], [44], [48].”