Click Prediction for Web Image Reranking Using Multimodal Sparse Coding
By Jun Yu, Yong Rui, Dacheng Tao. 2014
544 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Dacheng Tao
- Yi Yang
- Luc Van Gool
- Yueting Zhuang
- Cordelia Schmid
- Zhou Zhao
- Ming-Hsuan Yang
- Xiaogang Wang
Citations in the corpus
- Multi-view low-rank sparse subspace clustering (2017, background): “However, compared to sparse coding where dictionary is learned such that the representation is sparse [28, 29], SSC is based on self-representation property i.e. data matrix stands for a dictionary.”
- Deep Compositional Cross-modal Learning to Rank via Local-Global Alignment (2015, background): “Some approaches have been proposed to jointly learn the common space of multi-modal data and the ranking function at the same time [8, 1, 17, 25, 30].”
- Local structure preserving discriminative projections for RGB-D sensor-based scene classification (2015, background): “Feature representation is a hot topic in image classification [37,41,46,55,64].”
- Partial Multi-Modal Sparse Coding via Adaptive Similarity Structure Regularization (2016, methodology): “2 Unified Partial Multi-Modal Sparse Coding via Adaptive Similarity Structure Regularization Unlike the existing graph regularized sparse coding approaches [13, 11], we introduce the probability neighborhood matrix [6] that learns the data similarity matrix and sparse coding simultaneously.”
- Learning to Rank Using User Clicks and Visual Features for Image Retrieval (2015, background): “One feasible solution to address this problem is to integrate visual information of images [42]–[44], [46], [48]–[50] into the rank learning framework [45], [47].”
- Multi-View Intact Space Learning (2015, background): “Recently, numbers of multi-view learning algorithms [1] have been designed and successfully applied to various computer vision and intelligent system problems [2],[3],[4], [5],[6].”
- A Survey of Multi-View Representation Learning (2016, unknown): “web data mining [122], as well as cross-media retrieval [123, 124].”