Parallel field alignment for cross media retrieval

By Xiangbo Mao, Binbin Lin, Deng Cai, Xiaofei He, Jian Pei. 2013

92 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

1. Yunchao Wei
2. Shuicheng Yan
3. Liang Wang
4. Yi Yang
5. Dacheng Tao
6. Yao Zhao
7. Yuxin Peng
8. Luc Van Gool

Citations in the corpus

• A Comprehensive Survey on Cross-modal Retrieval (2016, methodology): “Mao et al. [47] introduce a method for cross media retrieval, named parallel field alignment retrieval (PFAR), which integrates a manifold alignment framework from the perspective of vector fields.”
• Cross-Modal Retrieval With CNN Visual Features: A New Baseline (2017, methodology): “[20], [22], [29], [33], [47], [49]–[52], [54], [57], [60], [62], [64], [66], [68] are also proposed to address multimodal problems.”
• An Overview of Cross-Media Retrieval: Concepts, Methodologies, Benchmarks, and Challenges (2017, background): “…[56](U) [57](F) [58](F) [59](F) Dictionary learning [61](U) [62](S,F) [64](F) Cross-mediahashing [67](U) [68](U) [49](F) [69](U) [70](F) [71](F) [72](F) [73](U) [74](F) [75](U) [76](U) [77](F) [78](F) [79](U) [80](U) [81](F) [82](U) [83](U) Others [20](U) [84](F) [85](F) [86](F) [87](F) [89](S)”