Awesome Learning to Hash

Binary Quantisation Models

Quantisation models Two main categories of quantisation have been proposed for nearest neighbour search: scalar and vector quantisation, which are differentiated by whether the input and output of the quantisation is a scalar or a vector quantity. This page lists models for scalar quantisation. Scalar quantisation is frequently applied to quantise the real-values (projections) resulting from the dot product of the feature vector of each data-point onto the normal vectors to a set of hyperplanes partitioning the feature space. Each dot product yields a scalar value which is then subsequently quantised into binary (0/1) by thresholding. The resulting bits are concatenated to form the hashcode for a data-point.

Paper	Learning Type
Yue Cao, Mingsheng Long, Jianmin Wang, Qiang Yang, Philip S. Yu, 2016.Deep Visual-Semantic Hashing for Cross-Modal Retrieval	Supervised
Yue Cao, Mingsheng Long, Jianmin Wang, Shichen Liu, 2017.Collective Deep Quantization for Efficient Cross-Modal Retrieval	Supervised
Yuan Cao; Heng Qi; Jie Gui; Keqiu Li; Yuan Yan Tang; James Tin-Yau Kwok, 2020.Learning to Hash with a Dimension Analysis-based Quantizer for Image Retrieval	Unsupervised
Sobhan Hemati, H.R. Tizhoosh, 2021.Beyond Neighbourhood-Preserving Transformations for Quantization-Based Unsupervised Hashing	Unsupervised
W. Kong, W. Li, M. Guo, 2012.Manhattan Hashing for Large-Scale Image Retrieval	Unsupervised
W. Kong, W. Li, 2012.Double-Bit Quantisation for Hashing	Unsupervised
Yunqiang Li, Wenjie Pei, Yufei zha, Jan van Gemert, 2019.Push for Quantization: Deep Fisher Hashing	Supervised
S. Moran, V. Lavrenko, and M. Osborne, 2013.Neighbourhood Preserving Quantisation for LSH	Semi-Supervised
S. Moran, V. Lavrenko, and M. Osborne, 2013.Variable Bit Quantisation for LSH	Semi-Supervised
S. Moran, 2016.Learning to Project and Binarise for Hashing-Based Approximate Nearest Neighbour Search	Semi-Supervised
Yang Shi, Xiushan Nie, Xin Zhou, Xiaoming Xi, Yilong Yin, 2019.Variable-Length Quantization Strategy for Hashing	Unsupervised
Z. Wang, L. Duan, J. Lin, X. Wang, T. Huang and W. Gao, 2015.Hamming Compatible Quantization for Hashing	Unsupervised
Z. Wang, L. Duan, T. Huang, W. Gao, 2016.Affinity Preserving Quantization for Hashing: A Vector Quantization Approach to Learning Compact Binary Codes	Unsupervised
C. Xiong, W. Chen, G. Chen, D. Johnson, J. Corso, 2014.Adaptive Quantization for Hashing: An Information-Based Approach to Learning Binary Codes	Unsupervised
Li Yuan, Tao Wang, Xiaopeng Zhang, Francis EH Tay, Zequn Jie, Wei Liu, Jiashi Feng, 2020.Central Similarity Quantization for Efficient Image and Video Retrieval	Supervised