Sparse composite quantization
By Ting Zhang, Guo-Jun Qi, Jinhui Tang, Jingdong Wang. 2015
80 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Efficiency & Compression
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
- Jingdong Wang
- Luc Van Gool
- Yi Yang
- Wei David Liu
- Trevor Darrell
- Xiaogang Wang
- Andrew Zisserman
In Efficiency & Compression
- Herv'e J'egou
- Jingdong Wang
- Sanjiv Kumar
- Felix X. Yu
- Matthijs Douze
- Rongrong Ji
- Nicu Sebe
- Florent Perronnin
Citations in the corpus
- Quantization based Fast Inner Product Search (2015, background): “As confirmed in [21], this overhead becomes non-negligible in practice.”
- Learning Binary Codes for Maximum Inner Product Search (2015, background): “Besides hashing, vector quantization (VQ) [2, 6, 10, 27, 40, 41] is another promising direction for large-scale search problems in high-dimensional space.”
- Multiscale Quantization for Fast Similarity Search (2017, methodology): “This includes additive quantization [5, 6, 26], composite quantization [37, 38] and stacked quantization [27].”
- Polysemous Codes (2016, background): “Subsequent works [5,57,58] have pushed possible memory/efficiency trade-off by adopting a more general point of view, such as “Ad-ditive quantization” [5], which provides an excellent approximation and search performance, yet obtained with a much higher computational encoding cost [7].”
- Shared Predictive Cross-Modal Deep Quantization (2018, unknown): “It has been proven that quantization enjoys a more powerful representation ability than hashing thanks to the more accurate distance approximation [34]–[37].”
- Top Rank Supervised Binary Coding for Visual Search (2015, background): “During the past few years, binary coding (also known as hashing [26] in the literature) techniques have been widely used in a variety of computer vision and machine learning applications, including object recognition [23, 24, 21], scene classification [22], image retrieval [9, 14, 8, 33, 34, 20], li”
- A Survey on Learning to Hash (2016, methodology): “More results, such as the search time cost, and results using inverted multi-index with different quantization algorithms can be found in [172].”