Unsupervised Hashing with Similarity Distribution Calibration

Autor:	Ng, Kam Woh, Zhu, Xiatian, Hoe, Jiun Tian, Chan, Chee Seng, Zhang, Tianyu, Song, Yi-Zhe, Xiang, Tao
Rok vydání:	2023
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Information Retrieval
Druh dokumentu:	Working Paper
Popis:	Unsupervised hashing methods typically aim to preserve the similarity between data points in a feature space by mapping them to binary hash codes. However, these methods often overlook the fact that the similarity between data points in the continuous feature space may not be preserved in the discrete hash code space, due to the limited similarity range of hash codes. The similarity range is bounded by the code length and can lead to a problem known as similarity collapse. That is, the positive and negative pairs of data points become less distinguishable from each other in the hash space. To alleviate this problem, in this paper a novel Similarity Distribution Calibration (SDC) method is introduced. SDC aligns the hash code similarity distribution towards a calibration distribution (e.g., beta distribution) with sufficient spread across the entire similarity range, thus alleviating the similarity collapse problem. Extensive experiments show that our SDC outperforms significantly the state-of-the-art alternatives on coarse category-level and instance-level image retrieval. Code is available at https://github.com/kamwoh/sdc. Comment: BMVC 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2302.07669 Zobrazit plný text záznamu View this record from Arxiv