Pseudo Labels and Soft Multi-Part Corresponding Similarity for Unsupervised Deep Hashing
In recent years, unsupervised deep hashing methods have achieved great success in large-scale image retrieval. However, these approaches still suffer two major problems in real world applications. On the one hand, due to the lack of effective supervision information, hash codes of different categori...
Main Authors: | Huiying Li, Yang Li, Xin Xie, Shuai Gao, Dongsheng Mao |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9039610/ |
Similar Items
-
PLDH: Pseudo-Labels Based Deep Hashing
by: Huawen Liu, et al.
Published: (2023-05-01) -
Unsupervised Hashing with Gradient Attention
by: Shaochen Jiang, et al.
Published: (2020-07-01) -
Contrastive Self-Supervised Hashing With Dual Pseudo Agreement
by: Yang Li, et al.
Published: (2020-01-01) -
Angular Quantization Online Hashing for Image Retrieval
by: Yuzhi Fang, et al.
Published: (2022-01-01) -
Multi-Grained Similarity Preserving and Updating for Unsupervised Cross-Modal Hashing
by: Runbing Wu, et al.
Published: (2024-01-01)