Results 141 to 150 of about 80,492 (210)

Multisource Data Reconstruction-Based Deep Unsupervised Hashing for Unisource Remote Sensing Image Retrieval

IEEE Transactions on Geoscience and Remote Sensing, 2022
Unsupervised hashing for remote sensing (RS) image retrieval first extracts image features and then uses these features to construct supervised information (e.g., pseudolabels) to train hashing networks.
Yuxi Sun, Yunming Ye, Jian Kang, R. Fernández-Beltran, Yifang Ban, Xutao Li, Bowen Zhang, A. Plaza   +7 more
semanticscholar   +1 more source

To hash or not to hash: A security assessment of CSP’s unsafe-hashes expression

2022 IEEE Security and Privacy Workshops (SPW), 2022
More and more people use the Web on a daily basis. We use it for communicating, doing bank transactions, and entertainment. This popularity of the Web has made it one of the main targets of attacks, most prominently Cross-Site Scripting (XSS). To mitigate the effect of those attacks, the prevalence of the Content Security Policy (CSP) is increasing ...
Peter Stolz, Sebastian Roth, Ben Stock
openaire   +1 more source

Work Together: Correlation-Identity Reconstruction Hashing for Unsupervised Cross-Modal Retrieval

IEEE Transactions on Knowledge and Data Engineering, 2023
Unsupervised cross-modal hashing has attracted considerable attention to support large-scale cross-modal retrieval. Although promising progresses have been made so far, existing methods still suffer from limited capability on excavating and preserving ...
Lei Zhu, Xize Wu, Jingjing Li, Zheng Zhang, Weili Guan, H. Shen   +5 more
semanticscholar   +1 more source

Unsupervised Cross-Modal Hashing With Modality-Interaction

IEEE transactions on circuits and systems for video technology (Print), 2023
Recently, numerous unsupervised cross-modal hashing methods have been proposed to deal the image-text retrieval tasks for the unlabeled cross-modal data.
Rong-Cheng Tu, Jie Jiang, Qinghong Lin, Chengfei Cai, Shangxuan Tian, Hongfa Wang, Wei Liu   +6 more
semanticscholar   +1 more source

Unsupervised Contrastive Cross-Modal Hashing

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022
In this paper, we study how to make unsupervised cross-modal hashing (CMH) benefit from contrastive learning (CL) by overcoming two challenges. To be exact, i) to address the performance degradation issue caused by binary optimization for hashing, we ...
Peng Hu, Hongyuan Zhu, Jie Lin, Dezhong Peng, Yin-Ping Zhao, Xi Peng   +5 more
semanticscholar   +1 more source

Deep Hashing with Minimal-Distance-Separated Hash Centers

Computer Vision and Pattern Recognition, 2023
Deep hashing is an appealing approach for large-scale image retrieval. Most existing supervised deep hashing methods learn hash functions using pairwise or triple image similarities in randomly sampled mini-batches.
Liangdao Wang, Yan Pan, Cong Liu, Hanjiang Lai, Jian Yin, Ye Liu   +5 more
semanticscholar   +1 more source

Differentiable Cross-modal Hashing via Multimodal Transformers

ACM Multimedia, 2022
Cross-modal hashing aims at projecting the cross modal content into a common Hamming space for efficient search. Most existing work first encodes the samples with a deep network and then binaries the encoded feature into hashing code.
Junfeng Tu, Xueliang Liu, Zongxiang Lin, Richang Hong, Meng Wang   +4 more
semanticscholar   +1 more source

BATCH: A Scalable Asymmetric Discrete Cross-Modal Hashing

IEEE Transactions on Knowledge and Data Engineering, 2021
Supervised cross-modal hashing has attracted much attention. However, there are still some challenges, e.g., how to effectively embed the label information into binary codes, how to avoid using a large similarity matrix and make a model scalable to large-
Yongxin Wang, Xin Luo, Liqiang Nie, Jingkuan Song, W. Zhang, Xin-Shun Xu   +5 more
semanticscholar   +1 more source

Aggregation-Based Graph Convolutional Hashing for Unsupervised Cross-Modal Retrieval

IEEE transactions on multimedia, 2021
Cross-modal hashing has sparked much attention in large-scale information retrieval for its storage and query efficiency. Despite the great success achieved by supervised approaches, existing unsupervised hashing methods still suffer from the lack of ...
Pengfei Zhang, Yang Li, Zi Huang, Xin-Shun Xu   +3 more
semanticscholar   +1 more source

MRD Hashing

Designs, Codes and Cryptography, 2005
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Reihaneh Safavi-Naini, Chris Charnes
openaire   +1 more source