Results 121 to 130 of about 26,920 (307)

Transfer Adversarial Hashing for Hamming Space Retrieval

, 2017
Hashing is widely applied to large-scale image retrieval due to the storage and retrieval efficiency. Existing work on deep hashing assumes that the database in the target domain is identically distributed with the training set in the source domain. This
Cao, Zhangjie, Huang, Chao, Long, Mingsheng, Wang, Jianmin   +3 more
core   +1 more source

Push for Quantization: Deep Fisher Hashing

, 2019
BMVC ...
Li, Y. (author), Pei, W. (author), Zha, Yufei (author), van Gemert, J.C. (author)   +3 more
openaire   +3 more sources

Targeted Attack for Deep Hashing based Retrieval [PDF]

, 2020
Jiawang Bai, Bin Chen, Yiming Li, Dongxian Wu, Weiwei Guo, Shu‐Tao Xia, En‐hui Yang   +6 more
openalex   +1 more source

Blockchain technology disruptions: Exploring accounting and auditing academics and practitioners' perception

Accounting &Finance, EarlyView.
Abstract This study explores the practical impact of blockchain technology (BCT), which contrasts strongly with literature that has predominantly hypothesised BCT's potential to disrupt accounting practice. We interviewed 44 practitioners and academics with knowledge of BCT across 13 countries and industries.
Musbaudeen Titilope Oladejo, Vida Botes, Mary Low, Steve Reeves   +3 more
wiley   +1 more source

DSCEH: Dual-Stream Correlation-Enhanced Deep Hashing for Image Retrieval

Mathematics
Deep Hashing is widely used for large-scale image-retrieval tasks to speed up the retrieval process. Current deep hashing methods are mainly based on the Convolutional Neural Network (CNN) or Vision Transformer (VIT).
Yulin Yang, Huizhen Chen, Rongkai Liu, Shuning Liu, Yu Zhan, Chao Hu, Ronghua Shi   +6 more
doaj   +1 more source

Estimating thresholds for risk of cannabis use disorder using standard delta‐9‐tetrahydrocannabinol (THC) units

Addiction, EarlyView.
Abstract Background and aims Lower risk guidelines for safer levels of cannabis use could help to reduce the health burden posed by cannabis use disorder (CUD). We aimed to estimate risk thresholds for CUD based on delta‐9‐tetrahydrocannabinol (THC) consumption using standard THC units (1 unit = 5 mg THC).
Rachel Lees Thorne, Will Lawn, Kat Petrilli, Katie Trinci, Anya Borissova, Shelan Ofori, Claire Mokrysz, H. Valerie Curran, Lindsey A. Hines, Tom P. Freeman   +9 more
wiley   +1 more source

Deep Class-Guided Hashing for Multi-Label Cross-Modal Retrieval

Applied Sciences
Deep hashing has gained widespread attention in cross-modal retrieval due to its low cost and efficient retrieval advantages. However, existing cross-modal hashing methods either focus solely on exploring relationships between data points—which ...
Hao Chen, Zhuoyang Zou, Yiqiang Liu, Xinghui Zhu   +3 more
doaj   +1 more source

Multi-level supervised hashing with deep features for efficient image retrieval

, 2020
Wing W. Y. Ng, Jiayong Li, Xing Tian, Hui Wang, Sam Kwong, Jonathan Wallace   +5 more
openalex   +2 more sources

Adolescent cannabis use and psychological distress from 2013 to 2023: A population‐based study in Ontario, Canada

Addiction, EarlyView.
Abstract Background and aims Epidemiologic research suggests that adolescent cannabis use is associated with psychological distress (i.e. depression and anxiety symptoms); however, most studies have relied on 20th‐century data, when cannabis was significantly less potent than today.
André J. McDonald, Amanda Doggett, Susan J. Bondy, Ian Colman, Steven Cook, Hayley A. Hamilton, Paul Kurdyak, Scott T. Leatherdale, Daniel T. Myran, Jürgen Rehm, Christine M. Wickens, James MacKillop, Jillian Halladay   +12 more
wiley   +1 more source

Deep Hash Learning Model Based on High-Order Statistical Information

, 2020
GU Yan, ZHAO Chongyu, HUANG Ping
openalex   +2 more sources