no code implementations • 17 Mar 2024 • Yuetong Fang, Ziqing Wang, Lingfeng Zhang, Jiahang Cao, Honglei Chen, Renjing Xu
Spiking neural networks (SNNs) offer an energy-efficient alternative to conventional deep learning by mimicking the event-driven processing of the brain.
1 code implementation • 22 Nov 2022 • Lingfeng Zhang, Nishard Abdeen, Jochen Lang
We propose an anomaly detection method based on a novel center-based deep contrastive metric learning loss function (cDCM) which enables the automatic detection of cases of potential PMG.
no code implementations • 15 May 2018 • Lingfeng Zhang, Ioannis A. Kakadiaris
A Fully Associative Patch-based Signature Matcher (FAPSM) is proposed so that the local matching identity of each patch contributes to the global matching identities of all the patches.
no code implementations • 7 May 2018 • Lingfeng Zhang, Ioannis A. Kakadiaris
This paper focuses on improving the performance of current convolutional neural networks in visual recognition without changing the network architecture.
no code implementations • 3 Apr 2018 • Lingfeng Zhang, Pengfei Dou, Ioannis A. Kakadiaris
Three ways are introduced to learn the global matching: majority voting, l1-regularized weighting, and decision rule.
no code implementations • 25 Mar 2018 • Lingfeng Zhang, Pengfei Dou, Ioannis A. Kakadiaris
This paper focuses on improving face recognition performance with a new signature combining implicit facial features with explicit soft facial attributes.
no code implementations • 5 Sep 2015 • Yuewei Lin, Jing Chen, Yu Cao, Youjie Zhou, Lingfeng Zhang, Yuan Yan Tang, Song Wang
By adopting a natural and widely used assumption -- "the data samples from the same class should lay on a low-dimensional subspace, even if they come from different domains", the proposed method circumvents the limitation of the global domain shift, and solves the cross-domain recognition by finding the compact joint subspaces of source and target domain.