no code implementations • 26 Apr 2024 • Xin Zhang, Liangxiu Han, Tam Sobeih, Lianghao Han, Darren Dancey
This necessitates the development of innovative, spike-aware algorithms tailored for event cameras, a task compounded by the irregularity, continuity, noise, and spatial and temporal characteristics inherent in spiking data. Harnessing the strong generalization capabilities of transformer neural networks for spatiotemporal data, we propose a purely spike-driven spike transformer network for depth estimation from spiking camera data.
no code implementations • 17 Feb 2023 • Xin Zhang, Liangxiu Han, Lianghao Han, Haoming Chen, Darren Dancey, Daoqiang Zhang
Specifically, it consists of two primary components: 1) A fast and efficient explainable patch selection mechanism for determining the most discriminative patches based on computing the SHapley Additive exPlanations (SHAP) contribution to a transfer learning model for AD diagnosis on massive medical data; and 2) A novel patch-based network for extracting deep features and AD classfication from the selected patches with position embeddings to retain position information, capable of capturing the global and local information of inter- and intra-patches.
no code implementations • 16 Nov 2021 • Yue Shi, Liangxiu Han, Lianghao Han, Sheng Chang, Tongle Hu, Darren Dancey
To alleviate the problem of mode collapse, this work has proposed a novel GAN model coupled with a latent encoder (LE-GAN), which can map the generated spectral-spatial features from the image space to the latent space and produce a coupling component to regularise the generated samples.
Generative Adversarial Network Hyperspectral Image Super-Resolution +1
no code implementations • 20 Oct 2021 • Xin Zhang, Liangxiu Han, Tam Sobeih, Lianghao Han, Nina Dempsey, Symeon Lechareas, Ascanio Tridente, Haoming Chen, Stephen White
The proposed method can provide more detailed high resolution visual explanation for the classification decision, compared to current state-of-the-art visual explanation methods and has a great potential to be used in clinical practice for COVID-19 pneumonia diagnosis.
no code implementations • 19 Apr 2020 • Yue Shi, Liangxiu Han, Wenjiang Huang, Sheng Chang, Yingying Dong, Darren Dancey, Lianghao Han
Spectral-spatial based deep learning models have recently proven to be effective in hyperspectral image (HSI) classification for various earth monitoring applications such as land cover classification and agricultural monitoring.