5 citations · 9 across the 9 of their papers we have counts for
10 papers
InterPruner: Interactive Structured Pruning via Taylor-Implicit Criterion and Language-Prior Modulator for Multimodal Object Detection
Qi Ming, Zihan Yang, Shaoguang Huang +6
Multimodal object detection proves effective in remote sensing, especially the RGB-Infrared paradigm. The parallel feature extractors provide rich multimodal information for robust…
Joint Superpixel and Self-Representation Learning for Scalable Hyperspectral Image Clustering
Xianlu Li, Nicolas Nadisic, Shaoguang Huang +1
Subspace clustering is a powerful unsupervised approach for hyperspectral image (HSI) analysis, but its high computational and memory costs limit scalability. Superpixel segmentati…
Generalized Category Discovery in Hyperspectral Images via Prototype Subspace Modeling
Xianlu Li, Nicolas Nadisic, Shaoguang Huang +1
Generalized category discovery~(GCD) seeks to jointly identify both known and novel categories in unlabeled data. While prior works have mainly focused on RGB images, their assumpt…
Scalable Context-Preserving Model-Aware Deep Clustering for Hyperspectral Images
Xianlu Li, Nicolas Nadisic, Shaoguang Huang +2
Subspace clustering has become widely adopted for the unsupervised analysis of hyperspectral images (HSIs). Recent model-aware deep subspace clustering methods often use a two-stag…
Self-Supervised One-Step Diffusion Refinement for Snapshot Compressive Imaging
Shaoguang Huang, Yunzhen Wang, Haijin Zeng +2
Snapshot compressive imaging (SCI) captures multispectral images (MSIs) using a single coded two-dimensional (2-D) measurement, but reconstructing high-fidelity MSIs from these com…
Unfolding ADMM for Enhanced Subspace Clustering of Hyperspectral Images
Xianlu Li, Nicolas Nadisic, Shaoguang Huang +1
Deep subspace clustering methods are now prominent in clustering, typically using fully connected networks and a self-representation loss function. However, these methods often str…
Degradation-Noise-Aware Deep Unfolding Transformer for Hyperspectral Image Denoising
Haijin Zeng, Jiezhang Cao, Kai Feng +4
Hyperspectral imaging (HI) has emerged as a powerful tool in diverse fields such as medical diagnosis, industrial inspection, and agriculture, owing to its ability to detect subtle…
Inheriting Bayer's Legacy-Joint Remosaicing and Denoising for Quad Bayer Image Sensor
Haijin Zeng, Kai Feng, Jiezhang Cao +5
Pixel binning based Quad sensors have emerged as a promising solution to overcome the hardware limitations of compact cameras in low-light imaging. However, binning results in lowe…
MSFA-Frequency-Aware Transformer for Hyperspectral Images Demosaicing
Haijin Zeng, Kai Feng, Shaoguang Huang +5
Hyperspectral imaging systems that use multispectral filter arrays (MSFA) capture only one spectral component in each pixel. Hyperspectral demosaicing is used to recover the non-me…
Low-rank Meets Sparseness: An Integrated Spatial-Spectral Total Variation Approach to Hyperspectral Denoising
Haijin Zeng, Shaoguang Huang, Yongyong Chen +2
Spatial-Spectral Total Variation (SSTV) can quantify local smoothness of image structures, so it is widely used in hyperspectral image (HSI) processing tasks. Essentially, SSTV ass…