7 papers
EM-SNN: Efficiently Modulated Spiking Neural Network for Remote Sensing Image Dehazing
Jie Shao, Jiaqi Ma, Wenwen Min +4
Although spiking neural networks (SNNs) provide an energy-efficient alternative to artificial neural networks (ANNs), their application to remote sensing image dehazing remains lim…
FreqFLD: Towards All-in-One Facial Landmark Detection via Frequency Modulation
Shun Ren, Kaijie Jin, Shengkai Hu +5
Recent progress in deep learning has significantly advanced facial landmark detection. However, most existing methods process features in a spatial-domain manner under a dataset-sp…
Cross-Slice Knowledge Transfer via Masked Multi-Modal Heterogeneous Graph Contrastive Learning for Spatial Gene Expression Inference
Zhiceng Shi, Changmiao Wang, Jun Wan +1
While spatial transcriptomics (ST) has advanced our understanding of gene expression in tissue context, its high experimental cost limits its large-scale application. Predicting ST…
SpaCRD: Multimodal Deep Fusion of Histology and Spatial Transcriptomics for Cancer Region Detection
Shuailin Xue, Jun Wan, Lihua Zhang +1
Accurate detection of cancer tissue regions (CTR) enables deeper analysis of the tumor microenvironment and offers crucial insights into treatment response. Traditional CTR detecti…
Supervision-by-Hallucination-and-Transfer: A Weakly-Supervised Approach for Robust and Precise Facial Landmark Detection
Jun Wan, Yuanzhi Yao, Zhihui Lai +3
High-precision facial landmark detection (FLD) relies on high-resolution deep feature representations. However, low-resolution face images or the compression (via pooling or stride…
FGTBT: Frequency-Guided Task-Balancing Transformer for Unified Facial Landmark Detection
Jun Wan, Xinyu Xiong, Ning Chen +3
Recently, deep learning based facial landmark detection (FLD) methods have achieved considerable success. However, in challenging scenarios such as large pose variations, illuminat…
ClusIR: Towards Cluster-Guided All-in-One Image Restoration
Shengkai Hu, Jiaqi Ma, Xu Zhang +3
All-in-One Image Restoration (AiOIR) aims to recover high-quality images from diverse degradations within a unified framework. However, existing methods often fail to explicitly mo…