eLife

Human EEG and artificial neural networks reveal disentangled representations and processing timelines of object real-world size and depth in natural images

Neural and computational evidence reveals that real-world size is a temporally late, semantically grounded, and hierarchically stable dimension of object representation in both human brains and ...
IEEE

Spatial-Spectral Contrastive Graph Neural Network for Few-Shot Hyperspectral Image Classification

Abstract: Graph Neural Networks (GNNs) have emerged as a promising solution for few-shot hyperspectral image (HSI) classification. However, existing GNN-based approaches face critical limitations in ...
GitHub

NeCT: Neural Computed Tomography

NeCT leverages deep learning to improve computed tomography (CT) image quality, supporting both static and dynamic CT reconstruction. For dynamic (4D) CT, the temporal resolution approaches the time ...
IEEE

Multi-Granularity Graph Representation and Augmentation for Image

Abstract: Graph Neural Networks (GNNs) process graph- structured data by learning features and patterns through neural networks, applied to tasks such as clustering, classification, and prediction.