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Found 4 papers, 3 papers with code
MedFMC: A Real-world Dataset and Benchmark For Foundation Model Adaptation in Medical Image Classification
Foundation models, often pre-trained with large-scale data, have achieved paramount success in jump-starting various vision and language applications.
Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting
SFTGNN could effectively learn hidden spatial-temporal dependencies by a novel fusion operation of various spatial and temporal graphs, which is generated by a data-driven method.
Ranked #4 on
Traffic Prediction
on BJTaxi
Automatic Data Augmentation for 3D Medical Image Segmentation
Data augmentation is an effective and universal technique for improving generalization performance of deep neural networks.
3D Graph Convolutional Networks with Temporal Graphs: A Spatial Information Free Framework For Traffic Forecasting
(2) We propose an original 3D graph convolution model to model the spatio-temporal data more accurately.
Ranked #2 on
Traffic Prediction
on PeMS-M
