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Found 3 papers, 1 papers with code
S-T CRF: Spatial-Temporal Conditional Random Field for Human Trajectory Prediction
Trajectory prediction is of significant importance in computer vision.
STF: Spatial Temporal Fusion for Trajectory Prediction
The main reason is that the trajectory is a kind of complex data, including spatial and temporal information, which is crucial for accurate prediction.
CSG: Curriculum Representation Learning for Signed Graph
Signed graphs are valuable for modeling complex relationships with positive and negative connections, and Signed Graph Neural Networks (SGNNs) have become crucial tools for their analysis.
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