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Found 4 papers, 0 papers with code
Large Language Model Meets Graph Neural Network in Knowledge Distillation
Building on this graph, it develops a target-prompt graph attention network to extract online deep latent features of users and services at each time slice, simultaneously considering implicit collaborative relationships between target users/services and their neighbors, as well as relevant historical QoS values.
Enhanced Fine-grained Motion Diffusion for Text-driven Human Motion Synthesis
The emergence of text-driven motion synthesis technique provides animators with great potential to create efficiently.
Structure-reinforced Transformer for Dynamic Graph Representation Learning with Edge Temporal States
The burgeoning field of dynamic graph representation learning, fuelled by the increasing demand for graph data analysis in real-world applications, poses both enticing opportunities and formidable challenges.
Human Joint Kinematics Diffusion-Refinement for Stochastic Motion Prediction
This process offers a natural way to obtain the "whitened" latents without any trainable parameters, and human motion prediction can be regarded as the reverse diffusion process that converts the noise distribution into realistic future motions conditioned on the observed sequence.
