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MUSE-VAE: Multi-Scale VAE for Environment-Aware Long Term Trajectory Prediction
Accurate long-term trajectory prediction in complex scenes, where multiple agents (e. g., pedestrians or vehicles) interact with each other and the environment while attempting to accomplish diverse and often unknown goals, is a challenging stochastic forecasting problem.
Private-Shared Disentangled Multimodal VAE for Learning of Hybrid Latent Representations
Multi-modal generative models represent an important family of deep models, whose goal is to facilitate representation learning on data with multiple views or modalities.
Fast and Effective Adaptation of Facial Action Unit Detection Deep Model
In this paper, we propose a deep learning approach for facial AU detection that can easily and in a fast manner adapt to a new AU or target subject by leveraging only a few labeled samples from the new task (either an AU or subject).
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