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Found 9 papers, 0 papers with code
PhysPT: Physics-aware Pretrained Transformer for Estimating Human Dynamics from Monocular Videos
PhysPT exploits a Transformer encoder-decoder backbone to effectively learn human dynamics in a self-supervised manner.
Interpretable Detection of Out-of-Context Misinformation with Neural-Symbolic-Enhanced Large Multimodal Model
Recent years have witnessed the sustained evolution of misinformation that aims at manipulating public opinions.
Biomechanics-Guided Facial Action Unit Detection Through Force Modeling
In this paper, we propose a biomechanics-guided AU detection approach, where facial muscle activation forces are modelled, and are employed to predict AU activation.
Knowledge-augmented Deep Learning and Its Applications: A Survey
Based on our taxonomy, we provide a systematic review of existing techniques, different from existing works that survey integration approaches agnostic to taxonomy of knowledge.
Empirical Bayesian Approaches for Robust Constraint-based Causal Discovery under Insufficient Data
Causal discovery is to learn cause-effect relationships among variables given observational data and is important for many applications.
Hybrid Message Passing With Performance-Driven Structures for Facial Action Unit Detection
In this paper, we propose a novel hybrid message passing neural network with performance-driven structures (HMP-PS), which combines complementary message passing methods and captures more possible structures in a Bayesian manner.
Dynamic Probabilistic Graph Convolution for Facial Action Unit Intensity Estimation
Second, we introduce probabilistic graph convolution that allows to perform graph convolution on the distribution of Bayesian Network structure to extract AU structural features.
Knowledge Augmented Deep Neural Networks for Joint Facial Expression and Action Unit Recognition
This paper proposes to systematically capture their dependencies and incorporate them into a deep learning framework for joint facial expression recognition and action unit detection.
Type-augmented Relation Prediction in Knowledge Graphs
Knowledge graph completion (also known as relation prediction) is the task of inferring missing facts given existing ones.
