Skip to content
conftrace_

intensity estimation

22 papers

Papers

Trend-Aware Supervision: On Learning Invariance for Semi-supervised Facial Action Unit Intensity Estimation AAAI 2024 Bayesian Nonparametric Learning for Point Processes with Spatial Homogeneity: A Spatial Analysis of NBA Shot Locations ICML 2022 Uniform deconvolution for Poisson Point Processes JMLR 2022 Dynamic Probabilistic Graph Convolution for Facial Action Unit Intensity Estimation CVPR 2021 Real-time Tropical Cyclone Intensity Estimation by Handling Temporally Heterogeneous Satellite Data AAAI 2021 Facial Action Unit Intensity Estimation via Semantic Correspondence Learning with Dynamic Graph Convolution AAAI 2020 G2RL: Geometry-Guided Representation Learning for Facial Action Unit Intensity Estimation IJCAI 2020 Context-Aware Feature and Label Fusion for Facial Action Unit Intensity Estimation With Partially Labeled Data ICCV 2019 Deep Random Splines for Point Process Intensity Estimation of Neural Population Data NeurIPS 2019 Joint Representation and Estimator Learning for Facial Action Unit Intensity Estimation CVPR 2019 Thinning for Accelerating the Learning of Point Processes NeurIPS 2019 Bilateral Ordinal Relevance Multi-Instance Regression for Facial Action Unit Intensity Estimation CVPR 2018 Discovering Point Lights With Intensity Distance Fields CVPR 2018 Weakly-Supervised Deep Convolutional Neural Network Learning for Facial Action Unit Intensity Estimation CVPR 2018 Z-Transforms and its Inference on Partially Observable Point Processes IJCAI 2018 Fast Bayesian Intensity Estimation for the Permanental Process ICML 2017 Poisson intensity estimation with reproducing kernels AISTATS 2017 Copula Ordinal Regression for Joint Estimation of Facial Action Unit Intensity CVPR 2016 Facial Expression Intensity Estimation Using Ordinal Information CVPR 2016 Simultaneous Optical Flow and Intensity Estimation From an Event Camera CVPR 2016 Latent Trees for Estimating Intensity of Facial Action Units CVPR 2015 Optimality of Poisson Processes Intensity Learning with Gaussian Processes JMLR 2015