Search Results for author:
Found 9 papers, 4 papers with code
A comparison study on patient-psychologist voice diarization
Conversations between a clinician and a patient, in natural conditions, are valuable sources of information for medical follow-up.
Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations
Large-scale recommendation systems are characterized by their reliance on high cardinality, heterogeneous features and the need to handle tens of billions of user actions on a daily basis.
Ranked #1 on
Recommendation Systems
on MovieLens 20M
(HR@10 (full corpus) metric)
Adversarial Refinement Network for Human Motion Prediction
Specifically, we take both the historical motion sequences and coarse prediction as input of our cascaded refinement network to predict refined human motion and strengthen the refinement network with adversarial error augmentation.
Adversarial Semantic Data Augmentation for Human Pose Estimation
Human pose estimation is the task of localizing body keypoints from still images.
Bayesian Group Selection in Logistic Regression with Application to MRI Data Analysis
We consider Bayesian logistic regression models with group-structured covariates.
Methodology
Anti-Confusing: Region-Aware Network for Human Pose Estimation
In this work, we propose a novel framework named Region-Aware Network (RANet), which learns the ability of anti-confusing in case of heavy occlusion, nearby person and symmetric appearance, for human pose estimation.
Consistent Bayesian Sparsity Selection for High-dimensional Gaussian DAG Models with Multiplicative and Beta-mixture Priors
Estimation of the covariance matrix for high-dimensional multivariate datasets is a challenging and important problem in modern statistics.
Statistics Theory Methodology Statistics Theory
Sparse Photometric 3D Face Reconstruction Guided by Morphable Models
We present a novel 3D face reconstruction technique that leverages sparse photometric stereo (PS) and latest advances on face registration/modeling from a single image.
A Learning-based Framework for Hybrid Depth-from-Defocus and Stereo Matching
Depth from defocus (DfD) and stereo matching are two most studied passive depth sensing schemes.
