Search Results for author:
Found 19 papers, 11 papers with code
3D Human Pose and Shape Estimation via HybrIK-Transformer
HybrIK relies on a combination of analytical inverse kinematics and deep learning to produce more accurate 3D pose estimation from 2D monocular images.
Ranked #23 on
3D Human Pose Estimation
on 3DPW
SMPL-IK: Learned Morphology-Aware Inverse Kinematics for AI Driven Artistic Workflows
Inverse Kinematics (IK) systems are often rigid with respect to their input character, thus requiring user intervention to be adapted to new skeletons.
N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting
Recent progress in neural forecasting accelerated improvements in the performance of large-scale forecasting systems.
Motion Inbetweening via Deep $Δ$-Interpolator
We show that the task of synthesizing human motion conditioned on a set of key frames can be solved more accurately and effectively if a deep learning based interpolator operates in the delta mode using the spherical linear interpolator as a baseline.
Ranked #1 on
Motion Synthesis
on LaFAN1
Neural forecasting at scale
We study the problem of efficiently scaling ensemble-based deep neural networks for multi-step time series (TS) forecasting on a large set of time series.
ProtoRes: Proto-Residual Network for Pose Authoring via Learned Inverse Kinematics
Our work focuses on the development of a learnable neural representation of human pose for advanced AI assisted animation tooling.
Adaptive filters for the moving target indicator system
Regularization, also known in radar literature as sample covariance loading, can be used to combat both ill conditioning of the original problem and contamination of the empirical covariance by the desired signal for the adaptive algorithms based on sample covariance matrix inversion.
Optimization of loading factor preventing target cancellation
Adaptive algorithms based on sample matrix inversion belong to an important class of algorithms used in radar target detection to overcome prior uncertainty of interference covariance.
Optimization over Random and Gradient Probabilistic Pixel Sampling for Fast, Robust Multi-Resolution Image Registration
This paper presents an approach to fast image registration through probabilistic pixel sampling.
Uncertainty driven probabilistic voxel selection for image registration
This paper presents a novel probabilistic voxel selection strategy for medical image registration in time-sensitive contexts, where the goal is aggressive voxel sampling (e. g. using less than 1% of the total number) while maintaining registration accuracy and low failure rate.
N-BEATS neural network for mid-term electricity load forecasting
We show that our proposed deep neural network modeling approach based on the deep neural architecture is effective at solving the mid-term electricity load forecasting problem.
FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic Forecasting
Forecasting of multivariate time-series is an important problem that has applications in traffic management, cellular network configuration, and quantitative finance.
Meta-learning framework with applications to zero-shot time-series forecasting
Can meta-learning discover generic ways of processing time series (TS) from a diverse dataset so as to greatly improve generalization on new TS coming from different datasets?
Weakly Supervised Few-shot Object Segmentation using Co-Attention with Visual and Semantic Embeddings
Our results show that few-shot segmentation benefits from utilizing word embeddings, and that we are able to perform few-shot segmentation using stacked joint visual semantic processing with weak image-level labels.
One-Shot Weakly Supervised Video Object Segmentation
Conventional few-shot object segmentation methods learn object segmentation from a few labelled support images with strongly labelled segmentation masks.
CLAREL: Classification via retrieval loss for zero-shot learning
We address the problem of learning fine-grained cross-modal representations.
N-BEATS: Neural basis expansion analysis for interpretable time series forecasting
We focus on solving the univariate times series point forecasting problem using deep learning.
Time Series
Time-Series Few-Shot Learning with Heterogeneous Channels
+1
Adaptive Cross-Modal Few-Shot Learning
Through a series of experiments, we show that by this adaptive combination of the two modalities, our model outperforms current uni-modality few-shot learning methods and modality-alignment methods by a large margin on all benchmarks and few-shot scenarios tested.
TADAM: Task dependent adaptive metric for improved few-shot learning
We further propose a simple and effective way of conditioning a learner on the task sample set, resulting in learning a task-dependent metric space.
