Search Results for author:
Found 31 papers, 12 papers with code
Unsupervised Learning of Efficient Geometry-Aware Neural Articulated Representations
We propose an unsupervised method for 3D geometry-aware representation learning of articulated objects.
Bringing Rolling Shutter Images Alive with Dual Reversed Distortion
In this paper, instead of two consecutive frames, we propose to exploit a pair of images captured by dual RS cameras with reversed RS directions for this highly challenging task.
A Simple Multi-Modality Transfer Learning Baseline for Sign Language Translation
This simple baseline surpasses the previous state-of-the-art results on two sign language translation benchmarks, demonstrating the effectiveness of transfer learning.
Cross-Model Pseudo-Labeling for Semi-Supervised Action Recognition
Typically in recent work, the pseudo-labels are obtained by training a model on the labeled data, and then using confident predictions from the model to teach itself.
Towards Tokenized Human Dynamics Representation
For human action understanding, a popular research direction is to analyze short video clips with unambiguous semantic content, such as jumping and drinking.
Self-supervised Discovery of Human Actons from Long Kinematic Videos
However, methods for understanding short semantic actions cannot be directly translated to long kinematic sequences such as dancing, where it becomes challenging even to semantically label the human movements.
ACP++: Action Co-occurrence Priors for Human-Object Interaction Detection
A common problem in the task of human-object interaction (HOI) detection is that numerous HOI classes have only a small number of labeled examples, resulting in training sets with a long-tailed distribution.
Ranked #22 on
Human-Object Interaction Detection
on HICO-DET
4-bit Quantization of LSTM-based Speech Recognition Models
We investigate the impact of aggressive low-precision representations of weights and activations in two families of large LSTM-based architectures for Automatic Speech Recognition (ASR): hybrid Deep Bidirectional LSTM - Hidden Markov Models (DBLSTM-HMMs) and Recurrent Neural Network - Transducers (RNN-Ts).
Learning Skeletal Graph Neural Networks for Hard 3D Pose Estimation
While the average prediction accuracy has been improved significantly over the years, the performance on hard poses with depth ambiguity, self-occlusion, and complex or rare poses is still far from satisfactory.
Ranked #6 on
Skeleton Based Action Recognition
on NTU RGB+D 120
Data-driven discovery of interpretable causal relations for deep learning material laws with uncertainty propagation
This paper presents a computational framework that generates ensemble predictive mechanics models with uncertainty quantification (UQ).
ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training
Large-scale distributed training of Deep Neural Networks (DNNs) on state-of-the-art platforms is expected to be severely communication constrained.
Neural Articulated Radiance Field
We present Neural Articulated Radiance Field (NARF), a novel deformable 3D representation for articulated objects learned from images.
Ultra-Low Precision 4-bit Training of Deep Neural Networks
In this paper, we propose a number of novel techniques and numerical representation formats that enable, for the very first time, the precision of training systems to be aggressively scaled from 8-bits to 4-bits.
SRNet: Improving Generalization in 3D Human Pose Estimation with a Split-and-Recombine Approach
With the reduced dimensionality of less relevant body areas, the training set distribution within network branches more closely reflects the statistics of local poses instead of global body poses, without sacrificing information important for joint inference.
Ranked #7 on
Monocular 3D Human Pose Estimation
on Human3.6M
Detecting Human-Object Interactions with Action Co-occurrence Priors
A common problem in human-object interaction (HOI) detection task is that numerous HOI classes have only a small number of labeled examples, resulting in training sets with a long-tailed distribution.
Point-Set Anchors for Object Detection, Instance Segmentation and Pose Estimation
A recent approach for object detection and human pose estimation is to regress bounding boxes or human keypoints from a central point on the object or person.
Hybrid 8-bit Floating Point (HFP8) Training and Inference for Deep Neural Networks
Reducing the numerical precision of data and computation is extremely effective in accelerating deep learning training workloads.
SRINet: Learning Strictly Rotation-Invariant Representations for Point Cloud Classification and Segmentation
Point cloud analysis has drawn broader attentions due to its increasing demands in various fields.
Downhole Track Detection via Multiscale Conditional Generative Adversarial Nets
A multiscale shared convolution structure is adopted in the discriminator network to further supervise training the generator.
Reinforcement Learning Based Emotional Editing Constraint Conversation Generation
In recent years, the generation of conversation content based on deep neural networks has attracted many researchers.
Explicit Spatiotemporal Joint Relation Learning for Tracking Human Pose
We present a method for human pose tracking that is based on learning spatiotemporal relationships among joints.
An Integral Pose Regression System for the ECCV2018 PoseTrack Challenge
For the ECCV 2018 PoseTrack Challenge, we present a 3D human pose estimation system based mainly on the integral human pose regression method.
Ranked #1 on
3D Human Pose Estimation
on CHALL H80K
A Syntactically Constrained Bidirectional-Asynchronous Approach for Emotional Conversation Generation
Traditional neural language models tend to generate generic replies with poor logic and no emotion.
Integral Human Pose Regression
State-of-the-art human pose estimation methods are based on heat map representation.
Ranked #19 on
Pose Estimation
on MPII Human Pose
Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach
We propose a weakly-supervised transfer learning method that uses mixed 2D and 3D labels in a unified deep neutral network that presents two-stage cascaded structure.
Compositional Human Pose Regression
A central problem is that the structural information in the pose is not well exploited in the previous regression methods.
Deep Kinematic Pose Regression
In this work, we propose to directly embed a kinematic object model into the deep neutral network learning for general articulated object pose estimation.
Cascaded Hand Pose Regression
We extends the previous 2D cascaded object pose regression work [9] in two aspects so that it works better for 3D articulated objects.
Realtime and Robust Hand Tracking from Depth
We present a realtime hand tracking system using a depth sensor.
