Search Results for author:
Found 13 papers, 4 papers with code
GCN-DevLSTM: Path Development for Skeleton-Based Action Recognition
Skeleton-based action recognition (SAR) in videos is an important but challenging task in computer vision.
Logsig-RNN: a novel network for robust and efficient skeleton-based action recognition
In this paper, we propose a novel module, namely Logsig-RNN, which is the combination of the log-signature layer and recurrent type neural networks (RNNs).
Action Recognition In Videos
Skeleton Based Action Recognition
+3
The Signature Kernel is the solution of a Goursat PDE
Recently, there has been an increased interest in the development of kernel methods for learning with sequential data.
Learning stochastic differential equations using RNN with log signature features
We illustrate the approach by approximating the unknown functional as a controlled differential equation.
Ranked #54 on
Skeleton Based Action Recognition
on NTU RGB+D 120
Skeleton-based Gesture Recognition Using Several Fully Connected Layers with Path Signature Features and Temporal Transformer Module
In this paper, we first leverage a robust feature descriptor, path signature (PS), and propose three PS features to explicitly represent the spatial and temporal motion characteristics, i. e., spatial PS (S_PS), temporal PS (T_PS) and temporal spatial PS (T_S_PS).
Ranked #1 on
Gesture Recognition
on ChaLearn 2013
Developing the Path Signature Methodology and its Application to Landmark-based Human Action Recognition
To this end, we regard the evolving landmark data as a high-dimensional path and apply non-linear path signature techniques to provide an expressive, robust, non-linear, and interpretable representation for the sequential events.
Online Signature Verification using Recurrent Neural Network and Length-normalized Path Signature
Inspired by the great success of recurrent neural networks (RNNs) in sequential modeling, we introduce a novel RNN system to improve the performance of online signature verification.
Building Fast and Compact Convolutional Neural Networks for Offline Handwritten Chinese Character Recognition
We design a nine-layer CNN for HCCR consisting of 3, 755 classes, and devise an algorithm that can reduce the networks computational cost by nine times and compress the network to 1/18 of the original size of the baseline model, with only a 0. 21% drop in accuracy.
Toward high-performance online HCCR: a CNN approach with DropDistortion, path signature and spatial stochastic max-pooling
This paper presents an investigation of several techniques that increase the accuracy of online handwritten Chinese character recognition (HCCR).
DeepWriterID: An End-to-end Online Text-independent Writer Identification System
A key feature of DeepWriterID is a new method we are proposing, called DropSegment.
Improved Deep Convolutional Neural Network For Online Handwritten Chinese Character Recognition using Domain-Specific Knowledge
Deep convolutional neural networks (DCNNs) have achieved great success in various computer vision and pattern recognition applications, including those for handwritten Chinese character recognition (HCCR).
DropSample: A New Training Method to Enhance Deep Convolutional Neural Networks for Large-Scale Unconstrained Handwritten Chinese Character Recognition
Inspired by the theory of Leitners learning box from the field of psychology, we propose DropSample, a new method for training deep convolutional neural networks (DCNNs), and apply it to large-scale online handwritten Chinese character recognition (HCCR).
Character-level Chinese Writer Identification using Path Signature Feature, DropStroke and Deep CNN
The results reveal that the path-signature feature is useful for writer identification, and the proposed DropStroke technique enhances the generalization and significantly improves performance.
