Search Results for author:
Found 9 papers, 1 papers with code
Word-level Sign Language Recognition with Multi-stream Neural Networks Focusing on Local Regions
Thus in this work, we utilized local region images of both hands and face, along with skeletal information to capture local information and the positions of both hands relative to the body, respectively.
Ranked #2 on
Sign Language Recognition
on WLASL100
(using extra training data)
Distortion-Adaptive Grape Bunch Counting for Omnidirectional Images
Because conventional object counting methods cannot handle the distortion of omnidirectional images, we propose to process them using stereographic projection, which enables conventional methods to obtain a good approximation of the density function.
Advances of Scene Text Datasets
This article introduces publicly available datasets in scene text detection and recognition.
ShakeDrop Regularization for Deep Residual Learning
In this paper, to relieve the overfitting effect of ResNet and its improvements (i. e., Wide ResNet, PyramidNet, and ResNeXt), we propose a new regularization method called ShakeDrop regularization.
ShakeDrop regularization
This paper proposes a powerful regularization method named \textit{ShakeDrop regularization}.
Automatic Generation of Typographic Font from a Small Font Subset
The proposed method uses strokes from given samples for font generation.
Deep Pyramidal Residual Networks with Separated Stochastic Depth
On general object recognition, Deep Convolutional Neural Networks (DCNNs) achieve high accuracy.
A Generic Method for Automatic Ground Truth Generation of Camera-captured Documents
The method is generic, language independent and can be used for generation of labeled documents datasets (both scanned and cameracaptured) in any cursive and non-cursive language, e. g., English, Russian, Arabic, Urdu, etc.
Scalable Solution for Approximate Nearest Subspace Search
The existing methods for the problem are, however, useless in a large-scale problem with a large number of subspaces and high dimensionality of the feature space.
