scholarly journals Sketch-Specific Data Augmentation for Freehand Sketch Recognition

2021 ◽  
Author(s):  
Ying Zheng ◽  
Hongxun Yao ◽  
Xiaoshuai Sun ◽  
Shengping Zhang ◽  
Sicheng Zhao ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Sketch Recognition ◽  
Specific Data ◽  
Freehand Sketch

scholarly journals Attention-Aware Adversarial Network for Person Re-Identification

2019 ◽  
Vol 9 (8) ◽  
pp. 1550 ◽  
Author(s):  
Aihong Shen ◽  
Huasheng Wang ◽  
Junjie Wang ◽  
Hongchen Tan ◽  
Xiuping Liu ◽  
...  
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Data Augmentation ◽  
Fundamental Problem ◽  
Training Data ◽  
Specific Data ◽  
Training Strategy ◽  
Adversarial Network ◽  
Benchmark Datasets ◽  
Adversarial Training

Person re-identification (re-ID) is a fundamental problem in the field of computer vision. The performance of deep learning-based person re-ID models suffers from a lack of training data. In this work, we introduce a novel image-specific data augmentation method on the feature map level to enforce feature diversity in the network. Furthermore, an attention assignment mechanism is proposed to enforce that the person re-ID classifier focuses on nearly all important regions of the input person image. To achieve this, a three-stage framework is proposed. First, a baseline classification network is trained for person re-ID. Second, an attention assignment network is proposed based on the baseline network, in which the attention module learns to suppress the response of the current detected regions and re-assign attentions to other important locations. By this means, multiple important regions for classification are highlighted by the attention map. Finally, the attention map is integrated in the attention-aware adversarial network (AAA-Net), which generates high-performance classification results with an adversarial training strategy. We evaluate the proposed method on two large-scale benchmark datasets, including Market1501 and DukeMTMC-reID. Experimental results show that our algorithm performs favorably against the state-of-the-art methods.

scholarly journals Research on Recognition Effect of DSCN Network Structure in Hand-Drawn Sketch

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Qunjing Ji
Keyword(s):  
Neural Network ◽  
Recognition Accuracy ◽  
Rapid Development ◽  
Image Features ◽  
Sketch Recognition ◽  
Freehand Sketch ◽  
Classical Models ◽  
Manual Selection ◽  
Stroke Sequence ◽  
Deep Learning Model

With the rapid development of image recognition technology, freehand sketch recognition has attracted more and more attention. How to achieve good recognition effect in the absence of color and texture information is the key to the development of freehand sketch recognition. Traditional nonlearning classical models are highly dependent on manual selection features. To solve this problem, a neural network sketch recognition method based on DSCN structure is proposed in this paper. Firstly, the stroke sequence of the sketch is drawn; then, the feature is extracted according to the stroke sequence combined with neural network, and the extracted image features are used as the input of the model to construct the time relationship between different image features. Through the control experiment on TU-Berlin dataset, the results show that, compared with the traditional nonlearning methods, HOG-SVM, SIFT-Fisher Vector, MKL-SVM, and FV-SP, the recognition accuracy of DSCN network is improved by 15.8%, 10.3%, 6.0%, and 2.9%, respectively. Compared with the classical deep learning model, Alex-Net, the recognition accuracy is improved by 5.6%. The above results show that the DSCN network proposed in this paper has strong ability of feature extraction and nonlinear expression and can effectively improve the recognition accuracy of hand-painted sketches after introducing the stroke order.

A survey on freehand sketch recognition and retrieval

2019 ◽  
Vol 89 ◽  
pp. 67-87 ◽  
Author(s):  
Xianlin Zhang ◽  
Xueming Li ◽  
Yang Liu ◽  
Fangxiang Feng
Keyword(s):  
Sketch Recognition ◽  
Freehand Sketch

scholarly journals Efficient Deep Learning Model for COVID-19 Detection in large CT images datasets: A cross-dataset analysis

2020 ◽  
Author(s):  
Pedro Silva ◽  
Eduardo Luz ◽  
Guilherme Silva ◽  
Gladston Moreira ◽  
Rodrigo Silva ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
State Of The Art ◽  
Ct Images ◽  
Critical Factors ◽  
Specific Data ◽  
Dataset Analysis ◽  
Detection And Diagnosis ◽  
Deep Learning Model ◽  
Early Detection And Diagnosis

Abstract Early detection and diagnosis are critical factors to control the COVID-19 spreading. A number of deep learning-based methodologies have been recently proposed for COVID-19 screening in CT scans as a tool to automate and help with the diagnosis. To achieve these goals, in this work, we propose a slice voting-based approach extending the EfficientNet Family of deep artificial neural networks.We also design a specific data augmentation process and transfer learning for such task.Moreover, a cross-dataset study is performed into the two largest datasets to date. The proposed method presents comparable results to the state-of-the-art methods and the highest accuracy to date on both datasets (accuracy of 87.60\% for the COVID-CT dataset and accuracy of 98.99% for the SARS-CoV-2 CT-scan dataset). The cross-dataset analysis showed that the generalization power of deep learning models is far from acceptable for the task since accuracy drops from 87.68% to 56.16% on the best evaluation scenario.These results highlighted that the methods that aim at COVID-19 detection in CT-images have to improve significantly to be considered as a clinical option and larger and more diverse datasets are needed to evaluate the methods in a realistic scenario.

Face-Specific Data Augmentation for Unconstrained Face Recognition

2019 ◽  
Vol 127 (6-7) ◽  
pp. 642-667 ◽  
Author(s):  
Iacopo Masi ◽  
Anh Tuấn Trần ◽  
Tal Hassner ◽  
Gozde Sahin ◽  
Gérard Medioni
Keyword(s):  
Face Recognition ◽  
Data Augmentation ◽  
Specific Data ◽  
Unconstrained Face Recognition ◽  
Unconstrained Face

scholarly journals Segmentation of Online Freehand Sketching Based on Speed Feature

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Guanfeng Wang ◽  
Shouxia Wang ◽  
Jingjing Kang ◽  
Shuxia Wang
Keyword(s):  
User Study ◽  
Feature Points ◽  
Sketch Recognition ◽  
Average Speed ◽  
Freehand Sketch ◽  
Novel Method ◽  
Geometric Primitives ◽  
Threshold Technique

We present a novel method to extract speed feature points for segmenting hand-drawn strokes into geometric primitives. The method consists of three steps. Firstly, the input strokes are classified into uniform and nonuniform speed strokes, representing a stroke drawn at relatively constant or uneven speeds, respectively. Then, a sharpening filter is used to enhance the peak features of the uniform speed strokes. Finally, a three-threshold technique that uses the average speed of the pen and its upper and lower deviations is used to extract speed feature points of strokes. We integrate the proposed method into our freehand sketch recognition (FSR) system to improve its robustness to support multiprimitive strokes. Through a user study with 8 participants, we demonstrate that the proposed method achieves higher segmentation efficiency in finding speed feature points than the existing method based on a single speed threshold.

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