Video Manipulation Detection via Recurrent Residual Feature Learning Networks

2019 ◽  
Author(s):  
Matthew J. Howard ◽  
Alexander S. Williamson ◽  
Narges Norouzi
Keyword(s):  
Feature Learning ◽  
Learning Networks ◽  
Video Manipulation ◽  
Manipulation Detection

scholarly journals Image Classification in HTP Test Based on Convolutional Neural Network Model

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lin Liu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Reliability And Validity ◽  
Feature Learning ◽  
Human Vision ◽  
Assessment Process ◽  
Learning Networks ◽  
Good Reliability

HTP test in psychometrics is a widely studied and applied psychological assessment technique. HTP test is a kind of projection test, which refers to the free expression of painting itself and its creativity. Therefore, the form of group psychological counselling is widely used in mental health education. Compared with traditional neural networks, deep learning networks have deeper and more network layers and can learn more complex processing functions. In this stage, image recognition technology can be used as an assistant of human vision. People can quickly get the information in the picture through retrieval. For example, you can take a picture of an object that is difficult to describe and quickly search the content related to it. Convolutional neural network, which is widely used in the image classification task of computer vision, can automatically complete feature learning on the data without manual feature extraction. Compared with the traditional test, the test can reflect the painting characteristics of different groups. After quantitative scoring, it has good reliability and validity. It has high application value in psychological evaluation, especially in the diagnosis of mental diseases. This paper focuses on the subjectivity of HTP evaluation. Convolutional neural network is a mature technology in deep learning. The traditional HTP assessment process relies on the experience of researchers to extract painting features and classification.

scholarly journals Semi-Supervised Deep Metric Learning Networks for Classification of Polarimetric SAR Data

2020 ◽  
Vol 12 (10) ◽  
pp. 1593
Author(s):  
Hongying Liu ◽  
Ruyi Luo ◽  
Fanhua Shang ◽  
Xuechun Meng ◽  
Shuiping Gou ◽  
...  
Keyword(s):  
Real World ◽  
Metric Learning ◽  
Feature Learning ◽  
Linear Mapping ◽  
Layer By Layer ◽  
Learning Networks ◽  
Distance Metric ◽  
Linear Projection ◽  
Deep Metric Learning

Recently, classification methods based on deep learning have attained sound results for the classification of Polarimetric synthetic aperture radar (PolSAR) data. However, they generally require a great deal of labeled data to train their models, which limits their potential real-world applications. This paper proposes a novel semi-supervised deep metric learning network (SSDMLN) for feature learning and classification of PolSAR data. Inspired by distance metric learning, we construct a network, which transforms the linear mapping of metric learning into the non-linear projection in the layer-by-layer learning. With the prior knowledge of the sample categories, the network also learns a distance metric under which all pairs of similarly labeled samples are closer and dissimilar samples have larger relative distances. Moreover, we introduce a new manifold regularization to reduce the distance between neighboring samples since they are more likely to be homogeneous. The categorizing is achieved by using a simple classifier. Several experiments on both synthetic and real-world PolSAR data from different sensors are conducted and they demonstrate the effectiveness of SSDMLN with limited labeled samples, and SSDMLN is superior to state-of-the-art methods.

scholarly journals ATTENTION BASED CONVOLUTIONAL NEURAL NETWORK FOR BUILDING EXTRACTION FROM VERY HIGH RESOLUTION REMOTE SENSING IMAGE

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 507-512
Author(s):  
H. R. Hosseinpoor ◽  
F. Samadzadegan
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Feature Learning ◽  
Remote Sensing Data ◽  
Superior Performance ◽  
Model Complexity ◽  
Building Extraction ◽  
Learning Networks ◽  
Benchmark Datasets ◽  
Very High

Abstract. Buildings are a major element in the formation of cities and are essential for urban mapping. The precise extraction of buildings from remote sensing data has become a significant topic and has received much attention in recent years. The recently developed convolutional neural networks have shown effective and superior performance to perform well on learning high-level and discriminative features in extracting buildings because of the outstanding feature learning and end-to-end pixel labelling abilities. However, it is difficult to use the features of different levels with a certain degree of importance that is appropriate to deep learning networks. To tackle this problem, a network based on U-Nets and the attention mechanism block was proposed. The network contains an encoder part and a decoder part and a spatial attention module. The special architecture presented in this article enhances the propagation of features and effectively utilizes the features at various levels to reduce errors. The other remarkable thing is that attention module blocks only lead to a minimal increase in model complexity. We effectively demonstrate an improvement of building extraction accuracy on challenging Potsdam and Vaihingen benchmark datasets. The results of this paper show that the proposed architecture improves building extraction in very high resolution remote sensing images compared to previous models.

scholarly journals Structure-Aware Convolution for 3D Point Cloud Classification and Segmentation

2020 ◽  
Vol 12 (4) ◽  
pp. 634 ◽  
Author(s):  
Lei Wang ◽  
Yuxuan Liu ◽  
Shenman Zhang ◽  
Jixing Yan ◽  
Pengjie Tao
Keyword(s):  
Deep Learning ◽  
Template Matching ◽  
Point Cloud ◽  
Structure Learning ◽  
Feature Learning ◽  
Point Clouds ◽  
Learning Networks ◽  
Geometric Structures ◽  
Learning Capability ◽  
3D Point Clouds

Semantic feature learning on 3D point clouds is quite challenging because of their irregular and unordered data structure. In this paper, we propose a novel structure-aware convolution (SAC) to generalize deep learning on regular grids to irregular 3D point clouds. Similar to the template-matching process of convolution on 2D images, the key of our SAC is to match the point clouds’ neighborhoods with a series of 3D kernels, where each kernel can be regarded as a “geometric template” formed by a set of learnable 3D points. Thus, the interested geometric structures of the input point clouds can be activated by the corresponding kernels. To verify the effectiveness of the proposed SAC, we embedded it into three recently developed point cloud deep learning networks (PointNet, PointNet++, and KCNet) as a lightweight module, and evaluated its performance on both classification and segmentation tasks. Experimental results show that, benefiting from the geometric structure learning capability of our SAC, all these back-end networks achieved better classification and segmentation performance (e.g., +2.77% mean accuracy for classification and +4.99% mean intersection over union (IoU) for segmentation) with few additional parameters. Furthermore, results also demonstrate that the proposed SAC is helpful in improving the robustness of networks with the constraints of geometric structures.

Joint category-level and discriminative feature learning networks for unsupervised domain adaptation

2019 ◽  
Vol 37 (6) ◽  
pp. 8499-8510 ◽  
Author(s):  
Pengyu Zhang ◽  
Junchu Huang ◽  
Zhiheng Zhou ◽  
Zengqun Chen ◽  
Junyuan Shang ◽  
...  
Keyword(s):  
Domain Adaptation ◽  
Feature Learning ◽  
Learning Networks ◽  
Unsupervised Domain Adaptation ◽  
Discriminative Feature

scholarly journals Multi-Level Joint Feature Learning for Person Re-Identification

Algorithms ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 111
Author(s):  
Shaojun Wu ◽  
Ling Gao
Keyword(s):  
Deep Learning ◽  
Feature Fusion ◽  
Feature Learning ◽  
Local Features ◽  
Image Features ◽  
Learning Networks ◽  
Fusion Model ◽  
Global Features ◽  
Multi Level ◽  
High Level

In person re-identification, extracting image features is an important step when retrieving pedestrian images. Most of the current methods only extract global features or local features of pedestrian images. Some inconspicuous details are easily ignored when learning image features, which is not efficient or robust to for scenarios with large differences. In this paper, we propose a Multi-level Feature Fusion model that combines both global features and local features of images through deep learning networks to generate more discriminative pedestrian descriptors. Specifically, we extract local features from different depths of network by the Part-based Multi-level Net to fuse low-to-high level local features of pedestrian images. Global-Local Branches are used to extract the local features and global features at the highest level. The experiments have proved that our deep learning model based on multi-level feature fusion works well in person re-identification. The overall results outperform the state of the art with considerable margins on three widely-used datasets. For instance, we achieve 96% Rank-1 accuracy on the Market-1501 dataset and 76.1% mAP on the DukeMTMC-reID dataset, outperforming the existing works by a large margin (more than 6%).

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