Deep Metric Learning Assisted by Intra-variance in A Semi-supervised View of Learning

Deep metric learning aims to construct an embedding space where samples of the same class are close to each other, while samples of different classes are far away from each other. Most existing deep metric learning methods attempt to maximize the difference of inter-class features. And semantic related information is obtained by increasing the distance between samples of different classes in the embedding space. However, compressing all positive samples together while creating large margins between different classes unconsciously destroys the local structure between similar samples. Ignoring the intra-class variance contained in the local structure between similar samples, the embedding space obtained from training receives lower generalizability over unseen classes, which would lead to the network overfitting the training set and crashing on the test set. To address these considerations, this paper designs a self-supervised generative assisted ranking framework that provides a semi-supervised view of intra-class variance learning scheme for typical supervised deep metric learning. Specifically, this paper performs sample synthesis with different intensities and diversity for samples satisfying certain conditions to simulate the complex transformation of intra-class samples. And an intra-class ranking loss function is designed using the idea of self-supervised learning to constrain the network to maintain the intra-class distribution during the training process to capture the subtle intra-class variance. With this approach, a more realistic embedding space can be obtained in which global and local structures of samples are well preserved, thus enhancing the effectiveness of downstream tasks. Extensive experiments on four benchmarks have shown that this approach surpasses state-of-the-art method

A Dense-Depth Representation for VLAD descriptors in Content-Based Image Retrieval

The recent advances brought by deep learning allowed to improve the performance in image retrieval tasks. Through the many convolutional layers, available in a Convolutional Neural Network (CNN), it is possible to obtain a hierarchy of features from the evaluated image. At every step, the patches extracted are smaller than the previous levels and more representative. Following this idea, this paper introduces a new detector applied on the feature maps extracted from pre-trained CNN. Specifically, this approach lets to increase the number of features in order to increase the performance of the aggregation algorithms like the most famous and used VLAD embedding. The proposed approach is tested on different public datasets: Holidays, Oxford5k, Paris6k and UKB

Dynamic Response Analysis of Microflow Electrochemical Sensors with Two Types of Elastic Membrane

The Molecular Electric Transducer (MET), widely applied for vibration measurement, has excellent sensitivity and dynamic response at low frequencies. The elastic membrane in the MET is a significant factor with an obvious effect on the performance of the MET in the low frequency domain and is the focus of this paper. In simulation experiments, the elastic membrane and the reaction cavity of the MET were analysed in a model based on the multiphysics finite element method. Meanwhile, the effects caused by the elastic membrane elements are verified in this paper. With the numerical simulation and practical experiments, a suitable elastic membrane can be designed for different cavity structures. Thus, the MET can exhibit the best dynamic response characteristics to measure the vibration signals. With the new method presented in this paper, it is possible to develop and optimize the characteristics of the MET effectively, and the dynamic characteristics of the MET can be improved in a thorough and systematic manner

Diabetic Retinal Grading Using Attention-Based Bilinear Convolutional Neural Network and Complement Cross Entropy

Diabetic retinopathy (DR) is a common complication of diabetes mellitus (DM), and it is necessary to diagnose DR in the early stages of treatment. With the rapid development of convolutional neural networks in the field of image processing, deep learning methods have achieved great success in the field of medical image processing. Various medical lesion detection systems have been proposed to detect fundus lesions. At present, in the image classification process of diabetic retinopathy, the fine-grained properties of the diseased image are ignored and most of the retinopathy image data sets have serious uneven distribution problems, which limits the ability of the network to predict the classification of lesions to a large extent. We propose a new non-homologous bilinear pooling convolutional neural network model and combine it with the attention mechanism to further improve the network’s ability to extract specific features of the image. The experimental results show that, compared with the most popular fundus image classification models, the network model we proposed can greatly improve the prediction accuracy of the network while maintaining computational efficiency

AC Equivalent Circuit Model of an Electrochemical Accelerometer for 3D Numerical Simulation in the Low-Frequency Range

The electrochemical principles presented in this paper can be applied to the manufacture of vibration sensors for oil and gas exploration, as well as long-period vibration sensors for the observation of natural earthquakes. To facilitate the manufacture of high-volume electrochemical accelerometer (EAM), this paper presents an AC equivalent circuit model of an EAM in a low-frequency range. A 3D time-dependent numerical simulation based on finite element analysis was designed to combine a complex chemical reaction with electric circuit theory. A sensitive chip channel model was constructed by using partial differential equations and the problem caused by a designed mathematical model was solved by using multi-physics finite element analysis. When the electrochemical properties of an electrochemical vibration sensor and its design parameters as well as the parameters of the AC equivalent circuit model are considered, the abstract processing of the sensor on the equivalent circuit is better accomplished. The effectiveness of the proposed simulation model and the equivalent circuit model were verified by comparing the amplitude-frequency characteristic curve of the equivalent circuit with the amplitude-frequency characteristic curve of the single-channel simulation model of the sensitive chip. These model not only have great significance for the design guidance of an external conditioning circuit but also provide an effective method to decouple the output signal and noise of the sensor reaction cavity

Deep Hash Remote-Sensing Image Retrieval Assisted by Semantic Cues

With the significant and rapid growth in the number of remote-sensing images, deep hash methods have become a research topic. The main work of deep hash method is to build a discriminate embedding space through the similarity relation between sample pairs and then map the feature vector into Hamming space for hashing retrieval. We demonstrate that adding a binary classification label as a kind of semantic cue could further improve the retrieval performance. In this work, we propose a new method, which we called deep hashing, based on classification label (DHCL). First, we propose a network architecture, which can classify and retrieve remote-sensing images under a unified framework, and the classification labels are further utilized as the semantic cues to assist in network training. Second, we propose a hash code structure, which can integrate the classification results into the hash-retrieval process to improve accuracy. Finally, we validate the performance of the proposed method on several remote-sensing image datasets and show the superiority of our method

Dynamic Response Analysis of Microflow Electrochemical Sensors with Two Types of Elastic Membrane

The Molecular Electric Transducer (MET), widely applied for vibration measurement, has excellent sensitivity and dynamic response at low frequencies. The elastic membrane in the MET is a significant factor with an obvious effect on the performance of the MET in the low frequency domain and is the focus of this paper. In simulation experiments, the elastic membrane and the reaction cavity of the MET were analysed in a model based on the multiphysics finite element method. Meanwhile, the effects caused by the elastic membrane elements are verified in this paper. With the numerical simulation and practical experiments, a suitable elastic membrane can be designed for different cavity structures. Thus, the MET can exhibit the best dynamic response characteristics to measure the vibration signals. With the new method presented in this paper, it is possible to develop and optimize the characteristics of the MET effectively, and the dynamic characteristics of the MET can be improved in a thorough and systematic manner

Fusing Feature Distribution Entropy with R-MAC Features in Image Retrieval

Image retrieval based on a convolutional neural network (CNN) has attracted great attention among researchers because of the high performance. The pooling method has become a research hotpot in the task of image retrieval in recent years. In this paper, we propose the feature distribution entropy (FDE) to measure the difference of regional distribution information in the feature maps from CNNs. We propose a novel pooling method, which fuses our proposed FDE with region maximum activations of convolutions (R-MAC) features to improve the performance of image retrieval, as it takes the advantage of regional distribution information in the feature maps. Compared with the descriptors computed by R-MAC pooling, our proposed method considers not only the most significant feature values of each region in feature map, but also the distribution difference in different regions. We utilize the histogram of feature values to calculate regional distribution entropy and concatenate the regional distribution entropy into FDE, which is further normalized and fused with R-MAC feature vectors by weighted summation to generate the final feature descriptors. We have conducted experiments on public datasets and the results demonstrate that our proposed method could produce better retrieval performances than existing state-of-the-art algorithms. Further, higher performance could be achieved by performing these post-processing on the improved feature descriptors