Few-shot image classification : current status and research trends

Conventional image classification methods usually require a large number of training samples for the training model. However, in practical scenarios, the amount of available sample data is often insufficient, which easily leads to overfitting in network construction. Few-shot learning provides an effective solution to this problem and has been a hot research topic. This paper provides an intensive survey on the state-of-the-art techniques in image classification based on few-shot learning. According to the different deep learning mechanisms, the existing algorithms are di-vided into four categories: transfer learning based, meta-learning based, data augmentation based, and multimodal based methods. Transfer learning based methods transfer useful prior knowledge from the source domain to the target domain. Meta-learning based methods employ past prior knowledge to guide the learning of new tasks. Data augmentation based methods expand the amount of sample data with auxiliary information. Multimodal based methods use the information of the auxiliary modal to facilitate the implementation of image classification tasks. This paper also summarizes the few-shot image datasets available in the literature, and experimental results tested by some representative algorithms are provided to compare their performance and analyze their pros and cons. In addition, the application of existing research outcomes on few-shot image classification in different practical fields are discussed. Finally, a few future research directions are iden-tified. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Practical Deep Dispersed Watermarking with Synchronization and Fusion

Deep learning based blind watermarking works have gradually emerged and achieved impressive performance. However, previous deep watermarking studies mainly focus on fixed low-resolution images while paying less attention to arbitrary resolution images, especially widespread high-resolution images nowadays. Moreover, most works usually demonstrate robustness against typical non-geometric attacks (\textit{e.g.}, JPEG compression) but ignore common geometric attacks (\textit{e.g.}, Rotate) and more challenging combined attacks. To overcome the above limitations, we propose a practical deep \textbf{D}ispersed \textbf{W}atermarking with \textbf{S}ynchronization and \textbf{F}usion, called \textbf{\proposed}. Specifically, given an arbitrary-resolution cover image, we adopt a dispersed embedding scheme which sparsely and randomly selects several fixed small-size cover blocks to embed a consistent watermark message by a well-trained encoder. In the extraction stage, we first design a watermark synchronization module to locate and rectify the encoded blocks in the noised watermarked image. We then utilize a decoder to obtain messages embedded in these blocks, and propose a message fusion strategy based on similarity to make full use of the consistency among messages, thus determining a reliable message. Extensive experiments conducted on different datasets convincingly demonstrate the effectiveness of our proposed {\proposed}. Compared with state-of-the-art approaches, our blind watermarking can achieve better performance: averagely improve the bit accuracy by 5.28\% and 5.93\% against single and combined attacks, respectively, and show less file size increment and better visual quality. Our code is available at https://github.com/bytedance/DWSF.Comment: Accpeted by ACM MM 202

A Conversation is Worth A Thousand Recommendations: A Survey of Holistic Conversational Recommender Systems

Conversational recommender systems (CRS) generate recommendations through an interactive process. However, not all CRS approaches use human conversations as their source of interaction data; the majority of prior CRS work simulates interactions by exchanging entity-level information. As a result, claims of prior CRS work do not generalise to real-world settings where conversations take unexpected turns, or where conversational and intent understanding is not perfect. To tackle this challenge, the research community has started to examine holistic CRS, which are trained using conversational data collected from real-world scenarios. Despite their emergence, such holistic approaches are under-explored. We present a comprehensive survey of holistic CRS methods by summarizing the literature in a structured manner. Our survey recognises holistic CRS approaches as having three components: 1) a backbone language model, the optional use of 2) external knowledge, and/or 3) external guidance. We also give a detailed analysis of CRS datasets and evaluation methods in real application scenarios. We offer our insight as to the current challenges of holistic CRS and possible future trends.Comment: Accepted by 5th KaRS Workshop @ ACM RecSys 2023, 8 page

Research on the Structure of Peanut Allergen Protein Ara h1 Based on Aquaphotomics

Peanut allergy is becoming a life-threatening disease that could induce severe allergic reactions in modern society, especially for children. The most promising method applied for deallergization is heating pretreatment. However, the mechanism from the view of spectroscopy has not been illustrated. In this study, near-infrared spectroscopy (NIRS) combined with aquaphotomics was introduced to help us understand the detailed structural changes information during the heating process. First, near-infrared (NIR) spectra of Ara h1 were acquired from 25 to 80°C. Then, aquaphotomics processing tools including principal component analysis (PCA), continuous wavelet transform (CWT), and two-dimensional correlation spectroscopy (2D-COS) were utilized for better understanding the thermodynamic changes, secondary structure, and the hydrogen bond network of Ara h1. The results indicated that about 55°C could be a key temperature, which was the structural change point. During the heating process, the hydrogen bond network was destroyed, free water was increased, and the content of protein secondary structure was changed. Moreover, it could reveal the interaction between the water structure and Ara h1 from the perspective of water molecules, and explain the effect of temperature on the Ara h1 structure and hydrogen-bonding system. Thus, this study described a new way to explore the thermodynamic properties of Ara h1 from the perspective of spectroscopy and laid a theoretical foundation for the application of temperature-desensitized protein products

Cyclic axial compressive performance of hybrid double-skin tubular square columns

This paper presents an experimental study on the cyclic axial compressive behavior of FRP-concrete-steel hybrid double-skin tubular columns. The square column specimens were cast with an external Fiber Reinforced Polymer jackets, inner steel tube and concrete in between. The height of the columns was 500 mm and the side dimension was 150 mm. The effects of loading scheme, void ratio and diameter-thickness ratio on axial compression behavior were investigated. A total of eight columns were tested under monotonic and cyclic axial compression. The experimental results show that the effect of loading scheme on axial stress-strain envelope curve and the peak load were not significant, and the ultimate state of the square columns subjected to cyclic axial compression was very similar to that of specimens subjected to monotonic axial compression. Besides, compared with void ratio, the diameter-thickness ratio of the inner steel tube has significant influence on the peak load of the columns when subjected to cyclic axial compression

Giant power output in lead-free ferroelectrics by shock-induced phase transition

The force-electric effect in ferroelectrics is characterized by the release of bound charge during pressure/shock-induced depolarization. In contrast to other electrical energy storage systems, the charge-storage/release by the force-electric effect of ferroelectrics is determined by polarization switching or polar-nonpolar phase transition. This offers a further set of options for materials design in the realm of energy conversion, especially for the high power density applications. Here, we report that a ferroelectric ceramic, N a 0.5 B i 0.5 Ti O 3 (NBT), can generate a high power output ( 3.04 × 10 8 W / kg ) under shock compression, which is one of the highest values achieved by the force-electric effect. The in situ synchrotron x-ray diffraction studies reveal that this power output mainly arises from a polar-nonpolar phase transition (rhombohedral-orthorhombic). First-principles calculations show that this is a first-order phase transition that undergoes two-step structure changes. These results extend the application of the force-electric effect and are a key step in understanding the phase transition behaviors of NBT under high pressure

Few-Shot Image Classification: Current Status and Research Trends

Conventional image classification methods usually require a large number of training samples for the training model. However, in practical scenarios, the amount of available sample data is often insufficient, which easily leads to overfitting in network construction. Few-shot learning provides an effective solution to this problem and has been a hot research topic. This paper provides an intensive survey on the state-of-the-art techniques in image classification based on few-shot learning. According to the different deep learning mechanisms, the existing algorithms are divided into four categories: transfer learning based, meta-learning based, data augmentation based, and multimodal based methods. Transfer learning based methods transfer useful prior knowledge from the source domain to the target domain. Meta-learning based methods employ past prior knowledge to guide the learning of new tasks. Data augmentation based methods expand the amount of sample data with auxiliary information. Multimodal based methods use the information of the auxiliary modal to facilitate the implementation of image classification tasks. This paper also summarizes the few-shot image datasets available in the literature, and experimental results tested by some representative algorithms are provided to compare their performance and analyze their pros and cons. In addition, the application of existing research outcomes on few-shot image classification in different practical fields are discussed. Finally, a few future research directions are identified

Qualitative and Quantitative Analysis of Heparin during Precipitation by Near-Infrared Spectroscopy

ABSTRACT Heparin is a glycosaminoglycan (GAG) that plays an important role in the blood coagulation system. Its quality is of great importance, so it is necessary to develop a fast analytical method during the manufacture process to analyse the quality of heparin produced. In this study, the heparin contents of 80 samples collected from five batches during the precipitation process were analysed using nearinfrared (NIR) spectroscopy and a chemometrics approach. This was done in order to improve the efficiency, to understand the process directly and accurately, and to reduce the variation in product quality during manufacturing. First, the principal component analysis (PCA) method was applied to study the stability and the characteristic trajectory of all of the batches of heparin from ethanol precipitation qualitatively. Then, partial least square (PLS) regression, combined with several spectral pretreatment methods and variable selection methods, was performed to quantitatively predict the heparin contents during the ethanol precipitation process. The results showed that the values of the coefficient of determination (R2), the root mean square error of prediction (RMSEP) and the residual predictive deviation (RPD) were 0.974, 1.105 g/l and 6.37, respectively. This approach has a considerable potential for on-line monitoring of the heparin contents of each ethanol precipitation process. Additionally, it will cause a large transformation in the pattern of production of the pharmaceutical industry by application of NIR spectroscopy in the future

Railway Fastener Fault Diagnosis Based on Generative Adversarial Network and Residual Network Model

The present work aimed at the problems of less negative samples and more positive samples in rail fastener fault diagnosis and low detection accuracy of heavy manual patrol inspection tasks. Exploiting the capacity of a Convolution Neural Network (CNN) to process unbalanced data to solve tedious and inefficient manual processing, a fault diagnosis method based on a Generative Adversarial Network (GAN) and a Residual Network (ResNet) was developed. First, GAN was used to track the distribution of rail fastener failure data. To study the noise distribution, the mapping relationship between image data was established. Additional real fault samples were then generated to balance and extend the existing data sets, and these data sets were used as input to ResNet for recognition and detection training. Finally, the average accuracy of multiple experiments was used as the evaluation index. The experimental results revealed that the fault diagnosis of rail fastener based on GAN and ResNet could improve the fault detection accuracy in the case of a serious shortage of fault data

Simultaneous multi-component seismic denoising and reconstruction via K-SVD

Data denoising and reconstruction play an increasingly significant role in seismic prospecting for their value in enhancing effective signals, dealing with surface obstacles and reducing acquisition costs. In this paper, we propose a novel method to denoise and reconstruct multicomponent seismic data simultaneously. This method lies within the framework of machine learning and the key points are defining a suitable weight function and a modified inner product operator. The purpose of these two processes are to perform missing data machine learning when the random noise deviation is unknown, and building a mathematical relationship for each component to incorporate all the information of multi-component data. Two examples, using synthetic and real multicomponent data, demonstrate that the new method is a feasible alternative for multi-component seismic data processing