Discriminative Information Research Articles

An attention-based deep network for plant disease classification

Plant disease classification using machine learning in a real agricultural field environment is a difficult task. Often, an automated plant disease diagnosis method might fail to capture and interpret discriminatory information due to small variations among leaf sub-categories. Yet, modern Convolutional Neural Networks (CNNs) have achieved decent success in discriminating various plant diseases using leave images. A few existing methods have applied additional pre-processing modules or sub-networks to tackle this challenge. Sometimes, the feature maps ignore partial information for holistic description by part-mining. A deep CNN that emphasizes integration of partial descriptiveness of leaf regions is proposed in this work. The efficacious attention mechanism is integrated with high-level feature map of a base CNN for enhancing feature representation. The proposed method focuses on important diseased areas in leaves, and employs an attention weighting scheme for utilizing useful neighborhood information. The proposed Attention-based network for Plant Disease Classification (APDC) method has achieved state-of-the-art performances on four public plant datasets containing visual/thermal images. The best top-1 accuracies attained by the proposed APDC are: PlantPathology 97.74%, PaddyCrop 99.62%, PaddyDoctor 99.65%, and PlantVillage 99.97%. These results justify the suitability of proposed method.

Intersectionality in Health Communication: How Health Communication Influences the Association Between Intersectional Discrimination and Health Information Seeking

With an intersectional orientation, we examine associations between discrimination, health communication, and information-seeking intention about HIV prevention in the context of pre-exposure prophylaxis (PrEP) among cisgender women through an online survey (N = 341). We elaborate on the idea that intersectional discrimination is a social determinant of health by considering the context with differing power relations—day-to-day life and the healthcare field—based on Bourdieu’s field theory, and explore moderating roles of health communication in this relationship according to the structural influence model of communication (SIM). The relationships between intersectional discrimination and intention show different patterns by the context in which intersectional experiences are considered, and the relationships are moderated differently by the mode of health communication. We discuss how to conduct intersectionality-informed health communication research without sacrificing intersectionality’s foundational foci.

A single-layer dense convolutional reversible residual network for bearing fault diagnosis based on differential local adaptive

Abstract For the cross-domain fault diagnosis of industrial bearings under different working conditions and noise, most current domain adaptation methods in transfer learning only focus on either marginal distribution alignment or conditional distribution alignment. They fail to adequately combine discriminative and global distribution information. Furthermore, the majority of models have a very high parameter count and memory utilization, which makes it challenging to use them in real-world industrial situations. Therefore, a single-layer densely connected reversible residual network based on differential local adaptation is proposed. This network is more competitive in industrial applications than other fault diagnosis models since it not only uses less memory and has fewer parameters, but it also shows superior cross-domain fault diagnostic capacity in noisy situations. Additionally, to extract discriminative and global domain-invariant features, a domain adaptation module is created that takes into account local and global data distributions differently. Multiple transfer tasks and two distinct datasets are used to validate the model. Comparative tests reveal that the suggested model uses less memory and requires fewer parameters to attain good accuracy and transferability.

Residual channel attention based sample adaptation few-shot learning for hyperspectral image classification.

Few-shot learning (FSL) uses prior knowledge and supervised experience to effectively classify hyperspectral images (HSIs), thereby reducing the cost of large numbers of labeled samples. However, existing few-shot methods ignore the correlation between cross-domain feature channels, and the feature representation ability is insufficient. To address above issue, this paper proposes a novel Residual Channel Attention Based Sample Adaptation Few-Shot Learning for Hyperspectral Image Classification(RCASA-FSL) for hyperspectral image classification (HSIC), which can capture and enhance cross-domain dependencies through multi-layer residual connection and random-based feature recalibration. Specifically, a Deep Residual Feature Channel Attention Mechanism (DRFCAM) is designed to obtain cross-domain dependencies by residual concatenation, and further the residual structure is stacked for mining depth discrimination information. Furthermore, a new Random-based Feature Recalibration Module (RFRM) is proposed to reassign the feature weights via random matrix, which fully explore feature weight relationships to guide the sample adaptation process. Besides, we design a joint loss function with combining the FSL loss and domain adaptive loss for further optimization model. Experiments conducted on several standard hyperspectral datasets demonstrate that the proposed RCASA-FSL is superior to other FSL techniques in both quantitative and qualitative aspects.

MGFNet: Cross-scene Crowd Counting via Multistage Gated Fusion Network

Existing crowd counting methods are mainly trained and tested in similar scenarios. When the testing and training scenarios of the model are different, the counting accuracy of these methods will sharply decrease, which seriously limits their practical application. To address this problem, we propose a multistage gated fusion network (MGFNet) for cross-scene crowd counting. MGFNet is primarily composed of dynamic gated convolution units (DGCU) and multilevel scale attention blocks (MSAB) modules. Specifically, DGCU uses a dynamic gating path to supplement detailed information to reduce the loss of crowd information and overestimation of background in different scenarios. MSAB calibrates crowd information at different scales and perspectives in different scenes by generating attention maps with discriminative information. In addition, we used a new global local consistency loss to optimize the model to adapt to changes in crowd density and distribution. Extensive experiments on four different types of scene counting benchmarks show that the proposed MGFNet achieves superior cross-scene counting performance.

UMS[formula omitted]-ODNet: Unified-scale domain adaptation mechanism driven object detection network with multi-scale attention

Unsupervised domain adaptation techniques improve the generalization capability and performance of detectors, especially when the source and target domains have different distributions. Compared with two-stage detectors, one-stage detectors (especially YOLO series) provide better real-time capabilities and become primary choices in industrial fields. In this paper, to improve cross-domain object detection performance, we propose a Unified-Scale Domain Adaptation Mechanism Driven Object Detection Network with Multi-Scale Attention (UMS2-ODNet). UMS2-ODNet chooses YOLOv6 as the basic framework in terms of its balance between efficiency and accuracy. UMS2-ODNet considers the adaptation consistency across different scale feature maps, which tends to be ignored by existing methods. A unified-scale domain adaptation mechanism is designed to fully utilize and unify the discriminative information from different scales. A multi-scale attention module is constructed to further improve the multi-scale representation ability of features. A novel loss function is created to maintain the consistency of multi-scale information by considering the homology of the descriptions from the same latent feature. Multiply experiments are conducted on four widely used datasets. Our proposed method outperforms other state-of-the-art techniques, illustrating the feasibility and effectiveness of the proposed UMS2-ODNet.

H3T: Hierarchical Transferable Transformer with TokenMix for Unsupervised Domain Adaptation

Recent research has been focused on exploring the capabilities of Vision Transformers (ViTs) in Unsupervised Domain Adaptation (UDA). This approach typically involves providing more significant attention to fine-grained common information through patch-level transferable discrimination. However, prematurely assigning narrow-range transferability information at the encoding stage can sparse image information, thereby increasing the difficulty of downstream tasks. Therefore, we propose a Hierarchical Transferable Transformer with TokenMix (H3T), which maintains the allocation of fine-grained transferability at the encoding stage while enhancing the learning strength of image information through feature mixup. To address the challenge of missing sample labels in the target domain within the domain adaptation task, we have specifically designed the TokenMix Module (TMM) for ViTs. This module learns the style information from both domains while alleviating the impact of image sparsity on downstream tasks. Furthermore, to enhance the semantic connections among narrow-range image transfer messages, we propose the Hierarchical Discriminative Module (HDM), which also serves a critical role in encoding discriminative information. Our approach underwent comprehensive experimentation across five datasets of varying sizes, demonstrating its effectiveness. Our code is available at https://github.com/reyihua/H3T.

FFS-Net: Fourier-based segmentation of colon cancer glands using frequency and spatial edge interaction

The morphological features of glands provide a reliable basis for pathologists to diagnose colon cancer correctly. Currently, most methods are limited in their ability to address blurred edges, adhesions, and morphological differences in glands when working in the spatial domain. In this study, a new perspective is proposed to segment glands by utilizing the interaction between Fourier and spatial domain features. Specifically, a Fourier transform is used to analyze the frequency characteristics of pathological images in which the phase component is related to image structural information and the amplitude component contains image intensity information. A Fourier-based colon cancer gland segmentation network (FFS-Net) is proposed, which gradually constructs feature representations in both the spatial and frequency domains through spatial frequency edge interactions. This network comprises spatial subnetworks, frequency subnetworks, and an edge phase fusion module (EPFM). To alleviate the interference of complex background information in feature discrimination, the spatial subnetwork collects high-frequency contour information regarding the glands. Then, the EPFM completes the spatial-frequency feature interaction to add edge-related constraints to the extracted features, enhance edge representation, and obtain global features from the frequency domain, thereby, addressing significant differences in gland morphology. Finally, learnable Gabor filters are introduced to enhance edge texture detail information in the frequency subnetwork, which further improves the segmentation of glands exhibiting blurred edges. The experimental results demonstrate that FFS-Net’s performance on two representative datasets was competitive and highly interpretable.

Point-MPP: Point Cloud Self-Supervised Learning From Masked Position Prediction.

Masked autoencoding has gained momentum for improving fine-tuning performance in many downstream tasks. However, it tends to focus on low-level reconstruction details, lacking high-level semantics and resulting in weak transfer capability. This article presents a novel jigsaw puzzle solver inspired by the idea that predicting the positions of disordered point cloud patches provides more semantic information, similar to how children learn by solving jigsaw puzzles. Our method adopts the mask-then-predict paradigm, erasing the positions of selected point patches rather than their contents. We first partition input point clouds into irregular patches and randomly erase the positions of some patches. Then, a Transformer-based model is used to learn high-level semantic features and regress the positions of the masked patches. This approach forces the model to focus on learning transfer-robust semantics while paying less attention to low-level details. To tie the predictions within the encoding space, we further introduce a consistency constraint on their latent representations to encourage the encoded features to contain more semantic cues. We demonstrate that a standard Transformer backbone with our pretraining scheme can capture discriminative point cloud semantic information. Furthermore, extensive experiments indicate that our method outperforms the previous best competitor across six popular downstream vision tasks, achieving new state-of-the-art performance. Codes will be available at https://git.openi.org.cn/OpenPointCloud/Point-MPP.

Seismic Discrimination Using Regional Distance Coda-Wave Ratios

Abstract This study utilizes regional seismic coda-wave amplitude ratios to distinguish earthquakes and declared nuclear explosions in the Democratic People’s Republic of Korea (DPRK). We compute the coda spectral ratios at common stations to isolate source information from propagation and site effects. The resulting frequency-dependent amplitude ratios help identify differences between source types at DPRK. We use parametric models to validate observations, linking them to seismic source parameters. Our method effectively distinguishes the spectral signatures of explosions, nearby earthquakes, and a post-explosion collapse event. The measurements show clear differences between source-type combinations and agree with estimated seismic source models. Notably, the coda spectral ratios between explosions and earthquakes indicate that explosive sources exhibit relatively lower high-frequency S-wave energy and corner frequency compared to P waves. We leverage these spectral differences to develop a Bayesian inference framework for seismic source discrimination at the DPRK test site. This study demonstrates that spectral differences between source types can be exploited in coda spectral ratios to provide critical information for seismic discrimination.

Reconstruction error based implicit regularization method and its engineering application to lung cancer diagnosis

The automatic diagnosis of lung cancer via artificial intelligence faces two hotspot issues: (1) insufficient data and (2) excessive redundant information, which make it difficult for convolutional neural networks (CNNs) to learn discriminative information of lung cancer. In this paper, we present the reconstruction error based implicit regularization method (REbIRM) that regularizes CNNs at the loss layer. During each training iteration, the reconstruction errors introduced by the two-stage discriminative auto-encoder are used to sharpen the generalization ability of deep CNNs by improving the decision boundary. In the application process, the trained deep CNN is used for completing computed tomography (CT) diagnostics. The main clinical benefit of our approach is that it is domain independent, requiring no specialized knowledge, and can therefore be applied to different types of datasets. To the best of our knowledge, this is the first attempt to implicitly regularize CNNs based on the reconstruction errors. Finally, experimental results on three CT image classification datasets show that REbIRM can achieve impressive results and that, in conjunction with Dropout, it obtains the state-of-the-art performance. REbIRM is also robust to the selection of hyper-parameters and only has the sublinear influence on the convergence of deep CNNs. Besides, empirical and theoretical evidence are provided to indicate that REbIRM prefers to converges in a constrained parameter space with flatter minima, which explains why it can generalize to new data. Finally, the nature of REbIRM is further explored through visualization techniques to analyze how it works in training deep CNNs.

Is metadata of articles about COVID-19 enough for multilabel topic classification task?

The ever-increasing volume of COVID-19-related articles presents a significant challenge for the manual curation and multilabel topic classification of LitCovid. For this purpose, a novel multilabel topic classification framework is developed in this study, which considers both the correlation and imbalance of topic labels, while empowering the pretrained model. With the help of this framework, this study devotes to answering the following question: Do full texts, MeSH (Medical Subject Heading), and biological entities of articles about COVID-19 encode more discriminative information than metadata (title, abstract, keyword, and journal name)? From extensive experiments on our enriched version of the BC7-LitCovid corpus and Hallmarks of Cancer corpus, the following conclusions can be drawn. Our framework demonstrates superior performance and robustness. The metadata of scientific publications about COVID-19 carries valuable information for multilabel topic classification. Compared to biological entities, full texts and MeSH can further enhance the performance of our framework for multilabel topic classification, but the improved performance is very limited. Database URL: https://github.com/pzczxs/Enriched-BC7-LitCovid.

Batch channel normalized-CWGAN with Swin Transformer for imbalanced data fault diagnosis of rotating machinery

Abstract In real scenarios, rotating machinery is mainly operated in optimal condition, leading to fault data scarce and difficult to collect. This issue results in imbalanced data, significantly limiting the effectiveness of intelligent fault diagnosis methods. To address this issue, a novel fault diagnosis method for rotating machinery is proposed in this paper, which combines the batch channel normalized conditional wasserstein generative adversarial network (BCN-CWGAN) with Swin Transformer. Firstly, the one-dimensional vibration signal is preprocessed into two-dimensional feature images using a symmetrized dot pattern (SDP). Subsequently, self-attention mechanism and deep feature learning module constructed by DenseNet are integrated into the generator of GAN to acquire more discriminative feature information. Meanwhile, the discriminator of GAN is combined with batch channel normalization strategy, which further enhances the generalization ability. Besides, a two time-scale update rule strategy enhances training stability and convergence speed by updating model parameters at different time scales. Then, the data augmentation capability of BCN-CWGAN is used to generate high-quality fault samples to augment the imbalanced dataset. Finally, Swin Transformer is combined to achieve accurate fault diagnosis. The performance enhancement of the proposed method is verified through comparison and diagnosis results of two engineering experiments, demonstrating its substantial value for research in engineering practice. With the proposed data augmentation method, the average accuracy of A1, B1, C1, and D1 datasets in experiment 1 reached 99.24%, 98.85%, 96.78%, and 96.04%, respectively. Meanwhile, the proposed method achieved the best accuracy in experiment 2.

Global and local semantic enhancement of samples for cross-modal hashing

Hashing becomes popular in cross-modal retrieval due to its exceptional performance in both search and storage. However, existing cross-modal hashing (CMH) methods may (a) neglect to learn sufficient modal-specific information, and (b) fail to fully exploit sample semantics. To address these issues, we propose a method called Semantic Enhancement of Sample Hashing (SESH). First, SESH employs a global modal-specific learning strategy to draw overall shared information and global modal-specific information by factoring the mapping matrix. Second, SESH introduces manifold learning and a local modal-specific learning strategy to extract additional local modal-specific and modal-shared data under label guidance. Meanwhile, local modal-specific information is integrated with global modal-specific details to add rich modal-specific information. Third, SESH uses discrete maximum similarity and orthogonal constraint transformation to enhance both global and local semantic information, embedding more discriminative information into the Hamming space. Finally, an efficient discrete optimization algorithm is proposed to generate the hash codes directly. Experiments on three datasets demonstrate the superior performance of SESH. The source code will be available at https://github.com/kokorording/SESH.

Radar gait recognition using Dual-branch Swin Transformer with Asymmetric Attention Fusion

Video-based gait recognition suffers from potential privacy issues and performance degradation due to dim environments, partial occlusions, or camera view changes. Radar has recently become increasingly popular and overcome various challenges presented by vision sensors. To capture tiny differences in radar gait signatures of different people, a dual-branch Swin Transformer is proposed, where one branch captures the time variations of the radar micro-Doppler signature and the other captures the repetitive frequency patterns in the spectrogram. Unlike natural images where objects can be translated, rotated, or scaled, the spatial coordinates of spectrograms and CVDs have unique physical meanings, and there is no affine transformation for radar targets in these synthetic images. The patch splitting mechanism in Vision Transformer makes it ideal to extract discriminant information from patches, and learn the attentive information across patches, as each patch carries some unique physical properties of radar targets. Swin Transformer consists of a set of cascaded Swin blocks to extract semantic features from shallow to deep representations, further improving the classification performance. Lastly, to highlight the branch with larger discriminant power, an Asymmetric Attention Fusion is proposed to optimally fuse the discriminant features from the two branches. To enrich the research on radar gait recognition, a large-scale NTU-RGR dataset is constructed, containing 45,768 radar frames of 98 subjects. The proposed method is evaluated on the NTU-RGR dataset and the MMRGait-1.0 database. It consistently and significantly outperforms all the compared methods on both datasets. The codes are available at:https://github.com/wentaoheunnc/NTU-RGR.

Resampling approach for imbalanced data classification based on class instance density per feature value intervals

In practical applications, imbalanced datasets significantly degrade the classification performance of machine learning models. However, most conventional resampling approaches fall short in adequately addressing the varying contributions of individual features to the classification model. In response to this defect, this study introduces three novel resampling approaches. The first approach, Oversampling based on class instance density per feature value intervals (OCF), focuses on augmenting the dataset. The second approach, Undersampling based on class instance density per feature value intervals (UCF), seeks to reduce dataset size. The third approach, Hybrid sampling based on class instance density per feature value intervals (HSCF), which can perform oversampling and undersampling simultaneously. These approaches categorize feature value into different intervals based on their varying information content, calculate class instance densities within these intervals, and generate feature values in intervals with high discriminative information. Subsequently, these generated features are combined to synthesize minority class data, effectively achieving oversampling. Additionally, the study combines class instance density and feature importance to identify majority class data at the classification boundary with minimal contribution and subsequently executes undersampling. The flexibility to adjust sampling ratios and the integration of OCF and UCF enable the implementation of hybrid sampling. Finally, experiments on the benchmark dataset demonstrate the superiority and effectiveness of the proposed method. Furthermore, it is observed that the method proposed in this study enhances the feature dividing capability of decision tree classifiers. Hence, the best results are achieved when working in synergy with decision tree classifiers, leading to the most significant improvements in classification performance. All codes have been published at https://github.com/Wangfeiopen/HSCF.

Semisupervised Progressive Representation Learning for Deep Multiview Clustering.

Multiview clustering has become a research hotspot in recent years due to its excellent capability of heterogeneous data fusion. Although a great deal of related works has appeared one after another, most of them generally overlook the potentials of prior knowledge utilization and progressive sample learning, resulting in unsatisfactory clustering performance in real-world applications. To deal with the aforementioned drawbacks, in this article, we propose a semisupervised progressive representation learning approach for deep multiview clustering (namely, SPDMC). Specifically, to make full use of the discriminative information contained in prior knowledge, we design a flexible and unified regularization, which models the sample pairwise relationship by enforcing the learned view-specific representation of must-link (ML) samples (cannot-link (CL) samples) to be similar (dissimilar) with cosine similarity. Moreover, we introduce the self-paced learning (SPL) paradigm and take good care of two characteristics in terms of both complexity and diversity when progressively learning multiview representations, such that the complementarity across multiple views can be squeezed thoroughly. Through comprehensive experiments on eight widely used image datasets, we prove that the proposed approach can perform better than the state-of-the-art opponents.

Information enhanced slow feature analysis integrated with prior fault data for sensitive monitoring of chemical processes

As an effective feature extraction method, slow feature analysis (SFA) has been successfully applied in the domain of chemical process monitoring. However, as an unsupervised method, the basic SFA ignores the key information carried by prior fault data so that many complicated faults cannot be sensitively detected. To address this issue, an enhanced SFA method, termed Information Enhanced SFA (IE-SFA), is proposed by integrating available fault discriminant information to achieve more sensitive detection of complicated chemical process faults. The proposed method builds a primary-auxiliary SFA modeling framework. On the one hand, the normal training data are analyzed to establish the primary SFA model. On the other, prior fault data are integrated to develop an auxiliary monitoring model for providing extra fault-sensitive information. In the auxiliary model, a sample-variable weighted Fisher discriminant analysis method is performed on normal data, fault data, and their respective slow feature components, thereby extracting fault-sensitive features for enhancing the fault detection capability. For full-view fault monitoring, the Bayesian fusion strategy is used to fuse the outcomes from both primary and auxiliary models. The applications on a benchmark Tennessee Eastman chemical process and a three-phase flow process demonstrate that the proposed IE-SFA method provides superior fault detection performance compared to the basic SFA method.

Enhancing Sex Estimation Accuracy with Cranial Angle Measurements and Machine Learning.

The development of current sexing methods largely depends on the use of adequate sources of data and adjustable classification techniques. Most sex estimation methods have been based on linear measurements, while the angles have been largely ignored, potentially leading to the loss of valuable information for sex discrimination. This study aims to evaluate the usefulness of cranial angles for sex estimation and to differentiate the most dimorphic ones by training machine learning algorithms. Computed tomography images of 154 males and 180 females were used to derive data of 36 cranial angles. The classification models were created by support vector machines, naïve Bayes, logistic regression, and the rule-induction algorithm CN2. A series of cranial angle subsets was arranged by an attribute selection scheme. The algorithms achieved the highest accuracy on subsets of cranial angles, most of which correspond to well-known features for sex discrimination. Angles characterizing the lower forehead and upper midface were included in the best-performing models of all algorithms. The accuracy results showed the considerable classification potential of the cranial angles. The study demonstrates the value of the cranial angles as sex indicators and the possibility to enhance the sex estimation accuracy by using them.

Semantic Guided prototype learning for Cross-Domain Few-Shot hyperspectral image classification

Hyperspectral image(HSI) classification has found extensive application in the field of remote sensing. However, traditional HSI classification methods typically rely on a large number of labeled samples, incurring significant costs and requiring considerable time and effort for acquisition. To address this issue, this paper proposes a novel cross-domain few-shot learning method termed Semantic Guided Prototype Learning (SGPL), which aims to optimize prototype learning through the incorporation semantic information, thereby enhancing the classification accuracy of hyperspectral images in few-shot scenarios. The core idea of the SGPL method is to integrate semantic information into the prototype learning framework, thereby enhancing the expressive capacity of each category prototype. Specifically, HSI category labels are encoded via one-hot encoding, and the corresponding HSI features are concatenated with these encoded vectors to construct an augmented feature representation. These augmented feature vectors are subsequently fed into a prototype generator, which is designed to learn the generation of category prototypes from the concatenated features. Through the application of one-hot encoding and feature concatenation, SGPL effectively captures and leverages discriminative information between classes. Moreover, the proposed model exhibits superior performance on three different HSI datasets: Salinas, the University of Pavia(UP), and Indian Pines(IP) with overall classification accuracies of 91.27%, 85.84%, and 69.33%, respectively, outperforming other methods. The source code is available at https://github.com/AIYAU/SGPL.