Image Classification Problems Research Articles

Learning Location From Shared Elevation Profiles in Fitness Apps: A Privacy Perspective

The extensive use of smartphones and wearable devices has facilitated many useful applications. For example, with Global Positioning System (GPS)-equipped smart and wearable devices, many applications can gather, process, and share rich metadata, such as geolocation, trajectories, elevation, and time. For example, fitness applications, such as Runkeeper and Strava, utilize the information for activity tracking and have recently witnessed a boom in popularity. Those fitness tracker applications have their own web platforms and allow users to share activities on such platforms or even with other social network platforms. To preserve the privacy of users while allowing sharing, several of those platforms may allow users to disclose partial information, such as the elevation profile for an activity, which supposedly would not leak the location of the users. In this work, and as a cautionary tale, we create a proof of concept where we examine the extent to which elevation profiles can be used to predict the location of users. To tackle this problem, we devise three plausible threat settings under which the city or borough of the targets can be predicted. Those threat settings define the amount of information available to the adversary to launch the prediction attacks. Establishing that simple features of elevation profiles, e.g., spectral features, are insufficient, we devise both natural language processing (NLP)-inspired text-like representation and computer vision-inspired image-like representation of elevation profiles, and we convert the problem at hand into text and image classification problem. We use both traditional machine learning- and deep learning-based techniques and achieve a prediction success rate ranging from 59.59% to 99.80%. The findings are alarming, highlighting that sharing elevation information may have significant location privacy risks.

Detection and Classification of MRI Images using Multistage Classifier for Early Prediction of Alzheimer’s Disease

The term “neuro imaging” describes a set of procedures used for studying, diagnosing, and treating neurological disorders as well as for basic scientific research into the human brain. In order for medical professionals to make early diagnoses for their patients, they must investigate the signs of various neuro imaging kinds; among the most important elements in this process is the problem of neuro image classification. No effective treatment has been found for Alzheimer's disease (AD), a degenerative brain ailment that has no known cure. It is not possible to halt the progression of the disease once it has begun. But there are medications that can slow it down. Nevertheless, with the use of sophisticated prediction, the disease-influencing protein functions can be reduced. Because of shared neural networks and pixel strength, Alzheimer's disease diagnosis in the elderly is discretely challenging and calls for the portrayal of a discriminating element separately. Several writers attempted to apply machine learning to make early disease diagnoses, but they were unable to get reliable classification accuracy. To that end, we set out to develop a model that would combine deep neural networks with multistage classifiers in the hopes that it would be able to efficiently and effectively extract characteristics from input data. Better and more effective detection and classification of Alzheimer's disease was achieved in this study by employing a multistage classifier that made use of deep learning techniques. Using a benchmark database supplied by the Alzheimer's Neuro Imaging Institute, the suggested strategy achieves better outcomes than individual techniques.

Fine-grained Recognition with Learnable Semantic Data Augmentation.

Fine-grained image recognition is a longstanding computer vision challenge that focuses on differentiating objects belonging to multiple subordinate categories within the same meta-category. Since images belonging to the same meta-category usually share similar visual appearances, mining discriminative visual cues is the key to distinguishing fine-grained categories. Although commonly used image-level data augmentation techniques have achieved great success in generic image classification problems, they are rarely applied in fine-grained scenarios, because their random editing-region behavior is prone to destroy the discriminative visual cues residing in the subtle regions. In this paper, we propose diversifying the training data at the feature-level to alleviate the discriminative region loss problem. Specifically, we produce diversified augmented samples by translating image features along semantically meaningful directions. The semantic directions are estimated with a covariance prediction network, which predicts a sample-wise covariance matrix to adapt to the large intra-class variation inherent in fine-grained images. Furthermore, the covariance prediction network is jointly optimized with the classification network in a meta-learning manner to alleviate the degenerate solution problem. Experiments on four competitive fine-grained recognition benchmarks (CUB-200-2011, Stanford Cars, FGVC Aircrafts, NABirds) demonstrate that our method significantly improves the generalization performance on several popular classification networks (e.g., ResNets, DenseNets, EfficientNets, RegNets and ViT). Combined with a recently proposed method, our semantic data augmentation approach achieves state-of-the-art performance on the CUB-200-2011 dataset. The source code will be released.

Quantum Convolutional Neural Network for Image Classification

In the field of image processing, a well-known model is the Convolutional Neural Network, or CNN. The unique benefit that sets this model apart is its exceptional ability to use the correlation information included in the data. Even with their amazing accomplishment, conventional CNNs could have trouble improving further in terms of generalization, accuracy, and computing economy. However, it could be challenging to train CNN correctly and process information quickly if the model or data dimensions are too large. This is since it will cause the data processing to lag. The Quantum Convolutional Neural Network, or QCNN for short, is a novel proposed quantum solution that might either enhance the functionality of an existing learning model or solve a problem requiring the combination of quantum computing with CNN. To highlight the flexibility and versatility of quantum circuits in improving feature extraction capabilities, this paper compares deep quantum circuit architecture designed for image-based tasks with classical Convolutional Neural Networks (CNNs) and a novel quantum circuit architecture. The covidx-cxr4 dataset was used to train quantum-CNN models, and their results were compared against those of other models. The results show that when paired with innovative feature extraction methods, the suggested deep Quantum Convolutional Neural Network (QCNN) outperformed the conventional CNN in terms of processing speed and recognition accuracy. Even though it required more processing time, QCNN outperformed CNN in terms of recognition accuracy. When training on the covidx-cxr4 dataset, this dominance becomes much more apparent, demonstrating how deeper quantum computing has the potential to completely transform image classification problems.

Designing Universally-Approximating Deep Neural Networks: A First-Order Optimization Approach.

Universal approximation capability, also referred to as universality, is an important property of deep neural networks, endowing them with the potency to accurately represent the underlying target function in learning tasks. In practice, the architecture of deep neural networks largely influences the performance of the models. However, most existing methodologies for designing neural architectures, such as the heuristic manual design or neural architecture search, ignore the universal approximation property, thus losing a potential safeguard about the performance. In this paper, we propose a unified framework to design the architectures of deep neural networks with a universality guarantee based on first-order optimization algorithms, where the forward pass is interpreted as the updates of an optimization algorithm. The (explicit or implicit) network is designed by replacing each gradient term in the algorithm with a learnable module similar to a two-layer network or its derivatives Specifically, we explore the realm of width-bounded neural networks, a common practical scenario, showcasing their universality. Moreover, adding operations of normalization, downsampling, and upsampling does not hurt the universality. To the best of our knowledge, this is the first work that width-bounded networks with universal approximation guarantee can be designed in a principled way. Our framework can inspire a variety of neural architectures including some renowned structures such as ResNet and DenseNet, as well as novel innovations. The experimental results on image classification problems demonstrate that the newly inspired networks are competitive and surpass the baselines of ResNet, DenseNet, as well as the advanced ConvNeXt and ViT, testifying to the effectiveness of our framework.

Cross-Scale Fusion Transformer for Histopathological Image Classification.

Histopathological images provide the medical evidences to help the disease diagnosis. However, pathologists are not always available or are overloaded by work. Moreover, the variations of pathological images with respect to different organs, cell sizes and magnification factors lead to the difficulty of developing a general method to solve the histopathological image classification problems. To address these issues, we propose a novel cross-scale fusion (CSF) transformer which consists of the multiple field-of-view patch embedding module, the transformer encoders and the cross-fusion modules. Based on the proposed modules, the CSF transformer can effectively integrate patch embeddings of different field-of-views to learn cross-scale contextual correlations, which represent tissues and cells of different sizes and magnification factors, with less memory usage and computation compared with the state-of-the-art transformers. To verify the generalization ability of the CSF transformer, experiments are performed on four public datasets of different organs and magnification factors. The CSF transformer outperforms the state-of-the-art task specific methods, convolutional neural network-based methods and transformer-based methods. The source code will be available in our GitHub https://github.com/nchucvml/CSFT.

Experimental Dataset of Tunable Mode Converter Based on Long-Period Fiber Gratings Written in Few-Mode Fiber: Impacts of Thermal, Wavelength, and Polarization Variations

Mode division multiplexing (MDM) is currently one of the most attractive multiplexing techniques in optical communications, as it allows for an increase in the number of channels available for data transmission. Optical modal converters are one of the main devices used in this technique. Therefore, the characterization and improvement of these devices are of great current interest. In this work, we present a dataset of 49,736 near-field intensity images of a modal converter based on a long-period fiber grating (LPFG) written on a few-mode fiber (FMF). This characterization was performed experimentally at various wavelengths, polarizations, and temperature conditions when the device converted from LP01 mode to LP11 mode. The results show that the modal converter can be tuned by adjusting these parameters, and that its operation is optimal under specific circumstances which have a great impact on its performance. Additionally, the potential application of the database is validated in this work. A modal decomposition technique based on the particle swarm algorithm (PSO) was employed as a tool for determining the most effective combinations of modal weights and relative phases from the spatial distributions collected in the dataset. The proposed dataset can open up new opportunities for researchers working on image segmentation, detection, and classification problems related to MDM technology. In addition, we implement novel artificial intelligence techniques that can help in finding the optimal operating conditions for this type of device.

Self-adaptive subspace representation from a geometric intuition

In this paper, we address the unsupervised subspace learning task from a geometric intuition, which offers a direct understanding of the data representation. First, we consider all subspaces of the Euclidean space as a series of graded Grassmannian manifolds, and represent them by orbits of rotation group action. Then, we reformulate the unsupervised subspace learning by a least square problem with respect to rotation and projection operator. Second, we introduce a low-rank regularization to obtain a low-dimensional and robust subspace representation. Then, the model is translated into a minimization problem on the Grassmannian manifold. By dividing the model into two subproblems of solving the optimal rotation and subspace dimension, we design an alternating iteration strategy, where the locally geodesic structure of the rotation group and the unconstrained quadratic 0-1 programming are utilized. Finally, we apply the proposed method to the image classification problem on five benchmark datasets and compare it with nine state-of-the-art methods. Numerical results show that our proposed method has better feature representation ability and almost achieves the best performance in terms of classification accuracy and robustness.

Simulating an Integrated Photonic Image Classifier for Diffractive Neural Networks.

The slowdown of Moore's law and the existence of the "von Neumann bottleneck" has led to electronic-based computing systems under von Neumann's architecture being unable to meet the fast-growing demand for artificial intelligence computing. However, all-optical diffractive neural networks provide a possible solution to this challenge. They can outperform conventional silicon-based electronic neural networks due to the significantly higher speed of the propagation of optical signals (≈108 m.s-1) compared to electrical signals (≈105 m.s-1), their parallelism in nature, and their low power consumption. The integrated diffractive deep neural network (ID2NN) uses an on-chip fully passive photonic approach to achieve the functionality of neural networks (matrix-vector operations) and can be fabricated via the CMOS process, which is technologically more amenable to implementing an artificial intelligence processor. In this paper, we present a detailed design framework for the integrated diffractive deep neural network and corresponding silicon-on-insulator integration implementation through Python-based simulations. The performance of our proposed ID2NN was evaluated by solving image classification problems using the MNIST dataset.

Precision agriculture based on convolutional neural network in rice production nutrient management using machine learning genetic algorithm

Precision Agriculture technology can use high-tech sensors and analytical tools to help farmers better manage their crop yields, allowing them to demonstrate their techniques on the field. The greater use of remote sensing technologies in farm productivity and nitrogen management tools to increase yields and reduce operational costs contributes to reduced energy consumption. This concept describes the need to fulfill nutrient and crop production objectives for the near and long term. Based on our proposed approach, we concluded that the predictors of individual flow occurrences are variables linked to surface temperatures, moisture, and nitrogen availability. We have developed an automated Convolutional Neural Network creation approach genetic algorithms approach to handling image classification problems more efficiently. The best feature of the method is that it is “automated” and doesn't need domain knowledge about CNNs. When compared to conventional encoding, we compare the performance and adaptability of our processing. Moreover, the comparison of individual Genetic algorithms with CNN schemes employed for training the systems was compared to that of other GENCNN schemes ResNet-34, ResNet-50, AlexNet, DenseNet, VGGNet in terms of convergence speed and overall accuracy, among other things. Our studies data indicate that our Soil Nitrogen Management significantly reduced commercial system costs while providing notable outcomes. The experimental results of soil nutrient deficiency 99%and 97% show that the suggested method may be used to automatically identify the most efficient GENCNN model compared to existing models in a wide range of domains.

Automated CNN optimization using multi-objective grammatical evolution

Selecting and optimizing Convolutional Neural Networks (CNNs) has become a very complex task given the number of associated optimizable parameters, as well as the fact that the arrangement of the layers present in a CNN directly influences its performance. Several research areas used automation techniques to construct and optimize these architectures, with Grammatical Evolution (GE) being one of the most promising techniques. Although several works proposed solutions to the problem in question, each adopts its own evaluation strategy (e.g., different datasets, evaluation metrics, hardware infrastructure). This divergence makes it difficult to compare the proposed approaches, and consequently, it is not possible to reach safe conclusions about the performance of the solutions. This work proposes an experimental evaluation of several context-free grammars listed in the literature for constructing and optimizing CNNs architectures. In addition, we included four well-known CNNs as baselines: DenseNet169, EfficientNetB1, InceptionV3 and ResNet50V2. We aim to identify the best practices for elaborating grammars and compare their results with consolidated CNNs for image classification problems in the literature. Besides, we assessed all approaches on the same controlled environment (e.g., datasets, evaluation metrics, software and hardware setup) to guarantee fairness in the evaluation process. The experiments were carried out by investigating the performance of the models generated by different grammars in solving image classification problems in three datasets of variable dimensions: CIFAR-10, EuroSAT, and MNIST.The experiments have validated several key findings: (i) the significance of optimizing Convolutional Neural Networks (CNNs); (ii) the potential of grammar-based methods as a promising alternative for this task, yielding CNN models that outperform state-of-the-art CNN architectures while possessing fewer trainable parameters, resulting in reduced computational complexity; (iii) grammars incorporating regularization layers (such as dropout and batch normalization) and those that confine the search space (via parameter constraints on CNNs) consistently produce high-performing models with lower complexity, even after a few generations of the evolutionary process; and (iv) the selection of the grammar for optimization can positively or negatively impact the model generation, depending on the specific task requirements.

Research on mass image classification based on CNN deep learning

Image classification is one of the most important fundamental problems in computer vision research. It involves many basic problems in the field of computer vision, such as feature extraction, object detection and so on. Therefore, the study of image classification is helpful to understand the basic knowledge in the field of computer vision. Since CNN model made a breakthrough, deep learning has been widely used in image classification field. This paper aims to apply the deep learning method to solve the problem of image classification, study and understand image classification problems to help further study and study in the future. By learning 50000 well classified images, this research tries to find the most suitable algorithm and model to achieve the optimal classification, realizing 20 classification and 100 classifications for 10000 unclassified images with the highest accuracy possible.

Fine-Grained Intoxicated Gait Classification Using a Bilinear CNN.

Consuming excessive amounts of alcohol causes impaired mobility and judgment and driving accidents, resulting in more than 800 injuries and fatalities each day. Passive methods to detect intoxicated drivers beyond the safe driving limit can facilitate Just-In-Time alerts and reduce Driving Under the Influence (DUI) incidents. Popularly-owned smartphones are not only equipped with motion sensors (accelerometer and gyroscope) that can be employed for passively collecting gait (walk) data but also have the processing power to run computationally expensive machine learning models. In this paper, we advance the state-of-the-art by proposing a novel method that utilizes a Bi-linear Convolution Neural Network (BiCNN) for analyzing smartphone accelerometer and gyroscope data to determine whether a smartphone user is over the legal driving limit (0.08) from their gait. After segmenting the gait data into steps, we converted the smartphone motion sensor data to a Gramian Angular Field (GAF) image and then leveraged the BiCNN architecture for intoxication classification. Distinguishing GAF-encoded images of the gait of intoxicated vs. sober users is challenging as the differences between the classes (intoxicated vs. sober) are subtle, also known as a fine-grained image classification problem. The BiCNN neural network has previously produced state-of-the-art results on fine-grained image classification of natural images. To the best of our knowledge, our work is the first to innovatively utilize the BiCNN to classify GAF encoded images of smartphone gait data in order to detect intoxication. Prior work had explored using the BiCNN to classify natural images or explored other gait-related tasks but not intoxication Our complete intoxication classification pipeline consists of several important pre-processing steps carefully adapted to the BAC classification task, including step detection and segmentation, data normalization to account for inter-subject variability, data fusion, GAF image generation from time-series data, and a BiCNN classification model. In rigorous evaluation, our BiCNN model achieves an accuracy of 83.5%, outperforming the previous state-of-the-art and demonstrating the feasibility of our approach.

Visual analytics for process monitoring: Leveraging time-series imaging for enhanced interpretability

In the era of big data driven by the advent of the Internet of Things (IoT), process industries face the challenge of analyzing massive and complex data to extract relevant information for effective process monitoring. Despite exploring various approaches, scalability and interpretability continue to present practical limitations. To address these limitations, we propose a new framework called visual analytics. Visual analytics offers a new perspective on solving process monitoring problems. It involves transforming historical process data into visual clues, thereby converting traditional fault detection problems into image classification problems. This approach allows process experts to visually analyze patterns and textures within the data, making interpretation much easier compared to traditional time domain analysis. Moreover, by treating process data as images, visual analytics can leverage a wide range of computer vision techniques, including convolutional neural networks (CNNs), to accurately classify and detect faults. By integrating human visual perception with advanced computer vision techniques, visual analytics enables the effective analysis of massive and complex process data. To empirically validate the proposed visual analytics approach, we conduct experiments on both the simulated continuous stirred tank heater (CSTH) benchmark and the industrial arc loss benchmark. The experimental results from both benchmarks demonstrate that the proposed visual analytics approach yields competitive performance in predicting process faults while enhancing interpretability by providing meaningful and informative visual representations.

Universal Representations: A Unified Look at Multiple Task and Domain Learning

AbstractWe propose a unified look at jointly learning multiple vision tasks and visual domains through universal representations, a single deep neural network. Learning multiple problems simultaneously involves minimizing a weighted sum of multiple loss functions with different magnitudes and characteristics and thus results in unbalanced state of one loss dominating the optimization and poor results compared to learning a separate model for each problem. To this end, we propose distilling knowledge of multiple task/domain-specific networks into a single deep neural network after aligning its representations with the task/domain-specific ones through small capacity adapters. We rigorously show that universal representations achieve state-of-the-art performances in learning of multiple dense prediction problems in NYU-v2 and Cityscapes, multiple image classification problems from diverse domains in Visual Decathlon Dataset and cross-domain few-shot learning in MetaDataset. Finally we also conduct multiple analysis through ablation and qualitative studies.

Composing Diverse Ensembles of Convolutional Neural Networks by Penalization

Ensemble-based systems are well known to have the capacity to outperform individual approaches if the ensemble members are sufficiently accurate and diverse. This paper investigates how an efficient ensemble of deep convolutional neural networks (CNNs) can be created by forcing them to adjust their parameters during the training process to increase diversity in their decisions. As a new theoretical approach to reach this aim, we join the member neural architectures via a fully connected layer and insert a new correlation penalty term in the loss function to obstruct their similar operation. With this complementary term, we implement the standard guideline of ensemble creation to increase the members’ diversity for CNNs in a more detailed and flexible way than similar existing techniques. As for applicability, we show that our approach can be efficiently used in various classification tasks. More specifically, we demonstrate its performance in challenging medical image analysis and natural image classification problems. Besides the theoretical considerations and foundations, our experimental findings suggest that the proposed technique is competitive. Namely, on the one hand, the classification rate of the ensemble trained in this way outperformed all the individual accuracies of the state-of-the-art member CNNs according to the standard error functions of these application domains. On the other hand, it is also validated that the ensemble members get more diverse and their accuracies are raised by adding the penalization term. Moreover, we performed a full comparative analysis, including other state-of-the-art ensemble-based approaches recommended for the same classification tasks. This comparative study also confirmed the superiority of our method, as it overcame the current solutions.

Markov chain stochastic DCA and applications in deep learning with PDEs regularization

This paper addresses a large class of nonsmooth nonconvex stochastic DC (difference-of-convex functions) programs where endogenous uncertainty is involved and i.i.d. (independent and identically distributed) samples are not available. Instead, we assume that it is only possible to access Markov chains whose sequences of distributions converge to the target distributions. This setting is legitimate as Markovian noise arises in many contexts including Bayesian inference, reinforcement learning, and stochastic optimization in high-dimensional or combinatorial spaces. We then design a stochastic algorithm named Markov chain stochastic DCA (MCSDCA) based on DCA (DC algorithm) - a well-known method for nonconvex optimization. We establish the convergence analysis in both asymptotic and nonasymptotic senses. The MCSDCA is then applied to deep learning via PDEs (partial differential equations) regularization, where two realizations of MCSDCA are constructed, namely MCSDCA-odLD and MCSDCA-udLD, based on overdamped and underdamped Langevin dynamics, respectively. Numerical experiments on time series prediction and image classification problems with a variety of neural network topologies show the merits of the proposed methods.

A framework-based transformer and knowledge distillation for interior style classification

Interior style classification is an interesting problem which has potential applications both commercial and academic communities. This task aims to devise interior design styles automatically. Thus, interior designers will explore customers’ tastes and then precisely provide suggestions for decor inspiration based on their preferences. Recently, Convolutional Neural Networks (CNNs) have been considered the de-facto standard in computer vision tasks. Therefore, several current works have tended to address interior style classification using CNN-based architectures. Moreover, transformer-based architectures and attention-based encoder–decoder models have been proven successfully in computer vision and natural language processing tasks. Sequentially, more studies have been arguing the efficiency of combining CNN-based architectures and transformer-based architectures for normal image classification problems. In this project, we focus on finding an architecture network that is suitable for the interior style classification problem. We propose a robustness method to address interior style design classification, named ISC-DeIT. The proposed method is based on Data-efficient image transformer architectures and knowledge distillation, which can be trained on small datasets effectively. Especially, a proposed additional module is plugged to leverage learning feature representations for improving predictive accuracy. Experiments were carried out on a new curated dataset with five interior styles including Art-Decor, Hitech, Indochina, Industrial, and Scandinavian. Empirical results of ISC- DeiT indicated that the ability of prediction for interior style classification of the proposed method has been increased significantly, compared with other state-of-the-art methods.

Generating and detecting true ambiguity: a forgotten danger in DNN supervision testing

Deep Neural Networks (DNNs) are becoming a crucial component of modern software systems, but they are prone to fail under conditions that are different from the ones observed during training (out-of-distribution inputs) or on inputs that are truly ambiguous, i.e., inputs that admit multiple classes with nonzero probability in their labels. Recent work proposed DNN supervisors to detect high-uncertainty inputs before their possible misclassification leads to any harm. To test and compare the capabilities of DNN supervisors, researchers proposed test generation techniques, to focus the testing effort on high-uncertainty inputs that should be recognized as anomalous by supervisors. However, existing test generators aim to produce out-of-distribution inputs. No existing model- and supervisor independent technique targets the generation of truly ambiguous test inputs, i.e., inputs that admit multiple classes according to expert human judgment. In this paper, we propose a novel way to generate ambiguous inputs to test DNN supervisors and used it to empirically compare several existing supervisor techniques. In particular, we propose AmbiGuess to generate ambiguous samples for image classification problems. AmbiGuess is based on gradient-guided sampling in the latent space of a regularized adversarial autoencoder. Moreover, we conducted what is {-} to the best of our knowledge {-} the most extensive comparative study of DNN supervisors, considering their capabilities to detect 4 distinct types of high-uncertainty inputs, including truly ambiguous ones. We find that the tested supervisors’ capabilities are complementary: Those best suited to detect true ambiguity perform worse on invalid, out-of-distribution and adversarial inputs and vice-versa.

A Lightweight Multimodal Footprint Recognition Network Based on Progressive Multi-Granularity Feature Fusion

The main differences in images of footprints are the proportion of the parts of foot and the distribution of pressure, which can be considered as fine-grained image classification. Moreover, the deviation of human body weight and muscle strength increases the difficulty of identifying the left and right feet. While using a fine-grained image classification network to solve the footprint image classification problem is certainly a feasible approach, the number of parameters in a fine-grained image classification network is generally large, and therefore we would like to build a lightweight classification network that is suitable for several small footprint datasets. In this paper, a multimodal footprint recognition algorithm based on progressive multi-granularity feature fusion is proposed. First, the shallow dense connection network is used to extract features. The feature extraction ability of the model is improved with the help of channel splicing and feature multiplexing. Second, to learn footprint images of different granularities, the progressive training strategy and puzzle scrambler are applied to the model. Finally, factorized bilinear coding can aggregate local features to obtain more discriminative global representation features. Experiments show that our network achieves comparable classification accuracy to some fine-grained image classification models (PMG, MSEC) on the complete pressure footprint dataset, but the number of parameters in our network is greatly reduced. Meanwhile, our network also achieves good classification results on several other footprint datasets, which demonstrates the effectiveness of our network. At the same time, an ablation experiment was carried out to verify the effectiveness of the progressive strategy and the factorized bilinear coding.