Hierarchical Features Research Articles

Indoor Scene Classification through Dual-Stream Deep Learning: A Framework for Improved Scene Understanding in Robotics

Indoor scene classification plays a pivotal role in enabling social robots to seamlessly adapt to their environments, facilitating effective navigation and interaction within diverse indoor scenes. By accurately characterizing indoor scenes, robots can autonomously tailor their behaviors, making informed decisions to accomplish specific tasks. Traditional methods relying on manually crafted features encounter difficulties when characterizing complex indoor scenes. On the other hand, deep learning models address the shortcomings of traditional methods by autonomously learning hierarchical features from raw images. Despite the success of deep learning models, existing models still struggle to effectively characterize complex indoor scenes. This is because there is high degree of intra-class variability and inter-class similarity within indoor environments. To address this problem, we propose a dual-stream framework that harnesses both global contextual information and local features for enhanced recognition. The global stream captures high-level features and relationships across the scene. The local stream employs a fully convolutional network to extract fine-grained local information. The proposed dual-stream architecture effectively distinguishes scenes that share similar global contexts but contain different localized objects. We evaluate the performance of the proposed framework on a publicly available benchmark indoor scene dataset. From the experimental results, we demonstrate the effectiveness of the proposed framework.

A hierarchical count data clustering based on Multinomial Nested Dirichlet Mixture using the Minorization-Maximization framework

Despite the widespread acceptance of mixture models among researchers, obtaining good results involves several challenges. In this paper, we address two main challenges: parameter estimation and data representation strategies.Expectation-Maximization (EM) is a widely used framework for parameter estimation. However, many factors complicate the process through intractable calculations of the posterior distribution and the parameters. Minorization-Maximization (MM) is an alternative framework that relaxes the complications and requirements of the EM. This paper adopts the MM framework for the Multinomial Nested Dirichlet Mixture in a hierarchical manner. The hierarchical nature of the MNDM is exploited through a Hierarchical Feature Learning framework (HFL), where the data represented is a result of the well-known Spatial Pyramid Matching method. Moreover, the mixture's components are determined by the Minimum Message Length (MML). Therefore, this paper presents an HFL framework for the data representation of the MNDM, where its learning is based on the MM framework, and its selection is based on MML. The validation of the two addressed improvements is proven through three visual datasets using recall and precision performance metrics.

Map retrieval intention recognition based on relevance feedback and geographic semantic guidance: For better understanding user retrieval demands

Effective retrieval is essential for finding resources in demand handily amidst extensive data records in data warehouse. Mainstream map retrieval methods suffer from intention gap problem and are incapable to describe sophisticated user demands precisely due to the limits of low- and middle-level text or visual feature matching, resulting in unsatisfactory retrieval results. Such limitations are more marked when map retrieval demands were characterized with joint constraints of geographic concepts. To address this issue, we propose a map retrieval intention recognition method to perceive user demands with relevance feedback samples and geographic semantics guidance. Specifically, we construct a hierarchical intention expression model to describe retrieval goals and their multi-dimensional attribute constrains; incorporate geographic ontologies to provide semantic guidance and facilitate recognition; utilize the frequent itemset mining (FIM) algorithm Apriori to generate intention candidates from relevance feedback samples, and search for the optimal intention set by adopting the minimum description length (MDL) principle. The experiments verify the effectiveness of Apriori algorithm and MDL principle on intention recognition. The proposed method outperforms the FIM algorithm Gene Ontology (RuleGO) and the Decision Tree algorithm with Hierarchical Features (DTHF) with higher recognition accuracy and noise tolerance. Furthermore, through our sample augmentation strategy, the method yields promising recognition accuracy even when the feedback sample size is as low as ten, substantially reducing the feedback burden in human-computer interactions. We envision that the application of our method in spatial data infrastructures (SDIs), such as geoportals and catalogue services, could enhance the quality of service and user experience in geospatial data discovery.

Emotion-aware hierarchical interaction network for multimodal image aesthetics assessment

Image aesthetics assessment (IAA) has attracted increasing attention recently but is still challenging due to its high abstraction and complexity. Intuitively, image emotion and aesthetics are both human subjective feelings evoked by visual content. Previous researches have demonstrated that image aesthetics has intrinsic relationships with emotion. In fact, human emotional experience has potential impact on the perception of image aesthetics. Therefore, emotion information can be exploited to enhance aesthetic representation learning. Inspired by this, this paper presents an Emotion-Aware Hierarchical Interaction NETwork (EAHI-NET) for multimodal image aesthetics assessment, which explores both intra-modal and inter-modal interactions between aesthetics and emotions hierarchically. Specifically, we first propose the intra-modal emotion-aesthetics interaction module to obtain emotion-enhanced visual and textual aesthetic representations respectively. Then we propose the inter-modal feature enhancement to obtain the cross-modal aesthetic and emotion features. Finally, we design the inter-modal emotion-aesthetics interaction module to further investigate the cross-modal interplay between aesthetics and emotion, based on which hierarchical feature representations are achieved for multimodal IAA. The extensive experiments show that the proposed method can outperform the state-of-the-arts on multimodal AVA and Photo.net datasets.

Multifunctional Hierarchical Nanoplatform with Anisotropic Bimodal Mesopores for Effective Neural Circuit Reconstruction after Spinal Cord Injury.

A persistent inflammatory response, intrinsic limitations in axonal regenerative capacity, and widespread presence of extrinsic axonal inhibitors impede the restoration of motor function after a spinal cord injury (SCI). A versatile treatment platform is urgently needed to address diverse clinical manifestations of SCI. Herein, we present a multifunctional nanoplatform with anisotropic bimodal mesopores for effective neural circuit reconstruction after SCI. The hierarchical nanoplatform features of a Janus structure consist of dual compartments of hydrophilic mesoporous silica (mSiO2) and hydrophobic periodic mesoporous organosilica (PMO), each possessing distinct pore sizes of 12 and 3 nm, respectively. Unlike traditional hierarchical mesoporous nanomaterials with dual-mesopores interlaced with each other, the two sets of mesopores in this Janus nanoplatform are spatially independent and possess completely distinct chemical properties. The Janus mesopores facilitate controllable codelivery of dual drugs with distinct properties: the hydrophilic macromolecular enoxaparin (ENO) and the hydrophobic small molecular paclitaxel (PTX). Anchoring with CeO2, the resulting mSiO2&PMO-CeO2-PTX&ENO nanoformulation not only effectively alleviates ROS-induced neuronal apoptosis but also enhances microtubule stability to promote intrinsic axonal regeneration and facilitates axonal extension by diminishing the inhibitory effect of extracellular chondroitin sulfate proteoglycans. We believe that this functional dual-mesoporous nanoplatform holds significant potential for combination therapy in treating severe multifaceted diseases.

EWT: Efficient Wavelet-Transformer for single image denoising

Transformer-based image denoising methods have shown remarkable potential but suffer from high computational cost and large memory footprint due to their linear operations for capturing long-range dependencies. In this work, we aim to develop a more resource-efficient Transformer-based image denoising method that maintains high performance. To this end, we propose an Efficient Wavelet Transformer (EWT), which incorporates a Frequency-domain Conversion Pipeline (FCP) to reduce image resolution without losing critical features, and a Multi-level Feature Aggregation Module (MFAM) with a Dual-stream Feature Extraction Block (DFEB) to harness hierarchical features effectively. EWT achieves a faster processing speed by over 80% and reduces GPU memory usage by more than 60% compared to the original Transformer, while still delivering denoising performance on par with state-of-the-art methods. Extensive experiments show that EWT significantly improves the efficiency of Transformer-based image denoising, providing a more balanced approach between performance and resource consumption.

AI-Empowered Multimodal Hierarchical Graph-Based Learning for Situation Awareness on Enhancing Disaster Responses

Situational awareness (SA) is crucial in disaster response, enhancing the understanding of the environment. Social media, with its extensive user base, offers valuable real-time information for such scenarios. Although SA systems excel in extracting disaster-related details from user-generated content, a common limitation in prior approaches is their emphasis on single-modal extraction rather than embracing multi-modalities. This paper proposed a multimodal hierarchical graph-based situational awareness (MHGSA) system for comprehensive disaster event classification. Specifically, the proposed multimodal hierarchical graph contains nodes representing different disaster events and the features of the event nodes are extracted from the corresponding images and acoustic features. The proposed feature extraction modules with multi-branches for vision and audio features provide hierarchical node features for disaster events of different granularities, aiming to build a coarse-granularity classification task to constrain the model and enhance fine-granularity classification. The relationships between different disaster events in multi-modalities are learned by graph convolutional neural networks to enhance the system’s ability to recognize disaster events, thus enabling the system to fuse complex features of vision and audio. Experimental results illustrate the effectiveness of the proposed visual and audio feature extraction modules in single-modal scenarios. Furthermore, the MHGSA successfully fuses visual and audio features, yielding promising results in disaster event classification tasks.

Electroencephalogram-Based ConvMixer Architecture for Recognizing Attention Deficit Hyperactivity Disorder in Children.

Attention deficit hyperactivity disorder (ADHD) is a neuro-developmental disorder that affects approximately 5-10% of school-aged children worldwide. Early diagnosis and intervention are essential to improve the quality of life of patients and their families. In this study, we propose ConvMixer-ECA, a novel deep learning architecture that combines ConvMixer with efficient channel attention (ECA) blocks for the accurate diagnosis of ADHD using electroencephalogram (EEG) signals. The model was trained and evaluated using EEG recordings from 60 healthy children and 61 children with ADHD. A series of experiments were conducted to evaluate the performance of the ConvMixer-ECA. The results showed that the ConvMixer-ECA performed well in ADHD recognition with 94.52% accuracy. The incorporation of attentional mechanisms, in particular ECA, improved the performance of ConvMixer; it outperformed other attention-based variants. In addition, ConvMixer-ECA outperformed state-of-the-art deep learning models including EEGNet, CNN, RNN, LSTM, and GRU. t-SNE visualization of the output of this model layer validated the effectiveness of ConvMixer-ECA in capturing the underlying patterns and features that separate ADHD from typically developing individuals through hierarchical feature learning. These outcomes demonstrate the potential of ConvMixer-ECA as a valuable tool to assist clinicians in the early diagnosis and intervention of ADHD in children.

Multi-level feature interaction image super-resolution network based on convolutional nonlinear spiking neural model

Image super-resolution (ISR) is designed to recover lost detail information from low-resolution images, resulting in high-quality and high-definition high-resolution images. In the existing single ISR (SISR) methods based on convolutional neural networks (CNN), however, most of the models cannot effectively combine global and local information and are also easy to ignore the correlation between different hierarchical feature information. To address these problems, this study proposes a multi-level feature interactive image super-resolution network, which is constructed by the convolutional units inspired by nonlinear spiking mechanism in nonlinear spiking neural P systems, including shallow feature processing, deep feature extraction and fusion, and reconstruction modules. The different omni domain self-attention blocks are introduced to extract global information in the deep feature extraction and fusion stage and formed a feature enhancement module having a Transformer structure using a novel convolutional unit for extracting local information. Furthermore, to adaptively fuse features between different hierarchies, we design a multi-level feature fusion module, which not only can adaptively fuse features between different hierarchies, but also can better interact with contextual information. The proposed model is compared with 16 state-of-the-art or baseline models on five benchmark datasets. The experimental results show that the proposed model not only achieves good reconstruction performance, but also strikes a good balance between model parameters and performance.

Learning context-aware local feature descriptors for 3D reconstruction

The generation of generalizable and discriminative descriptors plays a crucial role in image matching and 3D reconstruction. While numerous existing solutions are concentrated on encoding specific invariances, such as illumination or viewpoint invariance, they often face challenges in achieving robustness and generalization. These challenges arise from the frequent inadequacy of these solutions to effectively adapt to diverse and demanding environments due to their limited information capacity. In this paper, we introduce a novel approach aimed at maximizing the utilization of hidden feature informativeness to address these challenges. Specifically, we propose the Hierarchical Context-aware Aggregation Network (HCNet), which employs a hierarchical dense features constraint in a coarse-to-refinement description manner. In this approach, a coarse-level descriptor is used to present the overall information, while the refinement descriptor captures the detailed information of the image. Leveraging the strengths of both CNN and Transformer architectures, our hierarchical dense feature constraint encodes both local features and long-range information to efficiently generate dense feature descriptions. To boost descriptor informativeness and enhance matching accuracy, we introduce the Context-aware Attention Aggregation (CAA) model, which adaptively aggregates features from various scales through an efficient coarse-to-refinement manner. Additionally, we design a hierarchical triplet training strategy that considers both variant and invariant properties of hierarchical features, aiming to enhance descriptor informativeness while preserving their strong discriminative qualities. Our experiments, conducted on two popular feature-matching benchmarks, as well as a challenging long-term visual localization benchmark, demonstrate that our method significantly improves matching accuracy and outperforms state-of-the-art descriptors. Moreover, our approach exhibits superior generalization capabilities in various 3D reconstruction scenarios.

Granular3D: Delving into multi-granularity 3D scene graph prediction

This paper addresses the significant challenges in 3D Semantic Scene Graph (3DSSG) prediction, essential for understanding complex 3D environments. Traditional approaches, primarily using PointNet and Graph Convolutional Networks, struggle with effectively extracting multi-grained features from intricate 3D scenes, largely due to a focus on global scene processing and single-scale feature extraction. To overcome these limitations, we introduce Granular3D, a novel approach that shifts the focus towards multi-granularity analysis by predicting relation triplets from specific sub-scenes. One key is the Adaptive Instance Enveloping Method (AIEM), which establishes an approximate envelope structure around irregular instances, providing shape-adaptive local point cloud sampling, thereby comprehensively covering the contextual environments of instances. Moreover, Granular3D incorporates a Hierarchical Dual-Stage Network (HDSN), which differentiates and processes features of instances and their pairs at varying scales, leading to a targeted prediction of instance categories and their relationships. To advance the perception of sub-scene in HDSN, we design a Gather Point Transformer structure (GaPT) that enables the combinatorial interaction of local information from multiple point cloud sets, achieving a more comprehensive local contextual feature extraction. Extensive evaluations on the challenging 3DSSG benchmark demonstrate that our methods provide substantial improvements, establishing a new state-of-the-art in 3DSSG prediction, boosting the top-50 triplet accuracy by ＋2.8%.

ActiWise: Insight on Human Activity Recognition Using Deep Learning Approaches

In this study, we investigate the fusion of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks for human activity recognition (HAR). By integrating hierarchical spatial features extracted by CNNs with LSTM networks' temporal modelling capabilities, our approach excels in discerning nuanced patterns from raw sensor data collected via wearable devices. Through rigorous experimentation and validation, our CNN+LSTM model demonstrates robust performance in accurately classifying a spectrum of human activities. This research advances HAR methodologies, shedding light on the synergistic interplay between spatial and temporal modelling in activity recognition, with implications across healthcare, sports analytics, and human-computer interaction domains. Index Terms- Human activity recognition, deep learning, Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM).

A hierarchical recursive feature elimination algorithm to develop brain computer interface application of user behavior for statistical reasoning and decision making

BackgroundWith the aid of a brain computer interface (BCI), users can communicate and receive signals wirelessly or over wired connections to operate smart devices. A BCI classifier's architecture is quite difficult since numerous elements should be combined. These elements are made up of brain signals, which also include high levels of weak sounds that could provide reliable participant recordings of daily activities. We must use computer vision techniques to create a model in order to control those information. The high dimension and volume of signals present the classification classifier with its primary obstacles. New methodDue to this, we extracted and classified the brain activity in this study, and we also presented a novel hierarchical recursive feature elimination method that we refer to as HRFE embracing noisy additions. HRFE makes a variety of categorization suggestions to eliminate bias in classifying BCI systems of different types. We put the HRFE to the test on two BCI signal data sets—specifically, dataset I and BCI contests III—using shallow and deep convolution network classification techniques. Just a grid of assets is used to create electrocorticography (ECoG) signals on the contralateral (right) motor cortex, and these signals are recorded in the BCI contests III database. ResultsUsing ECoG signals, we choose the top 20 features that have the biggest effects on distortion and classification selection. Comparison with existing methodsThe simulation findings show that HRFE has a significant computer vision enhancement when compared to comparable feature selection methods in the literature, particularly for ECoG signal, which achieves about 93% reliability.

Microstructural mechanisms endowing high strength-ductility synergy in CoCrNi medium entropy alloy prepared by laser powder bed fusion

CoCrNi medium entropy alloy (MEA) exhibits significant potential as a structural component for engineering applications. However, the traditional processing methods for pursuing the superior mechanical properties of CoCrNi MEA are often time-consuming and inefficient. Laser powder bed fusion (LPBF) offers substantial processing flexibility without the need for complex secondary processing. In this study, we optimized the processing parameters for CoCrNi MEA through an orthogonal experimental design, achieving as-printed CoCrNi components with a relative density of up to 99.4 %. The microstructure of the as-printed CoCrNi revealed hierarchical features, including the molten pools, equiaxed and elongated columnar grains, sub-grain cellular microstructures, and high-density dislocation networks. The mechanical properties of as-printed CoCrNi were exceptional, demonstrating a synergistic combination of strength and ductility (yield strength: approximately 660.91 MPa, tensile strength: approximately 892.78 MPa, elongation: approximately 33.28 %, and a product of strength and elongation: 27.45 GPa %). Experimental characterization elucidated the activation and interplay of mechanisms such as dislocations, stacking faults, twinning-induced plasticity, and transformation-induced plasticity. These mechanisms collaboratively enhanced the refinement and heterogeneity of substructures, thereby endowing the as-printed CoCrNi with significant strength-ductility synergy. Molecular dynamics simulations indicated that, in the initial stages of plastic deformation, a significant activation of Shockley partial dislocations occurred, leading to the formation of stacking faults and the emergence of Hexagonal Close-Packed (HCP) phases. As plastic deformation progressed, a reverse phase transformation from HCP to Face-Centered Cubic (FCC) took place, resulting in the formation of nanotwins. This transformation generated a heterogeneous microstructure comprised of both nanotwins and HCP phases, suggesting a sequential twinning and phase transformation pathway in as-printed CoCrNi: from FCC to HCP, and subsequently to FCC nanotwins.

Heritage Identification of Monument Using Deep Learning

Abstract: There are many beautiful and colorful countries in the world which are famous for their culture and heritage. The historical landmarks of such countries convey a great deal through their magnificent structures and rich heritage. The extensive past and legacy of such nations are apparent in the splendid palaces, fortresses, towers, shrines, and cathedrals that are scattered throughout the entirety of their land. We need to protect cultural monuments and landmarks as they are one of the sources of inspiration for future generations. Traditional methods for monument identification often rely on manual inspection or rudimentary computer vision techniques, which can be time-consuming and error-prone. We can provide the best solutions using modern machine learning and deep learning techniques. In our model, we used an approach based on deep learning for the automated identification of heritage monuments from images. We leveraged the power of convolutional neural networks (CNNs) to extract hierarchical features from monument images and train a classifier to recognize different architectural styles and structures. We evaluated our approach on a dataset comprising diverse monument images collected from public archives and online repositories. We applied various pre-processing techniques such as image resizing, normalization of pixel values, and data augmentation. We analyzed and further improved the model’s performance for heritage identification of monuments.

Deep and wide nonnegative matrix factorization with embedded regularization

End-to-end learning is an advanced framework in deep learning. It combines feature extraction, followed by pattern recognition (classification, clustering, etc.) in a unified learning structure. However, these deep networks face several challenges such as overfitting, vanishing gradient, computational complexity, information loss in layers, and weak robustness to noisy data/features. To address these challenges, this paper presents Deep and Wide Nonnegative Matrix Factorization (DWNMF) with embedded regularization for the feature extraction stage of the end-to-end models. DWNMF aims to identify more robust features while preventing overfitting via embedding regularization. For this purpose, DWNMF integrates input data with its noisy versions as diverse augmented channels. Then, the features in all channels are extracted in parallel using distinct network branches. The parameters of this model learn the intrinsic hierarchical features in the channels of complex data objects. Finally, the extracted features in different channels are aggregated in a single feature space to perform the classification task. To embed regularization in the DWNMF model, some NMF neurons in the layers are substituted by random neurons to increase the stability and robustness of the extracted features. Experimental results confirm that the DWNMF model extracts more robust features, prevents overfitting, and achieves better classification accuracy compared to state-of-the-art methods.

A small target detection algorithm based on improved YOLOv5 in aerial image.

Uncrewed aerial vehicle (UAV) aerial photography technology is widely used in both industrial and military sectors, but remote sensing for small target detection still faces several challenges. Firstly, the small size of targets increases the difficulty of detection and recognition. Secondly, complex aerial environmental conditions, such as lighting changes and background noise, significantly affect the quality of detection. Rapid and accurate identification of target categories is also a key issue, requiring improvements in detection speed and accuracy. This study proposes an improved remote sensing target detection algorithm based on the YOLOv5 architecture. In the YOLOv5s model, the Distribution Focal Loss function is introduced to accelerate the convergence speed of the network and enhance the network's focus on annotated data. Simultaneously, adjustments are made to the Cross Stage Partial (CSP) network structure, modifying the convolution kernel size, adding a new stack-separated convolution module, and designing a new attention mechanism to achieve effective feature fusion between different hierarchical structure feature maps. Experimental results demonstrate a significant performance improvement of the proposed algorithm on the RSOD dataset, with a 3.5% increase in detection accuracy compared to the original algorithm. These findings indicate that our algorithm effectively enhances the precision of remote sensing target detection and holds potential application prospects.

Self-clustered GAN for precipitation nowcasting

This paper proposes a novel GAN framework with self-clustering approach for precipitation nowcasting (ClusterCast). Previous studies have primarily captured the motion vector using only a single latent space, making the models difficult to adapt to disparate space-time distribution of precipitation. Environmental factors (e.g., regional characteristics and precipitation scale) have an impact on precipitation systems and can cause non-stationary distribution. To tackle this problem, our key idea is to train a generator network to predict future radar frames by learning a sub-network that automatically labels precipitation types from a generative model. The training process consists of (i) clustering the hierarchical features derived from the generator stem using a sub-network and (ii) predicting future radar frames according to the self-supervised labels, enabling heterogeneous latent representation. Additionally, we attempt an ensemble forecast that prescribes random perturbations to improve performance. With the flexibility of representation learning, ClusterCast enables the model to learn precipitation distribution more accurately. Results indicate that our method generates non-blurry future frames by preventing mode collapse, and the proposed method demonstrates robustness across various precipitation scenarios. Extensive experiments demonstrate that our method outperforms four benchmarks on a 2-h prediction basis with a mean squared error (MSE) of 8.9% on unseen datasets.

BAF-Net: bidirectional attention-aware fluid pyramid feature integrated multimodal fusion network for diagnosis and prognosis

Objective. To go beyond the deficiencies of the three conventional multimodal fusion strategies (i.e. input-, feature- and output-level fusion), we propose a bidirectional attention-aware fluid pyramid feature integrated fusion network (BAF-Net) with cross-modal interactions for multimodal medical image diagnosis and prognosis. Approach. BAF-Net is composed of two identical branches to preserve the unimodal features and one bidirectional attention-aware distillation stream to progressively assimilate cross-modal complements and to learn supplementary features in both bottom-up and top-down processes. Fluid pyramid connections were adopted to integrate the hierarchical features at different levels of the network, and channel-wise attention modules were exploited to mitigate cross-modal cross-level incompatibility. Furthermore, depth-wise separable convolution was introduced to fuse the cross-modal cross-level features to alleviate the increase in parameters to a great extent. The generalization abilities of BAF-Net were evaluated in terms of two clinical tasks: (1) an in-house PET-CT dataset with 174 patients for differentiation between lung cancer and pulmonary tuberculosis. (2) A public multicenter PET-CT head and neck cancer dataset with 800 patients from nine centers for overall survival prediction. Main results. On the LC-PTB dataset, improved performance was found in BAF-Net (AUC = 0.7342) compared with input-level fusion model (AUC = 0.6825; p < 0.05), feature-level fusion model (AUC = 0.6968; p = 0.0547), output-level fusion model (AUC = 0.7011; p < 0.05). On the H&N cancer dataset, BAF-Net (C-index = 0.7241) outperformed the input-, feature-, and output-level fusion model, with 2.95%, 3.77%, and 1.52% increments of C-index (p = 0.3336, 0.0479 and 0.2911, respectively). The ablation experiments demonstrated the effectiveness of all the designed modules regarding all the evaluated metrics in both datasets. Significance. Extensive experiments on two datasets demonstrated better performance and robustness of BAF-Net than three conventional fusion strategies and PET or CT unimodal network in terms of diagnosis and prognosis.

Comparative Analysis of Machine Learning and Deep Learning in Practical Scenarios

With the emergence of ChatGPT, machine learning (ML) and deep learning (DL) technology have gradually attracted people's attention, and these two technologies have already penetrated People's Daily life unconsciously. However, the application of these two technologies in various industries is still in the development stage, and choosing an inappropriate technology in different scenarios will lead to problems such as a waste of resources. The purpose of this article is to introduce the basic concepts, principles, and common algorithms of these two technologies, and by comparing the results of different technologies in the same scene, this article will compare and analyze the advantages and disadvantages of ML and DL in feature generation, model complexity, data scale, etc. Finally, the different application scenarios of the two technologies are obtained. ML is better for situations where there is less data and the problem is relatively simple, while DL performs better for tasks that deal with large and complex data and require hierarchical feature representation. This article hopes to inspire problem-solving in this subject area.