Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimo...Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimodal Aspect-oriented Sentiment Classification(MASC).Currently,most existing models for JMASA only perform text and image feature encoding from a basic level,but often neglect the in-depth analysis of unimodal intrinsic features,which may lead to the low accuracy of aspect term extraction and the poor ability of sentiment prediction due to the insufficient learning of intra-modal features.Given this problem,we propose a Text-Image Feature Fine-grained Learning(TIFFL)model for JMASA.First,we construct an enhanced adjacency matrix of word dependencies and adopt graph convolutional network to learn the syntactic structure features for text,which addresses the context interference problem of identifying different aspect terms.Then,the adjective-noun pairs extracted from image are introduced to enable the semantic representation of visual features more intuitive,which addresses the ambiguous semantic extraction problem during image feature learning.Thereby,the model performance of aspect term extraction and sentiment polarity prediction can be further optimized and enhanced.Experiments on two Twitter benchmark datasets demonstrate that TIFFL achieves competitive results for JMASA,MATE and MASC,thus validating the effectiveness of our proposed methods.展开更多
Hepatocellular carcinoma presents with three distinct immune phenotypes,including immune-desert,immune-excluded,and immune-inflamed,indicating various treatment responses and prognostic outcomes.The clinical applicati...Hepatocellular carcinoma presents with three distinct immune phenotypes,including immune-desert,immune-excluded,and immune-inflamed,indicating various treatment responses and prognostic outcomes.The clinical application of multi-omics parameters is still restricted by the expensive and less accessible assays,although they accurately reflect immune status.A comprehensive evaluation framework based on“easy-to-obtain”multi-model clinical parameters is urgently required,incorporating clinical features to establish baseline patient profiles and disease staging;routine blood tests assessing systemic metabolic and functional status;immune cell subsets quantifying subcluster dynamics;imaging features delineating tumor morphology,spatial configuration,and perilesional anatomical relationships;immunohistochemical markers positioning qualitative and quantitative detection of tumor antigens from the cellular and molecular level.This integrated phenomic approach aims to improve prognostic stratification and clinical decision-making in hepatocellular carcinoma management conveniently and practically.展开更多
Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including ...Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including computed tomography(CT),magnetic resonance imaging(MRI),endoscopic imaging,and genomic profiles-to enable intelligent decision-making for individualized therapy.This approach leverages AI algorithms to fuse imaging,endoscopic,and omics data,facilitating comprehensive characterization of tumor biology,prediction of treatment response,and optimization of therapeutic strategies.By combining CT and MRI for structural assessment,endoscopic data for real-time visual inspection,and genomic information for molecular profiling,multimodal AI enhances the accuracy of patient stratification and treatment personalization.The clinical implementation of this technology demonstrates potential for improving patient outcomes,advancing precision oncology,and supporting individualized care in gastrointestinal cancers.Ultimately,multimodal AI serves as a transformative tool in oncology,bridging data integration with clinical application to effectively tailor therapies.展开更多
High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging ...High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging foundation models and multimodal learning frameworks are enabling scalable and transferable representations of cellular states,while advances in interpretability and real-world data integration are bridging the gap between discovery and clinical application.This paper outlines a concise roadmap for AI-driven,transcriptome-centered multi-omics integration in precision medicine(Figure 1).展开更多
Multimodal sensor fusion can make full use of the advantages of various sensors,make up for the shortcomings of a single sensor,achieve information verification or information security through information redundancy,a...Multimodal sensor fusion can make full use of the advantages of various sensors,make up for the shortcomings of a single sensor,achieve information verification or information security through information redundancy,and improve the reliability and safety of the system.Artificial intelligence(AI),referring to the simulation of human intelligence in machines that are programmed to think and learn like humans,represents a pivotal frontier in modern scientific research.With the continuous development and promotion of AI technology in Sensor 4.0 age,multimodal sensor fusion is becoming more and more intelligent and automated,and is expected to go further in the future.With this context,this review article takes a comprehensive look at the recent progress on AI-enhanced multimodal sensors and their integrated devices and systems.Based on the concept and principle of sensor technologies and AI algorithms,the theoretical underpinnings,technological breakthroughs,and pragmatic applications of AI-enhanced multimodal sensors in various fields such as robotics,healthcare,and environmental monitoring are highlighted.Through a comparative study of the dual/tri-modal sensors with and without using AI technologies(especially machine learning and deep learning),AI-enhanced multimodal sensors highlight the potential of AI to improve sensor performance,data processing,and decision-making capabilities.Furthermore,the review analyzes the challenges and opportunities afforded by AI-enhanced multimodal sensors,and offers a prospective outlook on the forthcoming advancements.展开更多
Thunderstorm wind gusts are small in scale,typically occurring within a range of a few kilometers.It is extremely challenging to monitor and forecast thunderstorm wind gusts using only automatic weather stations.There...Thunderstorm wind gusts are small in scale,typically occurring within a range of a few kilometers.It is extremely challenging to monitor and forecast thunderstorm wind gusts using only automatic weather stations.Therefore,it is necessary to establish thunderstorm wind gust identification techniques based on multisource high-resolution observations.This paper introduces a new algorithm,called thunderstorm wind gust identification network(TGNet).It leverages multimodal feature fusion to fuse the temporal and spatial features of thunderstorm wind gust events.The shapelet transform is first used to extract the temporal features of wind speeds from automatic weather stations,which is aimed at distinguishing thunderstorm wind gusts from those caused by synoptic-scale systems or typhoons.Then,the encoder,structured upon the U-shaped network(U-Net)and incorporating recurrent residual convolutional blocks(R2U-Net),is employed to extract the corresponding spatial convective characteristics of satellite,radar,and lightning observations.Finally,by using the multimodal deep fusion module based on multi-head cross-attention,the temporal features of wind speed at each automatic weather station are incorporated into the spatial features to obtain 10-minutely classification of thunderstorm wind gusts.TGNet products have high accuracy,with a critical success index reaching 0.77.Compared with those of U-Net and R2U-Net,the false alarm rate of TGNet products decreases by 31.28%and 24.15%,respectively.The new algorithm provides grid products of thunderstorm wind gusts with a spatial resolution of 0.01°,updated every 10minutes.The results are finer and more accurate,thereby helping to improve the accuracy of operational warnings for thunderstorm wind gusts.展开更多
Multimodal deep learning has emerged as a key paradigm in contemporary medical diagnostics,advancing precision medicine by enabling integration and learning from diverse data sources.The exponential growth of high-dim...Multimodal deep learning has emerged as a key paradigm in contemporary medical diagnostics,advancing precision medicine by enabling integration and learning from diverse data sources.The exponential growth of high-dimensional healthcare data,encompassing genomic,transcriptomic,and other omics profiles,as well as radiological imaging and histopathological slides,makes this approach increasingly important because,when examined separately,these data sources only offer a fragmented picture of intricate disease processes.Multimodal deep learning leverages the complementary properties of multiple data modalities to enable more accurate prognostic modeling,more robust disease characterization,and improved treatment decision-making.This review provides a comprehensive overview of the current state of multimodal deep learning approaches in medical diagnosis.We classify and examine important application domains,such as(1)radiology,where automated report generation and lesion detection are facilitated by image-text integration;(2)histopathology,where fusion models improve tumor classification and grading;and(3)multi-omics,where molecular subtypes and latent biomarkers are revealed through cross-modal learning.We provide an overview of representative research,methodological advancements,and clinical consequences for each domain.Additionally,we critically analyzed the fundamental issues preventing wider adoption,including computational complexity(particularly in training scalable,multi-branch networks),data heterogeneity(resulting from modality-specific noise,resolution variations,and inconsistent annotations),and the challenge of maintaining significant cross-modal correlations during fusion.These problems impede interpretability,which is crucial for clinical trust and use,in addition to performance and generalizability.Lastly,we outline important areas for future research,including the development of standardized protocols for harmonizing data,the creation of lightweight and interpretable fusion architectures,the integration of real-time clinical decision support systems,and the promotion of cooperation for federated multimodal learning.Our goal is to provide researchers and clinicians with a concise overview of the field’s present state,enduring constraints,and exciting directions for further research through this review.展开更多
P-glycoprotein(P-gp)is a transmembrane protein widely involved in the absorption,distribution,metabolism,excretion,and toxicity(ADMET)of drugs within the human body.Accurate prediction of Pgp inhibitors and substrates...P-glycoprotein(P-gp)is a transmembrane protein widely involved in the absorption,distribution,metabolism,excretion,and toxicity(ADMET)of drugs within the human body.Accurate prediction of Pgp inhibitors and substrates is crucial for drug discovery and toxicological assessment.However,existing models rely on limited molecular information,leading to suboptimal model performance for predicting P-gp inhibitors and substrates.To overcome this challenge,we compiled an extensive dataset from public databases and literature,consisting of 5,943 P-gp inhibitors and 4,018 substrates,notable for their high quantity,quality,and structural uniqueness.In addition,we curated two external test sets to validate the model's generalization capability.Subsequently,we developed a multimodal graph contrastive learning(GCL)model for the prediction of P-gp inhibitors and substrates(MC-PGP).This framework integrates three types of features from Simplified Molecular Input Line Entry System(SMILES)sequences,molecular fingerprints,and molecular graphs using an attention-based fusion strategy to generate a unified molecular representation.Furthermore,we employed a GCL approach to enhance structural representations by aligning local and global structures.Extensive experimental results highlight the superior performance of MC-PGP,which achieves improvements in the area under the curve of receiver operating characteristic(AUC-ROC)of 9.82%and 10.62%on the external P-gp inhibitor and external P-gp substrate datasets,respectively,compared with 12 state-of-the-art methods.Furthermore,the interpretability analysis of all three molecular feature types offers comprehensive and complementary insights,demonstrating that MC-PGP effectively identifies key functional groups involved in P-gp interactions.These chemically intuitive insights provide valuable guidance for the design and optimization of drug candidates.展开更多
Sleep monitoring is an important part of health management because sleep quality is crucial for restoration of human health.However,current commercial products of polysomnography are cumbersome with connecting wires a...Sleep monitoring is an important part of health management because sleep quality is crucial for restoration of human health.However,current commercial products of polysomnography are cumbersome with connecting wires and state-of-the-art flexible sensors are still interferential for being attached to the body.Herein,we develop a flexible-integrated multimodal sensing patch based on hydrogel and its application in unconstraint sleep monitoring.The patch comprises a bottom hydrogel-based dualmode pressure–temperature sensing layer and a top electrospun nanofiber-based non-contact detection layer as one integrated device.The hydrogel as core substrate exhibits strong toughness and water retention,and the multimodal sensing of temperature,pressure,and non-contact proximity is realized based on different sensing mechanisms with no crosstalk interference.The multimodal sensing function is verified in a simulated real-world scenario by a robotic hand grasping objects to validate its practicability.Multiple multimodal sensing patches integrated on different locations of a pillow are assembled for intelligent sleep monitoring.Versatile human–pillow interaction information as well as their evolution over time are acquired and analyzed by a one-dimensional convolutional neural network.Track of head movement and recognition of bad patterns that may lead to poor sleep are achieved,which provides a promising approach for sleep monitoring.展开更多
Mate choice plays a pivotal role in wildlife reproduction and population sustainability.The assessment of sexual displays in noise poses a common challenge for wildlife.Multimodal signals are hypothesized to be favore...Mate choice plays a pivotal role in wildlife reproduction and population sustainability.The assessment of sexual displays in noise poses a common challenge for wildlife.Multimodal signals are hypothesized to be favored since they improve the accuracy of signal detection and discrimination in noise.We verified whether female treefrogs exhibit a heightened reliance on visual cues when acoustic cues are drowned out by the noise and whether increased call complexity can compensate for the attractiveness differences between unimodal and multimodal signals.Our results demonstrated that female treefrogs prefer longer courtship signals in the absence of noise.Meanwhile,increasing call complexity effectively mitigated the attractiveness difference between acoustic and visual/multimodal signals.However,female treefrogs did not shift their reliance to visual signals when masked by noise.Noise prolonged the duration required for females to make a mate choice in most cases and reduced female preferences for attractive signals regardless of whether the mating scene was unimodal or multimodal,which lends further the hypothesis of cross-sensory interference.We examined how female treefrogs weigh unimodal and multimodal courtship cues in the absence and presence of noise and offered distinct perspectives on the interplay of multi-sensory sexual displays in noise.This study enhanced our comprehension of noise interference in mating choice and established a novel,comprehensive scientific foundation for the prevention and control of multimodal sensory pollution.展开更多
Hateful meme is a multimodal medium that combines images and texts.The potential hate content of hateful memes has caused serious problems for social media security.The current hateful memes classification task faces ...Hateful meme is a multimodal medium that combines images and texts.The potential hate content of hateful memes has caused serious problems for social media security.The current hateful memes classification task faces significant data scarcity challenges,and direct fine-tuning of large-scale pre-trained models often leads to severe overfitting issues.In addition,it is a challenge to understand the underlying relationship between text and images in the hateful memes.To address these issues,we propose a multimodal hateful memes classification model named LABF,which is based on low-rank adapter layers and bidirectional gated feature fusion.Firstly,low-rank adapter layers are adopted to learn the feature representation of the new dataset.This is achieved by introducing a small number of additional parameters while retaining prior knowledge of the CLIP model,which effectively alleviates the overfitting phenomenon.Secondly,a bidirectional gated feature fusion mechanism is designed to dynamically adjust the interaction weights of text and image features to achieve finer cross-modal fusion.Experimental results show that the method significantly outperforms existing methods on two public datasets,verifying its effectiveness and robustness.展开更多
Nonfungible tokens(NFTs)have become highly sought-after assets in recent years,exhibiting potential for profitability and hedging.The large and lucrative NFT market has attracted both practitioners and researchers to ...Nonfungible tokens(NFTs)have become highly sought-after assets in recent years,exhibiting potential for profitability and hedging.The large and lucrative NFT market has attracted both practitioners and researchers to develop NFT price-prediction models.However,the extant models have some weaknesses in terms of model comprehensiveness and operational convenience.To address these research gaps,we propose a multimodal end-to-end interpretable deep learning(MEID)framework for NFT investment.Our model integrates visual features,textual descriptions,transaction indicators,and historical price time series by leveraging the advantages of convolutional neural networks(CNNs),adopts integrated gradient(IG)to improve interpretability,and designs a built-in financial evaluation mechanism to generate not only the predicted price category but also the recommended purchase level.The experimental results demonstrate that the proposed MEID framework has excellent properties in terms of the evaluation metrics.The proposed MEID framework could help investors identify market opportunities and help NFT transaction platforms design smart investment tools and improve transaction volume.展开更多
With the rapid growth of the Internet and social media, information is widely disseminated in multimodal forms, such as text and images, where discriminatory content can manifest in various ways. Discrimination detect...With the rapid growth of the Internet and social media, information is widely disseminated in multimodal forms, such as text and images, where discriminatory content can manifest in various ways. Discrimination detection techniques for multilingual and multimodal data can identify potential discriminatory behavior and help foster a more equitable and inclusive cyberspace. However, existing methods often struggle in complex contexts and multilingual environments. To address these challenges, this paper proposes an innovative detection method, using image and multilingual text encoders to separately extract features from different modalities. It continuously updates a historical feature memory bank, aggregates the Top-K most similar samples, and utilizes a Gated Recurrent Unit (GRU) to integrate current and historical features, generating enhanced feature representations with stronger semantic expressiveness to improve the model’s ability to capture discriminatory signals. Experimental results demonstrate that the proposed method exhibits superior discriminative power and detection accuracy in multilingual and multimodal contexts, offering a reliable and effective solution for identifying discriminatory content.展开更多
Background:Irregular heartbeats can have serious health implications if left undetected and untreated for an extended period of time.Methods:This study leverages machine learning(ML)techniques to classify electrocardi...Background:Irregular heartbeats can have serious health implications if left undetected and untreated for an extended period of time.Methods:This study leverages machine learning(ML)techniques to classify electrocardiogram(ECG)heartbeats,comparing traditional feature-based ML methods with innovative image-based approaches.The dataset underwent rigorous preprocessing,including down-sampling,frequency filtering,beat segmentation,and normalization.Two methodologies were explored:(1)handcrafted feature extraction,utilizing metrics like heart rate variability and RR distances with LightGBM classifiers,and(2)image transformation of ECG signals using Gramian Angular Field(GAF),Markov Transition Field(MTF),and Recurrence Plot(RP),enabling multimodal input for convolutional neural networks(CNNs).The Synthetic Minority Oversampling Technique(SMOTE)addressed data imbalance,significantly improving minority-class metrics.Results:The handcrafted feature approach achieved notable performance,with LightGBM excelling in precision and recall.Image-based classification further enhanced outcomes,with a custom Inception-based CNN,attaining an 85%F1 score and 97%accuracy using combined GAF,MTF,and RP transformations.Statistical analyses confirmed the significance of these improvements.Conclusion:This work highlights the potential of ML for cardiac irregularities detection,demonstrating that combining advanced preprocessing,feature engineering,and state-of-the-art neural networks can improve classification accuracy.These findings contribute to advancing AI-driven diagnostic tools,offering promising implications for cardiovascular healthcare.展开更多
In the context of digitalization,course resources exhibit multimodal characteristics,covering various forms such as text,images,and videos.Course knowledge and learning resources are becoming increasingly diverse,prov...In the context of digitalization,course resources exhibit multimodal characteristics,covering various forms such as text,images,and videos.Course knowledge and learning resources are becoming increasingly diverse,providing favorable conditions for students’in-depth and efficient learning.Against this backdrop,how to scientifically apply emerging technologies to automatically collect,process,and integrate digital learning resources such as voices,videos,and courseware texts,and better innovate the organization and presentation forms of course knowledge has become an important development direction for“artificial intelligence+education.”This article elaborates on the elements and characteristics of knowledge graphs,analyzes the construction steps of knowledge graphs,and explores the construction methods of multimodal course knowledge graphs from aspects such as dataset collection,course knowledge ontology identification,knowledge discovery,and association,providing references for the intelligent application of online open courses.展开更多
With the popularization of social media,stickers have become an important tool for young students to express themselves and resist mainstream culture due to their unique visual and emotional expressiveness.Most existi...With the popularization of social media,stickers have become an important tool for young students to express themselves and resist mainstream culture due to their unique visual and emotional expressiveness.Most existing studies focus on the negative impacts of spoof stickers,while paying insufficient attention to their positive functions.From the perspective of multimodal metaphor,this paper uses methods such as virtual ethnography and image-text analysis to clarify the connotation of stickers,understand the evolution of their digital dissemination forms,and explore the multiple functions of subcultural stickers in the social interactions between teachers and students.Young students use stickers to convey emotions and information.Their expressive function,social function,and cultural metaphor function progress in a progressive manner.This not only shapes students’values but also promotes self-expression and teacher-student interaction.It also reminds teachers to correct students’negative thoughts by using stickers,achieving the effect of“cultivating and influencing people through culture.”展开更多
In this study,we present a small,integrated jumping-crawling robot capable of intermittent jumping and self-resetting.Compared to robots with a single mode of locomotion,this multi-modal robot exhibits enhanced obstac...In this study,we present a small,integrated jumping-crawling robot capable of intermittent jumping and self-resetting.Compared to robots with a single mode of locomotion,this multi-modal robot exhibits enhanced obstacle-surmounting capabilities.To achieve this,the robot employs a novel combination of a jumping module and a crawling module.The jumping module features improved energy storage capacity and an active clutch.Within the constraints of structural robustness,the jumping module maximizes the explosive power of the linear spring by utilizing the mechanical advantage of a closed-loop mechanism and controls the energy flow of the jumping module through an active clutch mechanism.Furthermore,inspired by the limb movements of tortoises during crawling and self-righting,a single-degree-of-freedom spatial four-bar crawling mechanism was designed to enable crawling,steering,and resetting functions.To demonstrate its practicality,the integrated jumping-crawling robot was tested in a laboratory environment for functions such as jumping,crawling,self-resetting,and steering.Experimental results confirmed the feasibility of the proposed integrated jumping-crawling robot.展开更多
Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate...Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate answer.In this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in Arabic.To support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the trade-o.between the model complexity and the overall model performance.Some fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA models.So far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no questions.Hence,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA systems.Experiments indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that a.ects the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model complexity.The Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.展开更多
To address the inherent trade-off between mechanical strength and repair efficiency in conventional microcapsule-based self-healing technologies,this study presents an eggshell-inspired approach for fabricating high-l...To address the inherent trade-off between mechanical strength and repair efficiency in conventional microcapsule-based self-healing technologies,this study presents an eggshell-inspired approach for fabricating high-load rigid porous microcapsules(HLRPMs)through subcritical water etching.By optimizing the subcritical water treatment parameters(OH−concentration:0.031 mol/L,tem-perature:240°C,duration:1.5 h),nanoscale through-holes were generated on hollow glass microspheres(shell thickness≈700 nm).The subsequent gradient pressure infiltration of flaxseed oil enabled a record-high core content of 88.2%.Systematic investigations demonstrated that incorporating 3 wt%HLRPMs into epoxy resin composites preserved excellent dielectric properties(breakdown strength≥30 kV/mm)and enhanced tensile strength by 7.52%.In addressing multimodal damage,the system achieved a 95.5%filling efficiency for mechanical scratches,a 97.0%reduction in frictional damage depth,and a 96.2%recovery of insulation following electrical treeing.This biomimetic microcapsule system concurrently improved self-healing capability and matrix performance,offering a promising strategy for the development of next-generation smart insulating materials.展开更多
Pedestrian trajectory prediction can significantly enhance the perception and decision-making capabilities of autonomous driving systems and intelligent surveillance systems based on camera sensors by predicting the s...Pedestrian trajectory prediction can significantly enhance the perception and decision-making capabilities of autonomous driving systems and intelligent surveillance systems based on camera sensors by predicting the states and behavior intentions of surrounding pedestrians.However,existing trajectory prediction methods remain failing to effectively model the diverse and complex interactions in the real world,including pedestrian-pedestrian interactions and pedestrian-environment interactions.Besides,these methods are not effective in capturing and characterizing the multimodal property of future trajectories.To address these challenges above,we propose to devise a handdesigned graph convolution and spatial cross attention to dynamically capture the diverse spatial interactions between pedestrians.To effectively explore the impact of scenarios on pedestrian trajectory,we build a pedestrian map,which can reflect the scene constraints and pedestrian motion preferences.Meanwhile,we construct a trajectory multimodality-aware module to capture the different potential mode implicit in diverse social behaviors for pedestrian future trajectory uncertainty.Finally,we compared the proposed method with trajectory prediction baselines on commonly used public pedestrian benchmarks,demonstrating the superior performance of our approach.展开更多
基金supported by the Science and Technology Project of Henan Province(No.222102210081).
文摘Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimodal Aspect-oriented Sentiment Classification(MASC).Currently,most existing models for JMASA only perform text and image feature encoding from a basic level,but often neglect the in-depth analysis of unimodal intrinsic features,which may lead to the low accuracy of aspect term extraction and the poor ability of sentiment prediction due to the insufficient learning of intra-modal features.Given this problem,we propose a Text-Image Feature Fine-grained Learning(TIFFL)model for JMASA.First,we construct an enhanced adjacency matrix of word dependencies and adopt graph convolutional network to learn the syntactic structure features for text,which addresses the context interference problem of identifying different aspect terms.Then,the adjective-noun pairs extracted from image are introduced to enable the semantic representation of visual features more intuitive,which addresses the ambiguous semantic extraction problem during image feature learning.Thereby,the model performance of aspect term extraction and sentiment polarity prediction can be further optimized and enhanced.Experiments on two Twitter benchmark datasets demonstrate that TIFFL achieves competitive results for JMASA,MATE and MASC,thus validating the effectiveness of our proposed methods.
文摘Hepatocellular carcinoma presents with three distinct immune phenotypes,including immune-desert,immune-excluded,and immune-inflamed,indicating various treatment responses and prognostic outcomes.The clinical application of multi-omics parameters is still restricted by the expensive and less accessible assays,although they accurately reflect immune status.A comprehensive evaluation framework based on“easy-to-obtain”multi-model clinical parameters is urgently required,incorporating clinical features to establish baseline patient profiles and disease staging;routine blood tests assessing systemic metabolic and functional status;immune cell subsets quantifying subcluster dynamics;imaging features delineating tumor morphology,spatial configuration,and perilesional anatomical relationships;immunohistochemical markers positioning qualitative and quantitative detection of tumor antigens from the cellular and molecular level.This integrated phenomic approach aims to improve prognostic stratification and clinical decision-making in hepatocellular carcinoma management conveniently and practically.
基金Supported by Xuhui District Health Commission,No.SHXH202214.
文摘Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including computed tomography(CT),magnetic resonance imaging(MRI),endoscopic imaging,and genomic profiles-to enable intelligent decision-making for individualized therapy.This approach leverages AI algorithms to fuse imaging,endoscopic,and omics data,facilitating comprehensive characterization of tumor biology,prediction of treatment response,and optimization of therapeutic strategies.By combining CT and MRI for structural assessment,endoscopic data for real-time visual inspection,and genomic information for molecular profiling,multimodal AI enhances the accuracy of patient stratification and treatment personalization.The clinical implementation of this technology demonstrates potential for improving patient outcomes,advancing precision oncology,and supporting individualized care in gastrointestinal cancers.Ultimately,multimodal AI serves as a transformative tool in oncology,bridging data integration with clinical application to effectively tailor therapies.
文摘High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging foundation models and multimodal learning frameworks are enabling scalable and transferable representations of cellular states,while advances in interpretability and real-world data integration are bridging the gap between discovery and clinical application.This paper outlines a concise roadmap for AI-driven,transcriptome-centered multi-omics integration in precision medicine(Figure 1).
基金supported by the National Natural Science Foundation of China(No.62404111)Natural Science Foundation of Jiangsu Province(No.BK20240635)+2 种基金Natural Science Foundation of the Jiangsu Higher Education Institutions of China(No.24KJB510025)Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications(No.NY223157 and NY223156)Opening Project of Advanced Inte-grated Circuit Package and Testing Research Center of Jiangsu Province(No.NTIKFJJ202303).
文摘Multimodal sensor fusion can make full use of the advantages of various sensors,make up for the shortcomings of a single sensor,achieve information verification or information security through information redundancy,and improve the reliability and safety of the system.Artificial intelligence(AI),referring to the simulation of human intelligence in machines that are programmed to think and learn like humans,represents a pivotal frontier in modern scientific research.With the continuous development and promotion of AI technology in Sensor 4.0 age,multimodal sensor fusion is becoming more and more intelligent and automated,and is expected to go further in the future.With this context,this review article takes a comprehensive look at the recent progress on AI-enhanced multimodal sensors and their integrated devices and systems.Based on the concept and principle of sensor technologies and AI algorithms,the theoretical underpinnings,technological breakthroughs,and pragmatic applications of AI-enhanced multimodal sensors in various fields such as robotics,healthcare,and environmental monitoring are highlighted.Through a comparative study of the dual/tri-modal sensors with and without using AI technologies(especially machine learning and deep learning),AI-enhanced multimodal sensors highlight the potential of AI to improve sensor performance,data processing,and decision-making capabilities.Furthermore,the review analyzes the challenges and opportunities afforded by AI-enhanced multimodal sensors,and offers a prospective outlook on the forthcoming advancements.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFC3004104)the National Natural Science Foundation of China(Grant No.U2342204)+4 种基金the Innovation and Development Program of the China Meteorological Administration(Grant No.CXFZ2024J001)the Open Research Project of the Key Open Laboratory of Hydrology and Meteorology of the China Meteorological Administration(Grant No.23SWQXZ010)the Science and Technology Plan Project of Zhejiang Province(Grant No.2022C03150)the Open Research Fund Project of Anyang National Climate Observatory(Grant No.AYNCOF202401)the Open Bidding for Selecting the Best Candidates Program(Grant No.CMAJBGS202318)。
文摘Thunderstorm wind gusts are small in scale,typically occurring within a range of a few kilometers.It is extremely challenging to monitor and forecast thunderstorm wind gusts using only automatic weather stations.Therefore,it is necessary to establish thunderstorm wind gust identification techniques based on multisource high-resolution observations.This paper introduces a new algorithm,called thunderstorm wind gust identification network(TGNet).It leverages multimodal feature fusion to fuse the temporal and spatial features of thunderstorm wind gust events.The shapelet transform is first used to extract the temporal features of wind speeds from automatic weather stations,which is aimed at distinguishing thunderstorm wind gusts from those caused by synoptic-scale systems or typhoons.Then,the encoder,structured upon the U-shaped network(U-Net)and incorporating recurrent residual convolutional blocks(R2U-Net),is employed to extract the corresponding spatial convective characteristics of satellite,radar,and lightning observations.Finally,by using the multimodal deep fusion module based on multi-head cross-attention,the temporal features of wind speed at each automatic weather station are incorporated into the spatial features to obtain 10-minutely classification of thunderstorm wind gusts.TGNet products have high accuracy,with a critical success index reaching 0.77.Compared with those of U-Net and R2U-Net,the false alarm rate of TGNet products decreases by 31.28%and 24.15%,respectively.The new algorithm provides grid products of thunderstorm wind gusts with a spatial resolution of 0.01°,updated every 10minutes.The results are finer and more accurate,thereby helping to improve the accuracy of operational warnings for thunderstorm wind gusts.
文摘Multimodal deep learning has emerged as a key paradigm in contemporary medical diagnostics,advancing precision medicine by enabling integration and learning from diverse data sources.The exponential growth of high-dimensional healthcare data,encompassing genomic,transcriptomic,and other omics profiles,as well as radiological imaging and histopathological slides,makes this approach increasingly important because,when examined separately,these data sources only offer a fragmented picture of intricate disease processes.Multimodal deep learning leverages the complementary properties of multiple data modalities to enable more accurate prognostic modeling,more robust disease characterization,and improved treatment decision-making.This review provides a comprehensive overview of the current state of multimodal deep learning approaches in medical diagnosis.We classify and examine important application domains,such as(1)radiology,where automated report generation and lesion detection are facilitated by image-text integration;(2)histopathology,where fusion models improve tumor classification and grading;and(3)multi-omics,where molecular subtypes and latent biomarkers are revealed through cross-modal learning.We provide an overview of representative research,methodological advancements,and clinical consequences for each domain.Additionally,we critically analyzed the fundamental issues preventing wider adoption,including computational complexity(particularly in training scalable,multi-branch networks),data heterogeneity(resulting from modality-specific noise,resolution variations,and inconsistent annotations),and the challenge of maintaining significant cross-modal correlations during fusion.These problems impede interpretability,which is crucial for clinical trust and use,in addition to performance and generalizability.Lastly,we outline important areas for future research,including the development of standardized protocols for harmonizing data,the creation of lightweight and interpretable fusion architectures,the integration of real-time clinical decision support systems,and the promotion of cooperation for federated multimodal learning.Our goal is to provide researchers and clinicians with a concise overview of the field’s present state,enduring constraints,and exciting directions for further research through this review.
基金supported by the National Key Research and Development Program of China(Program No.:2022YFF1203003)the National Natural Science Foundation of China(Grant No.:82373791).
文摘P-glycoprotein(P-gp)is a transmembrane protein widely involved in the absorption,distribution,metabolism,excretion,and toxicity(ADMET)of drugs within the human body.Accurate prediction of Pgp inhibitors and substrates is crucial for drug discovery and toxicological assessment.However,existing models rely on limited molecular information,leading to suboptimal model performance for predicting P-gp inhibitors and substrates.To overcome this challenge,we compiled an extensive dataset from public databases and literature,consisting of 5,943 P-gp inhibitors and 4,018 substrates,notable for their high quantity,quality,and structural uniqueness.In addition,we curated two external test sets to validate the model's generalization capability.Subsequently,we developed a multimodal graph contrastive learning(GCL)model for the prediction of P-gp inhibitors and substrates(MC-PGP).This framework integrates three types of features from Simplified Molecular Input Line Entry System(SMILES)sequences,molecular fingerprints,and molecular graphs using an attention-based fusion strategy to generate a unified molecular representation.Furthermore,we employed a GCL approach to enhance structural representations by aligning local and global structures.Extensive experimental results highlight the superior performance of MC-PGP,which achieves improvements in the area under the curve of receiver operating characteristic(AUC-ROC)of 9.82%and 10.62%on the external P-gp inhibitor and external P-gp substrate datasets,respectively,compared with 12 state-of-the-art methods.Furthermore,the interpretability analysis of all three molecular feature types offers comprehensive and complementary insights,demonstrating that MC-PGP effectively identifies key functional groups involved in P-gp interactions.These chemically intuitive insights provide valuable guidance for the design and optimization of drug candidates.
基金supported by the National Key Research and Development Program of China under Grant(2024YFE0100400)Taishan Scholars Project Special Funds(tsqn202312035)+2 种基金the open research foundation of State Key Laboratory of Integrated Chips and Systems,the Tianjin Science and Technology Plan Project(No.22JCZDJC00630)the Higher Education Institution Science and Technology Research Project of Hebei Province(No.JZX2024024)Jinan City-University Integrated Development Strategy Project under Grant(JNSX2023017).
文摘Sleep monitoring is an important part of health management because sleep quality is crucial for restoration of human health.However,current commercial products of polysomnography are cumbersome with connecting wires and state-of-the-art flexible sensors are still interferential for being attached to the body.Herein,we develop a flexible-integrated multimodal sensing patch based on hydrogel and its application in unconstraint sleep monitoring.The patch comprises a bottom hydrogel-based dualmode pressure–temperature sensing layer and a top electrospun nanofiber-based non-contact detection layer as one integrated device.The hydrogel as core substrate exhibits strong toughness and water retention,and the multimodal sensing of temperature,pressure,and non-contact proximity is realized based on different sensing mechanisms with no crosstalk interference.The multimodal sensing function is verified in a simulated real-world scenario by a robotic hand grasping objects to validate its practicability.Multiple multimodal sensing patches integrated on different locations of a pillow are assembled for intelligent sleep monitoring.Versatile human–pillow interaction information as well as their evolution over time are acquired and analyzed by a one-dimensional convolutional neural network.Track of head movement and recognition of bad patterns that may lead to poor sleep are achieved,which provides a promising approach for sleep monitoring.
基金supported by the National Key Programme of Research and Development,Ministry of Science and Technology(2022YFF1301401)National Natural Science Foundation of China(32370536,32470505)+2 种基金the Sichuan Scienceand Technology Program(2022JDTD0026,2022NSFSC1694)the Open Project of Ministry of Education Key Laboratory for Ecology of Tropical Islands,Hainan Normal University,China(No.HNSF-OP-2024-01)CIB Youth Exploration Project(QNTS202304).
文摘Mate choice plays a pivotal role in wildlife reproduction and population sustainability.The assessment of sexual displays in noise poses a common challenge for wildlife.Multimodal signals are hypothesized to be favored since they improve the accuracy of signal detection and discrimination in noise.We verified whether female treefrogs exhibit a heightened reliance on visual cues when acoustic cues are drowned out by the noise and whether increased call complexity can compensate for the attractiveness differences between unimodal and multimodal signals.Our results demonstrated that female treefrogs prefer longer courtship signals in the absence of noise.Meanwhile,increasing call complexity effectively mitigated the attractiveness difference between acoustic and visual/multimodal signals.However,female treefrogs did not shift their reliance to visual signals when masked by noise.Noise prolonged the duration required for females to make a mate choice in most cases and reduced female preferences for attractive signals regardless of whether the mating scene was unimodal or multimodal,which lends further the hypothesis of cross-sensory interference.We examined how female treefrogs weigh unimodal and multimodal courtship cues in the absence and presence of noise and offered distinct perspectives on the interplay of multi-sensory sexual displays in noise.This study enhanced our comprehension of noise interference in mating choice and established a novel,comprehensive scientific foundation for the prevention and control of multimodal sensory pollution.
基金supported by the Funding for Research on the Evolution of Cyberbullying Incidents and Intervention Strategies(24BSH033)Discipline Innovation and Talent Introduction Bases in Higher Education Institutions(B20087).
文摘Hateful meme is a multimodal medium that combines images and texts.The potential hate content of hateful memes has caused serious problems for social media security.The current hateful memes classification task faces significant data scarcity challenges,and direct fine-tuning of large-scale pre-trained models often leads to severe overfitting issues.In addition,it is a challenge to understand the underlying relationship between text and images in the hateful memes.To address these issues,we propose a multimodal hateful memes classification model named LABF,which is based on low-rank adapter layers and bidirectional gated feature fusion.Firstly,low-rank adapter layers are adopted to learn the feature representation of the new dataset.This is achieved by introducing a small number of additional parameters while retaining prior knowledge of the CLIP model,which effectively alleviates the overfitting phenomenon.Secondly,a bidirectional gated feature fusion mechanism is designed to dynamically adjust the interaction weights of text and image features to achieve finer cross-modal fusion.Experimental results show that the method significantly outperforms existing methods on two public datasets,verifying its effectiveness and robustness.
基金supported by the National Key Research and Development Program of China(Project No.2022YFC3320800)the National Natural Science Foundation of China(Project No.72571210).
文摘Nonfungible tokens(NFTs)have become highly sought-after assets in recent years,exhibiting potential for profitability and hedging.The large and lucrative NFT market has attracted both practitioners and researchers to develop NFT price-prediction models.However,the extant models have some weaknesses in terms of model comprehensiveness and operational convenience.To address these research gaps,we propose a multimodal end-to-end interpretable deep learning(MEID)framework for NFT investment.Our model integrates visual features,textual descriptions,transaction indicators,and historical price time series by leveraging the advantages of convolutional neural networks(CNNs),adopts integrated gradient(IG)to improve interpretability,and designs a built-in financial evaluation mechanism to generate not only the predicted price category but also the recommended purchase level.The experimental results demonstrate that the proposed MEID framework has excellent properties in terms of the evaluation metrics.The proposed MEID framework could help investors identify market opportunities and help NFT transaction platforms design smart investment tools and improve transaction volume.
基金funded by the Open Foundation of Key Laboratory of Cyberspace Security,Ministry of Education[KLCS20240210].
文摘With the rapid growth of the Internet and social media, information is widely disseminated in multimodal forms, such as text and images, where discriminatory content can manifest in various ways. Discrimination detection techniques for multilingual and multimodal data can identify potential discriminatory behavior and help foster a more equitable and inclusive cyberspace. However, existing methods often struggle in complex contexts and multilingual environments. To address these challenges, this paper proposes an innovative detection method, using image and multilingual text encoders to separately extract features from different modalities. It continuously updates a historical feature memory bank, aggregates the Top-K most similar samples, and utilizes a Gated Recurrent Unit (GRU) to integrate current and historical features, generating enhanced feature representations with stronger semantic expressiveness to improve the model’s ability to capture discriminatory signals. Experimental results demonstrate that the proposed method exhibits superior discriminative power and detection accuracy in multilingual and multimodal contexts, offering a reliable and effective solution for identifying discriminatory content.
文摘Background:Irregular heartbeats can have serious health implications if left undetected and untreated for an extended period of time.Methods:This study leverages machine learning(ML)techniques to classify electrocardiogram(ECG)heartbeats,comparing traditional feature-based ML methods with innovative image-based approaches.The dataset underwent rigorous preprocessing,including down-sampling,frequency filtering,beat segmentation,and normalization.Two methodologies were explored:(1)handcrafted feature extraction,utilizing metrics like heart rate variability and RR distances with LightGBM classifiers,and(2)image transformation of ECG signals using Gramian Angular Field(GAF),Markov Transition Field(MTF),and Recurrence Plot(RP),enabling multimodal input for convolutional neural networks(CNNs).The Synthetic Minority Oversampling Technique(SMOTE)addressed data imbalance,significantly improving minority-class metrics.Results:The handcrafted feature approach achieved notable performance,with LightGBM excelling in precision and recall.Image-based classification further enhanced outcomes,with a custom Inception-based CNN,attaining an 85%F1 score and 97%accuracy using combined GAF,MTF,and RP transformations.Statistical analyses confirmed the significance of these improvements.Conclusion:This work highlights the potential of ML for cardiac irregularities detection,demonstrating that combining advanced preprocessing,feature engineering,and state-of-the-art neural networks can improve classification accuracy.These findings contribute to advancing AI-driven diagnostic tools,offering promising implications for cardiovascular healthcare.
基金University-level Scientific Research Project in Natural Sciences“Research on the Retrieval Method of Multimodal First-Class Course Teaching Content Based on Knowledge Graph Collaboration”(GKY-2024KYYBK-31)。
文摘In the context of digitalization,course resources exhibit multimodal characteristics,covering various forms such as text,images,and videos.Course knowledge and learning resources are becoming increasingly diverse,providing favorable conditions for students’in-depth and efficient learning.Against this backdrop,how to scientifically apply emerging technologies to automatically collect,process,and integrate digital learning resources such as voices,videos,and courseware texts,and better innovate the organization and presentation forms of course knowledge has become an important development direction for“artificial intelligence+education.”This article elaborates on the elements and characteristics of knowledge graphs,analyzes the construction steps of knowledge graphs,and explores the construction methods of multimodal course knowledge graphs from aspects such as dataset collection,course knowledge ontology identification,knowledge discovery,and association,providing references for the intelligent application of online open courses.
文摘With the popularization of social media,stickers have become an important tool for young students to express themselves and resist mainstream culture due to their unique visual and emotional expressiveness.Most existing studies focus on the negative impacts of spoof stickers,while paying insufficient attention to their positive functions.From the perspective of multimodal metaphor,this paper uses methods such as virtual ethnography and image-text analysis to clarify the connotation of stickers,understand the evolution of their digital dissemination forms,and explore the multiple functions of subcultural stickers in the social interactions between teachers and students.Young students use stickers to convey emotions and information.Their expressive function,social function,and cultural metaphor function progress in a progressive manner.This not only shapes students’values but also promotes self-expression and teacher-student interaction.It also reminds teachers to correct students’negative thoughts by using stickers,achieving the effect of“cultivating and influencing people through culture.”
基金supported by the National Natural Science Foundation of China(Nos.51375383).
文摘In this study,we present a small,integrated jumping-crawling robot capable of intermittent jumping and self-resetting.Compared to robots with a single mode of locomotion,this multi-modal robot exhibits enhanced obstacle-surmounting capabilities.To achieve this,the robot employs a novel combination of a jumping module and a crawling module.The jumping module features improved energy storage capacity and an active clutch.Within the constraints of structural robustness,the jumping module maximizes the explosive power of the linear spring by utilizing the mechanical advantage of a closed-loop mechanism and controls the energy flow of the jumping module through an active clutch mechanism.Furthermore,inspired by the limb movements of tortoises during crawling and self-righting,a single-degree-of-freedom spatial four-bar crawling mechanism was designed to enable crawling,steering,and resetting functions.To demonstrate its practicality,the integrated jumping-crawling robot was tested in a laboratory environment for functions such as jumping,crawling,self-resetting,and steering.Experimental results confirmed the feasibility of the proposed integrated jumping-crawling robot.
文摘Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate answer.In this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in Arabic.To support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the trade-o.between the model complexity and the overall model performance.Some fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA models.So far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no questions.Hence,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA systems.Experiments indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that a.ects the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model complexity.The Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.
基金supported by the National Natural Science Foundation of China(Nos.52377133 and 52077014)the Youth Talent Support Program of Chongqing(CQYC2021058945)the General Program of the Natural Science Foundation of Chongqing Municipality(CSTB2022NSCQ-MSX0444).
文摘To address the inherent trade-off between mechanical strength and repair efficiency in conventional microcapsule-based self-healing technologies,this study presents an eggshell-inspired approach for fabricating high-load rigid porous microcapsules(HLRPMs)through subcritical water etching.By optimizing the subcritical water treatment parameters(OH−concentration:0.031 mol/L,tem-perature:240°C,duration:1.5 h),nanoscale through-holes were generated on hollow glass microspheres(shell thickness≈700 nm).The subsequent gradient pressure infiltration of flaxseed oil enabled a record-high core content of 88.2%.Systematic investigations demonstrated that incorporating 3 wt%HLRPMs into epoxy resin composites preserved excellent dielectric properties(breakdown strength≥30 kV/mm)and enhanced tensile strength by 7.52%.In addressing multimodal damage,the system achieved a 95.5%filling efficiency for mechanical scratches,a 97.0%reduction in frictional damage depth,and a 96.2%recovery of insulation following electrical treeing.This biomimetic microcapsule system concurrently improved self-healing capability and matrix performance,offering a promising strategy for the development of next-generation smart insulating materials.
文摘Pedestrian trajectory prediction can significantly enhance the perception and decision-making capabilities of autonomous driving systems and intelligent surveillance systems based on camera sensors by predicting the states and behavior intentions of surrounding pedestrians.However,existing trajectory prediction methods remain failing to effectively model the diverse and complex interactions in the real world,including pedestrian-pedestrian interactions and pedestrian-environment interactions.Besides,these methods are not effective in capturing and characterizing the multimodal property of future trajectories.To address these challenges above,we propose to devise a handdesigned graph convolution and spatial cross attention to dynamically capture the diverse spatial interactions between pedestrians.To effectively explore the impact of scenarios on pedestrian trajectory,we build a pedestrian map,which can reflect the scene constraints and pedestrian motion preferences.Meanwhile,we construct a trajectory multimodality-aware module to capture the different potential mode implicit in diverse social behaviors for pedestrian future trajectory uncertainty.Finally,we compared the proposed method with trajectory prediction baselines on commonly used public pedestrian benchmarks,demonstrating the superior performance of our approach.
