Matching Paradigm Research Articles

CLIPMulti: Explore the performance of multimodal enhanced CLIP for zero-shot text classification

Zero-shot text classification does not require large amounts of labeled data and is designed to handle text classification tasks that lack annotated training data. Existing zero-shot text classification uses either a text-text matching paradigm or a text-image matching paradigm, which shows good performance on different benchmark datasets. However, the existing classification paradigms only consider a single modality for text matching, and little attention is paid to the help of multimodality for text classification. In order to incorporate multimodality into zero-shot text classification, we propose a multimodal enhanced CLIP framework (CLIPMulti), which employs a text-image&text matching paradigm to enhance the effectiveness of zero-shot text classification. Three different image and text combinations are tested for their effects on zero-shot text classification, and a matching method (Match-CLIPMulti) is further proposed to find the corresponding text based on the classified images automatically. We conducted experiments on seven publicly available zero-shot text classification datasets and achieved competitive performance. In addition, we analyzed the effect of different parameters on the Match-CLIPMulti experiments. We hope this work will bring more thoughts and explorations on multimodal fusion in language tasks.

Visual imagery vividness correlates with afterimage conscious perception.

Afterimages are illusory, visual conscious perceptions. A widely accepted theory is that afterimages are caused by retinal signaling that continues after the physical disappearance of a light stimulus. However, afterimages have been reported without preceding visual, sensory stimulation (e.g. conditioned afterimages and afterimages induced by illusory vision). These observations suggest the role of top-down brain mechanisms in afterimage conscious perception. Therefore, some afterimages may share perceptual features with sensory-independent conscious perceptions (e.g. imagery, hallucinations, and dreams) that occur without bottom-up sensory input. In the current investigation, we tested for a link between the vividness of visual imagery and afterimage conscious perception. Participants reported their vividness of visual imagery and perceived sharpness, contrast, and duration of negative afterimages. The afterimage perceptual features were acquired using perception matching paradigms that were validated on image stimuli. Relating these perceptual reports revealed that the vividness of visual imagery positively correlated with afterimage contrast and sharpness. These behavioral results support shared neural mechanisms between visual imagery and afterimages. However, we cannot exclude alternative explanations, including demand characteristics and afterimage perception reporting inaccuracy. This study encourages future research combining neurophysiology recording methods and afterimage paradigms to directly examine the neural mechanisms of afterimage conscious perception.

Creativity and Perception: Unveiling the Role of Cross‐Modal Audiovisual Integration

ABSTRACTMultisensory environments rich in modal integration provide cues from various sensory modalities including visually, auditorily, and tactilely. Such modal integration plays a crucial role in cognitive processing, specifically in fostering creativity. Numerous studies highlight that emotional coherence through cross‐modal affective integration enhances cognitive competencies such as memory, attention, and the capacity to generate original, fluent ideas. Nonetheless, current research lacks comparative studies pinpointing how different sensory modalities impact individual creativity. We addressed this research void by employing a cross‐model matching paradigm, anchored on the concept of emotional coherence. Our investigation evaluated the impact of varying emotional stimuli (both positive and negative) on creativity performance, considering single sensory modalities (visual and auditory), as well as their cross. Our study participants were 119 Chinese university students who completed a creative task under varying emotional stimulation modality conditions. We observed that the bimodal audiovisual integration of positive emotional stimuli most effectively enhanced creativity as compared to solely auditory modality. The visual modality seemed the least effective, underscoring the impact of multichannel integration. Interestingly, the bimodal audiovisual integration of negative emotional stimuli significantly boosted originality, albeit with little difference in fluency relative to the auditory modality. Based on the theoretical significance of multimodal emotional integration, our interpretation of these findings suggests that audiovisual cross‐modal emotional integration, with its rich emotional information, serves as a catalyst for enhancing originality. However, we observed that the facilitative effect on overall creativity and fluency is primarily observed in the context of positive emotions. It is important to note that we carefully controlled for participants' creative personality factors and conducted three types of emotional evaluations across modalities to ensure the validity of our results. Furthermore, the impact of cross‐modal audiovisual emotional integration on creativity, both in terms of originality and fluency, is influenced by the distinct emotional perceptual characteristics inherent to each modality.

On the Practical Power of Automata in Pattern Matching

Many papers in the intersection of theoretical and applied algorithms show that the simple, asymptotically less efficient algorithm, performs better than the bestcomplex theoretical algorithms on random data or in specialized “real world” applications. This paper considers the Knuth–Morris–Pratt automaton, and shows a counter-intuitive practical result. The classical pattern matching paradigm is that of seeking occurrences of one string—the pattern, in another—the text, where both strings are drawn from an alphabet set Σ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Sigma$$\\end{document}. Assuming the text length is n and the pattern length is m, this problem can naively be solved in time O(nm). In Knuth, Morris and Pratt’s seminal paper of 1977, an automaton, was developed that allows solving this problem in time O(n) for any alphabet. This automaton, which we will refer to as the KMP-automaton, has proven useful in solving many other problems. A notable example is the parameterized pattern matching model. In this model, a consistent renaming of symbols from Σ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Sigma$$\\end{document} is allowed in a match. The parameterized matching paradigm has proven useful in problems in software engineering, computer vision, and other applications. It has long been believed that for texts where the symbols are uniformly random, the naive algorithm will perform as well as the KMP algorithm. In this paper, we examine the practical efficiency of the KMP algorithm versus the naive algorithm on a randomly generated text. We analyze the time under various parameters, such as alphabet size, pattern length, and the distribution of pattern occurrences in the text. We do this for both the original exact matching problem and parameterized matching. While the folklore wisdom is vindicated by these findings for the exact matching case, surprisingly, the KMP algorithm always works significantly faster than the naive in the parameterized matching case. We check this hypothesis for DNA texts and image data and observe a similar behavior as in the random text. We also show a very structured exact matching case where the automaton is much more efficient.

MiC: Image-text Matching in Circles with cross-modal generative knowledge enhancement

Image-text matching is a challenging task due to vast discrepancies between the visual and textual modalities. Existing solutions tend to focus on a limited set of strongly aligned or misaligned elements, failing to fully bridge the inherent gap between two modalities. In this work, we introduce a collaborative framework that enables simultaneous learning of the matching task alongside two generative processes designed to generate textual and visual knowledge from cross-modal inputs. In such a way, the overall cross-modal consistency is estimated in a dual-view manner by estimating the similarities not only between the original textual and visual data (cross-modal), but also across the input and the generative knowledge (uni-modal). With the incorporation of generative components, the entire matching pipeline forms two closed circles, introducing a novel paradigm of Matching in Circles (MiC). Comprehensive experiments on two public datasets (Flickr30K and MS-COCO) demonstrate the superiority of the proposed method in both image-to-text and text-to-image retrieval scenarios.

Large Scale Ontology Matching System (LSMatch)

Background: Ontology matching provides a solution to the semantic heterogeneity problem by finding semantic relationships between entities of ontologies. Over the last two decades, there has been considerable development and improvement in the ontology matching paradigm. More than 50 ontology matching systems have been developed, and some of them are performing really well. However, the initial rate of improvement was measurably high, which now is slowing down. However, there still is room for improvement, which we as a community can work towards to achieve. Method: In this light, we have developed a Large Scale Ontology Matching System (LSMatch), which uses different matchers to find similarities between concepts of two ontologies. LSMatch mainly uses two modules for matching. These modules perform string similarity and synonyms matching on the concepts of the ontologies. Results: For the evaluation of LSMatch, we have tested it in Ontology Alignment Evaluation Initiative (OAEI) 2021. The performance results show that LSMatch can perform matching operations on large ontologies. LSMatch was evaluated on anatomy, disease and phenotype, conference, Knowledge graph, and Common Knowledge Graphs (KG) track. In all of these tracks, LSMatch’s performance was at par with other systems. Conclusion: Being LSMatch’s first participation, the system showed potential and has room for improvement.

Pixel-Perfect Structure-From-Motion With Featuremetric Refinement.

Finding local features that are repeatable across multiple views is a cornerstone of sparse 3D reconstruction. The classical image matching paradigm detects keypoints per-image once and for all, which can yield poorly-localized features and propagate large errors to the final geometry. In this paper, we refine two key steps of structure-from-motion by a direct alignment of low-level image information from multiple views: we first adjust the initial keypoint locations prior to any geometric estimation, and subsequently refine points and camera poses as a post-processing. This refinement is robust to large detection noise and appearance changes, as it optimizes a featuremetric error based on dense features predicted by a neural network. This significantly improves the accuracy of camera poses and scene geometry for a wide range of keypoint detectors, challenging viewing conditions, and off-the-shelf deep features. Our system easily scales to large image collections, enabling pixel-perfect crowd-sourced localization at scale. Our code is publicly available athttps://github.com/cvg/pixel-perfect-sfm as an add-on to the popular Structure-from-Motion software COLMAP.

Seeing both sides: context-aware heterogeneous graph matching networks for extracting-related arguments

Our research focuses on extracting exchanged views from dialogical documents through argument pair extraction (APE). The objective of this process is to facilitate comprehension of complex argumentative discourse by finding the related arguments. The APE comprises two stages: argument mining and argument matching. Researchers typically employ sequence labeling models for mining arguments and text matching models to calculate the relationships between them, thereby generating argument pairs. However, these approaches fail to capture long-distance contextual information and struggle to fully comprehend the complex structure of arguments. In our work, we propose the context-aware heterogeneous graph matching (HGMN) model for the APE task. First, we design a graph schema specifically tailored to argumentative texts, along with a heterogeneous graph attention network that effectively captures context information and structural information of arguments. Moreover, the text matching between arguments is converted into a graph matching paradigm and a multi-granularity graph matching model is proposed to handle the intricate relationships between arguments at various levels of granularity. In this way, the semantics of argument are modeled structurally and thus capture the complicated correlations between arguments. Extensive experiments are conducted to evaluate the HGMN model, including comparisons with existing methods and the GPT series of large language models (LLM). The results demonstrate that HGMN outperforms the state-of-the-art method.

A simple recommendation for the analysis of matching data.

The matching paradigm can take a number of forms and has been used in many areas of psychology. When participants are asked to match or order sets of objects, researchers must correctly account for the number of matches expected purely by chance. Not accounting for the expected chance matches can lead to incorrectly drawing conclusions based on one's data. This study demonstrated that the z test can be an appropriate and easy test to use in the analysis of matching data from studies that require pairs of objects to be matched with each other. This article proves that in a matching paradigm the expected number of chance matches is 1.0 and the associated variance is also 1.0. The z test is shown to maintain the Type I error close to the nominal significance level when the null hypothesis is true and the sample size is at least 80 or 110. To attain power of .80, a sample size larger than 80 may be needed depending upon the effect size associated with the area of interest and the hypothesized alternative probability distribution. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Evidence accumulation is not essential for generating intertemporal preference: A comparison of dynamic cognitive models of matching tasks

Intertemporal preference has been investigated mainly with a choice paradigm. However, a matching paradigm might be more informative for a proper inference about intertemporal preference and a deep understanding of the underlying cognitive mechanisms. This research involved two empirical studies using the matching paradigm and compared various corresponding dynamic models. These models were developed under either the framework of decision field theory, an exemplar theory assuming evidence accumulation, or a non-evidence-accumulation framework built upon the well-established notions of random utility and discrimination threshold (i.e., the RUDT framework). Most of these models were alternative-based whereas the others were attribute-based ones. Participants in Study 1 were required to fill in the amount of an immediate stimulus to make it as attractive as a delayed stimulus, whereas those in Study 2 needed to accomplish a more general matching task in which either the payoff amount or delay length of one stimulus was missing. Consistent behavioral regularities regarding both matching values and response times were revealed in these studies. The results of model comparison favored in general the RUDT framework as well as an attribute-based perspective on intertemporal preference. In addition, the predicted matching values and response times of the best RUDT model were also highly correlated with the observed data and replicated most observed behavioral regularities. Together, this research and previous modeling work on intertemporal choice suggest that evidence accumulation is not essential for generating intertemporal preference. Future research should examine the validity of the new framework in other preferential decisions for a more stringent test of the framework.

On learning distribution alignment for video-based visible-infrared person re-identification

This paper studies the matching problem of cross-modality video data from a discrete distribution alignment view. Central to this discussion is the visible-infrared person re-identification (VI-reID), a crucial feature that bolsters surveillance systems’ efficacy in monitoring individuals across diverse lighting conditions. Going beyond traditional image-to-image matching paradigms, a recent study shows that temporal information can bring richer cues to encode the pedestrian representation, improving the representation power of deep neural networks. However, this integration further complicates cross-modality data matching due to the joint processing of spatial and temporal information. This paper formulates the video data as a discrete distribution and aligns the cross-modality video representation by reducing the matching cost between the two distributions. To this end, a natural idea for aligning the videos is to reduce the divergence of distributions. Moreover, the powerful optimal transport (OT) scheme, which generates the optimal matching flows and establishes the relevance of two distributions, is also employed as a way to measure the distance of distributions. Nevertheless, we observe that endowing the OT in the advanced VI-reID feature extractor leads to a non-symmetric measurement. To mitigate this, the paper introduces a new metric, namely symmetric optimal transport (SOT), reformulating OT into a symmetric form. Thorough analyses and empirical studies affirm the superiority of the proposed SOT, which significantly outperforms the current state-of-the-art methods according to standard benchmarking evaluations.

A transfer effect of affect: evidence from episodic simulation of moral scenario

ABSTRACT Based on a scenario-place name matching paradigm, the objective of the study was to explore the transfer of affective value under the episodic simulation of moral or immoral scenarios. Experiment 1 revealed that people’s affective attitude towards neutral places matched with moral events will become more positive after episodic simulation, while those matched with immoral events will become more negative. In Experiment 2, we added a new condition—natural reading—and replicated these findings. Besides, we found the different function between episodic simulation and reading in the transfer of negative affect which may verify that imaginative resistance could be as an explanation for the transfer of negative affect under the simulation. The results from the two experiments indicate that episodic simulation indeed induces the transfer of affective value. Therefore, our findings suggest that episodic simulation induces the transfer of affective value to neutral elements in a real-life environment.

The Role of the Dorsolateral Prefrontal Cortex in the Production and Comprehension of Phonologically and Semantically Related Words.

Previous studies suggest that producing and comprehending semantically related words relies on inhibitory control over competitive lexical selection which results in the recruitment of the left inferior frontal gyrus (IFG). Few studies, however, have examined the involvement of other regions of the frontal cortex, such as the dorsolateral prefrontal cortex (DLPFC), despite its role in cognitive control related to lexical processing. The primary objective of this study was to elucidate the role of the DLPFC in the production and comprehension of semantically and phonologically related words in blocked cyclic naming and picture-word matching paradigms. Twenty-one adults participated in neuroimaging with functional near-infrared spectroscopy to measure changes in oxygenated and deoxygenated hemoglobin concentrations across the bilateral frontal cortex during blocked cyclic picture naming and blocked cyclic picture-word-matching tasks. After preprocessing, oxygenated and deoxygenated hemoglobin concentrations were obtained for each task (production, comprehension), condition (semantic, phonological) and region (DLPFC, IFG). The results of pairwise t-tests adjusted for multiple comparisons showed significant increases in oxygenated hemoglobin concentration over baseline in the bilateral DLPFC during picture naming for phonologically related words. For picture-word matching, we found significant increases in oxygenated hemoglobin concentration over baseline in the right DLPFC for semantically related words and in the right IFG for phonologically related words. We discuss the results in light of the inhibitory attentional control over competitive lexical access theory in contrast to alternative potential explanations for the findings.

Fast Continuous Subgraph Matching over Streaming Graphs via Backtracking Reduction

Streaming graphs are drawing increasing attention in both academic and industrial communities as many graphs in real applications evolve over time. Continuous subgraph matching (shorted as CSM) aims to report the incremental matches of a query graph in such streaming graphs. It involves two major steps, i.e., candidate maintenance and incremental match generation, to answer CSM. Throughout the course of continuous subgraph matching, incremental match generation backtracking over the search space dominates the total cost. However, most previous approaches focus on developing techniques for efficient candidate maintenance, while incremental match generation receives less attention despite its importance in CSM. Aiming to minimize the overall cost, we propose two techniques to reduce backtrackings in this paper. We present a cost-effective index CaLiG that yields tighter candidate maintenance, shrinking the search space of backtracking. In addition, we develop a novel incremental matching paradigm KSS that decomposes the query vertices into conditional kernel vertices and shell vertices. With the matches of kernel vertices, the incremental matches can be produced immediately by joining the candidates of shell vertices without any backtrackings. Benefiting from reduced backtrackings, the elapsed time of CSM decreases significantly. Extensive experiments over real graphs show that our method runs faster than the state-of-the-art algorithm orders of magnitude.

A Siamese Transformer Network for Zero-Shot Ancient Coin Classification.

Ancient numismatics, the study of ancient coins, has in recent years become an attractive domain for the application of computer vision and machine learning. Though rich in research problems, the predominant focus in this area to date has been on the task of attributing a coin from an image, that is of identifying its issue. This may be considered the cardinal problem in the field and it continues to challenge automatic methods. In the present paper, we address a number of limitations of previous work. Firstly, the existing methods approach the problem as a classification task. As such, they are unable to deal with classes with no or few exemplars (which would be most, given over 50,000 issues of Roman Imperial coins alone), and require retraining when exemplars of a new class become available. Hence, rather than seeking to learn a representation that distinguishes a particular class from all the others, herein we seek a representation that is overall best at distinguishing classes from one another, thus relinquishing the demand for exemplars of any specific class. This leads to our adoption of the paradigm of pairwise coin matching by issue, rather than the usual classification paradigm, and the specific solution we propose in the form of a Siamese neural network. Furthermore, while adopting deep learning, motivated by its successes in the field and its unchallenged superiority over classical computer vision approaches, we also seek to leverage the advantages that transformers have over the previously employed convolutional neural networks, and in particular their non-local attention mechanisms, which ought to be particularly useful in ancient coin analysis by associating semantically but not visually related distal elements of a coin's design. Evaluated on a large data corpus of 14,820 images and 7605 issues, using transfer learning and only a small training set of 542 images of 24 issues, our Double Siamese ViT model is shown to surpass the state of the art by a large margin, achieving an overall accuracy of 81%. Moreover, our further investigation of the results shows that the majority of the method's errors are unrelated to the intrinsic aspects of the algorithm itself, but are rather a consequence of unclean data, which is a problem that can be easily addressed in practice by simple pre-processing and quality checking.

Beyond the N400: Confirmed and Disconfirmed Lexical Predictions in Aging

One of the basic tenets of predictive theories of language processing is that of misprediction cost. Post-N400 positive event-related potential (ERP) components are suitable for studying misprediction cost, but are not adequately described, especially in older adults, who show attenuated N400 ERP effects. We report a secondary analysis of a previously collected EEG data set. Using a picture–word matching paradigm, in which the target word could sometimes be predicted by context and sometimes not, the relationship between aging and late ERP effects was examined. Positivity effects for prediction violations were larger and more extended in older compared with younger adults, independent of context. Younger adults showed frontal negativity effects, indicating frame-shifting processes. Our data suggest that aging does not entail specific declines in prediction. Rather, they indicate that older adults may experience more processing difficulties than younger adults when their expectations are disconfirmed and that multiple mechanisms may be employed during language processing.

On the Source of U-Shape Learning in Interlanguage Processing: A Pilot Study on the L2 Acquisition of Number Agreement in French by Jordanian Arabic Learners

This paper investigates the L2 acquisition of the morphosyntax of number agreement of French by L1 learners. The study is based on a pilot study that investigates learners’ knowledge of number agreement of two paradigms of subject-verb number agreement in French: the so-called matching paradigm in which agreement in number is expressed audibly via an alternation of the verb stem between a singular and plural forms and the mismatching paradigm in which the number agreement is not audible realized via singular vs. plural alternation. We discussed our sample’s proficiency in (de-)normalizing number agreement and the implications it has for the theory of corrective feedback and linguistic input in L2 processing in Language acquisition.

A Computational Meta-Learning Inspired Model for Sketch-based Video Retrieval

Objectives: To design and develop an efficient computing framework for sketch-based video retrieval using fine-grained intrinsic computational approach. Methods: The primary method of sketch-based video retrieval adopts multi-stream multi-modality of joint embedding method for improved P-SBVR from improved fine-grained KTH and TSF related dataset. It considers the potential aspects of the computation of significant visual intrinsic appearance details for sketch objects. The extracted appearance and motion-based features are used to train three different CNN baselines under strong and weak supervision. The system also implements a meta-learning model for different supervised settings to attain better performance of sketch-based video retrieval along with a relational module to overcome the problem of overfitting. Findings: The study derives specific sketch sequences from its formulated dataset to compute instance-level query processing for video retrieval. Further, it also addresses the limitations arising in the context of coarse-grained video retrieval models and sketch-based still image retrieval. The aggregated dataset for rich annotation assisted in the experimental simulation. The experimental evaluation with respect to the performance metric evaluates the 3D CNN baselines under strong supervision and weak-supervision where CNN BL-Type-2 attains maximum video retrieval accuracy of 99.96% for triplet grading feature under relational schema. CNN BL-Type-1 attains maximum retrieval accuracy of 97.40% considering the triplet grading features from the improved SBVR. The evaluation metric for the instance level retrieval process also considers true matching of sketches with the videos, it clearly shows that the appropriate appearance and motion based feature selection has enhanced the video retrieval accuracy up to 96.90% with 99.28% accuracy in action identification considering motion stream, 98.17% for appearance module and 98.45% for fusion module. Another important aspect of the proposed research context is that it addresses the problem of cross-modality while executing the simultaneous matching paradigm for visual appearances of the object with its movement appearing on particular video scenes. The experimental outcome showsits comparable effectiveness relative to the existing system of CNN. Novelty: Unlike the conventional system of sketch analysis, which is more focused on static objects or scenes, the presented approach can efficiently compute the important visual intrinsic appearance details of the object of interest from the sketch and then activate the operations for video retrieval. The proposed CNN based learning model with improved P-SBVR dataset attains better computing time for retrieval with are approximately (200, 210 and 214) milliseconds for CNN BL-Type-1, CNN BL-Type-2, CNN BL-Type-3 and comparable with the existing deep learning based SBVR models. Keywords: Sketch Based Video Retrieval; Intrinsic Appearance Details; Meta Learning; Sketch Dataset; Cross Modality Problem

Where word and world meet: Language and vision share an abstract representation of symmetry.

Symmetry is ubiquitous in nature, in logic and mathematics, and in perception, language, and thought. Although humans are exquisitely sensitive to visual symmetry (e.g., of a butterfly), symmetry in natural language goes beyond visuospatial properties: many words point to abstract concepts with symmetrical content (e.g., equal, marry). For example, if Mark marries Bill, then Bill marries Mark. In both cases (vision and language), symmetry may be formally characterized as invariance under transformation. Is this a coincidence, or is there some deeper psychological resemblance? Here we asked whether representations of symmetry correspond across language and vision. To do so, we developed a novel cross-modal matching paradigm. On each trial, participants observed a visual stimulus (either symmetrical or nonsymmetrical) and had to choose between a symmetrical and nonsymmetrical English predicate unrelated to the stimulus (e.g., "negotiate" vs. "propose"). In a first study with visual events (symmetrical collision or asymmetrical launch), participants reliably chose the predicate matching the event's symmetry. A second study showed that this "language-vision correspondence" generalized to objects and was weakened when the stimuli's binary nature was made less apparent (i.e., for one object, rather than two inward-facing objects). A final study showed the same effect when nonsigners guessed English translations of signs from American Sign Language, which expresses many symmetrical concepts spatially. Taken together, our findings support the existence of an abstract representation of symmetry which humans access via both perceptual and linguistic means. More broadly, this work sheds light on the rich, structured nature of the language-cognition interface. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Long-Term Matching Optimization With Federated Neural Temporal Difference Learning in Mobility-on-Demand Systems

Efficient bipartite graph matching of orders and drivers is a central operational problem in industrial mobility-on-demand (MOD) systems. Traditional studies adopt pure combinatorial optimization models for order-and-driver matching, which do not consider the long-term rewards of the dynamic MOD decision making process. Toward long-term optimization, this article presents a systemic paradigm of online matching with federated neural temporal difference learning (FedTDLearning), which encompasses learning and matching phases. During the learning phase, the long-term matching process is modeled as a Markov decision process, which is typically solved by employing data-driven reinforcement learning in an offline central training scheme. Massive amounts of data would be generated on the network by industrial MOD systems. It is impossible to send all the large-scale industrial data to the cloud server for centralized model training due to network bandwidth limitations and safety concerns. Therefore, a generic and innovative FedTDLearning is proposed to achieve long-term matching in a distributed manner. During the matching phase, a real-time bipartite matching optimization problem is formulated to maximize the learned spatiotemporal value and minimize the pickup distance, which is reducible to the minimum-cost maximum weight bipartite graph matching problem. A distance-learned-value ratio algorithm is proposed to find an optimal matching in the bipartite graph based on the joint optimization of FedTDLearning and the combinatorial fractional programming approach. Furthermore, to pursue optimal computation efficiency, we solve the real-time matching problem by constructing a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -nearest neighbor <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(k$ </tex-math></inline-formula> NN) bipartite graph where each order is connected with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> NN drivers. Using openly available real-world data, the prototype system and experimental evaluations show that our proposed algorithms have effective problem-solving capability in practice.