False Matches Research Articles

A method for long car-following pair extraction and comprehensive data quality assessment: a case study using Zen Traffic Data

ABSTRACT This paper introduces a car-following (CF) extraction algorithm to address challenges in aerial-based trajectory data extraction. The algorithm, comprising four steps – vehicle grouping, elimination of false overtaking behavior, vehicle sorting, and CF pair matching – was applied to Zen Traffic Data, extracting 246 CF pairs. Three datasets were then generated: kilopost-based, geography-based, and velocity-based. A quality analysis revealed significant inconsistencies between data fields, with the geography-based dataset being least affected by high-frequency noise. The extracted CF data also demonstrated a more comprehensive driving regime than NGSIM, with complete driving regimes identified. Furthermore, the impact of data noise on CF model calibration and heterogeneity analysis was thoroughly assessed. This study enhances our understanding of trajectory data quality and highlights the richness of driving behavior information in Zen Traffic Data.

Assessment of Data-Independent Acquisition Mass Spectrometry (DIA-MS) for the Identification of Single Amino Acid Variants

Proteogenomics integrates genomic and proteomic data to elucidate cellular processes by identifying variant peptides, including single amino acid variants (SAAVs). In this study, we assessed the capability of data-independent acquisition mass spectrometry (DIA-MS) to identify SAAV peptides in HeLa cells using various search engine pipelines. We developed a customised sequence database (DB) incorporating SAAV sequences from the HeLa genome and conducted searches using DIA-NN, Spectronaut, and Fragpipe-MSFragger. Our evaluation focused on identifying true positive SAAV peptides and false positives through entrapment DBs. This study revealed that DIA-MS provides reproducible and comprehensive coverage of the proteome, identifying a substantial proportion of SAAV peptides. Notably, the DIA-MS searches maintained consistent identification of SAAV peptides despite varying sizes of the entrapment DB. A comparative analysis showed that Fragpipe-MSFragger (FP-DIA) demonstrated the most conservative and effective performance, exhibiting the lowest false discovery match ratio (FDMR). Additionally, integrating DIA and data-dependent acquisition (DDA) MS data search outputs enhanced SAAV peptide identification, with a lower false discovery rate (FDR) observed in DDA searches. The validation using stable isotope dilution and parallel reaction monitoring (SID-PRM) confirmed the SAAV peptides identified by DIA-MS and DDA-MS searches, highlighting the reliability of our approach. Our findings underscore the effectiveness of DIA-MS in proteogenomic workflows for identifying SAAV peptides, offering insights into optimising search engine pipelines and DB construction for accurate proteomics analysis. These methodologies advance the understanding of proteome variability, contributing to cancer research and the identification of novel proteoform therapeutic targets.

Speaker Recognition System Using Hybrid of MFCC and RCNN with HCO Algorithm Optimization

Though there are advancements in speaker recognition technology, available systems often fail to correctly recognize speakers especially in noisy environments. The use of Mel-frequency cepstral coefficients (MFCC) has been improved using Convolutional Neural Networks (CNN) yet difficulties in achieving high accuracies still exists. Hybrid algorithms combining MFCC and Region-based Convolutional Neural Networks (RCNN) have been found to be promising. In this research features from speech signals were extracted for speaker recognition, to denoise the signals, design and develop a DFT-based denoising system using spectrum subtraction and to develop a speaker recognition method for the Verbatim Transcription using MFCC. The DFT was used to transform the sampled audio signal waveform into a frequency-domain signal. RCNN was used to model the characteristics of speakers based on their voice samples, and to classify them into different categories or identities. The novelty of the research was that it used MFCC integrated with RCNN and optimized with Host-Cuckoo Optimization (HCO) algorithm. HCO algorithm is capable of further weight optimization through the process of generating fit cuckoos for best weights. It also captured the temporal dependencies and long-term information. The system was tested and validated on audio recordings from different personalities from the National Assembly of Kenya. The results were compared with the actual identity of the speakers to confirm accuracy. The performance of the proposed approach was compared with two other existing speaker recognition the traditional approaches being MFCC-CNN and Linear Predictive Coefficients (LPC)-CNN. The comparison was based the Equal Error Rate (EER), False Rejection Rate (FRR), False Match Rate (FMR), and True Match Rate (TMR). Results show that the proposed algorithm outperformed the others in maintaining a lowest EER, FMR, FRR and highest TMR.

Adaptive Quotient Filters

Filters trade off accuracy for space and occasionally return false positive matches with a bounded error. Numerous systems use filters in fast memory to avoid performing expensive I/Os to slow storage. A fundamental limitation in traditional filters is that they do not change their representation upon seeing a false positive match. Therefore, the maximum false positive rate is only guaranteed for a single query, not for an arbitrary set of queries. We can improve the filter's performance on a stream of queries, especially on a skewed distribution, if we can adapt after encountering false positives. Adaptive filters, such as telescoping quotient filters and adaptive cuckoo filters, update their representation upon detecting a false positive to avoid repeating the same error in the future. Adaptive filters require an auxiliary structure, typically much larger than the main filter and often residing on slow storage, to facilitate adaptation. However, existing adaptive filters are not practical and have not been adopted in real-world systems for two main reasons. First, they offer weak adaptivity guarantees, meaning that fixing a new false positive can cause a previously fixed false positive to come back. Secondly, the sub-optimal design of the auxiliary structure results in adaptivity overheads so substantial that they can actually diminish overall system performance compared to a traditional filter. In this paper, we design and implement the \sysname, the first practical adaptive filter with minimal adaptivity overhead and strong adaptivity guarantees, which means that the performance and false-positive guarantees continue to hold even for adversarial workloads. The \sysname is based on the state-of-the-art quotient filter design and preserves all the critical features of the quotient filter such as cache efficiency and mergeability. Furthermore, we employ a new auxiliary structure design which results in considerably low adaptivity overhead and makes the \sysname practical in real systems. We evaluate the \sysname by using it to filter queries to an on-disk B-tree database and find no negative impact on insert or query performance compared to traditional filters. Against adversarial workloads, the \sysname preserves system performance, whereas traditional filters incur 2× slowdown from adversaries representing as low as 1% of the workload. Finally, we show that on skewed query workloads, the \sysname can reduce the false-positive rate 100× using negligible (1/1000th of a bit per item) space overhead.

Leveraging Machine Learning to Combat Missingness and Error in Data

ObjectivePoor quality data confounds efforts to link clients across datasets. To combat this, we have trialed an approach which seeks to identify linkage candidates using associations with related services. We utilised a machine learning (ML) approach to query linkage candidates’ dataset associations and make predictions about whether candidates are likely to be a client of the service being integrated for linkage. ApproachUtilizing a K-nearest neighbors algorithm, we trained a model using 15 variables to predict whether a linkage candidate is a client of a family service dataset. We then evaluated the model's success, and modified its native performance to minimise false positive matches. Subsequently, we tested the validity of these predictions as linkage criteria by employing blocking strategies and linking the service dataset to our linkage spine. The trained model was then used to identify correct links against the spine. ResultsThe evaluation of the machine learning model yielded promising results, with high accuracy (88.5%) and precision (95.5%). Testing the predictions as linkage criteria resulted in highly accurate links ranging from 96.0% to 98.7% across different blocking strategies. Despite some records failing to establish any links to the spine, rates of false positive matches remained low (0.7% to 3.1%). ConclusionMissingness and inaccuracy in data remains a key problem for data linkage, and a robust approach is required to resolve complex linkage cases. However, these findings suggest that machine learning can present novel options for a toolbox of many approaches to link problem records to a linkage spine.

Generating synthetic identifiers to support development and evaluation of data linkage methods

We aimed to develop a framework for generating synthetic identifier datasets to support development and evaluation of data linkage methods. We evaluated whether replicating associations between attributes and identifiers improved the utility of the synthetic data for assessing linkage error. We determined the steps required to generate synthetic identifiers that replicate the properties of real-world data collection. We generated synthetic versions of a large United Kingdom cohort study (the Avon Longitudinal Study of Parents and Children), according to the quality and completeness of identifiers recorded over several waves of the cohort. We evaluated the utility of the synthetic identifier data in terms of assessing linkage quality (false matches and missed matches). Comparing data from two collection points in ALSPAC, we found within-person disagreement in identifiers (differences in recording due to both natural change and non-valid entries) in 18% of surnames and 12% of forenames. Rates of disagreement varied by maternal age and ethnic group. Synthetic data provided accurate estimates of linkage quality metrics compared with the original data (within 0.13-0.55% for missed matches and 0.00-0.04% for false matches). Incorporating associations between identifier errors and maternal age/ethnicity improved synthetic data utility. Replicating dependencies between attribute values (e.g. ethnicity), values of identifiers (e.g. name), identifier disagreements (e.g. missing values, errors or changes over time), and their patterns and distribution structure enables generation of realistic synthetic data that can be used for robust evaluation of linkage methods. Our framework provides a novel and generalisable mechanism for developing and benchmarking record linkage algorithms.

Linking Education Data in Wales (UK) – Methodology and Lessons Learned

There are separate data collections across education phases in Wales (UK). Each use a different unique learner identifier. The matched education dataset project used advanced data linking methods to produce a set of pseudo identifiers for each learner that can be matched back to the original datasets to undertake specific, de-identified analysis. The first phase of the project involved data cleaning, preparation, and the creation of new derived variables. The second phase established the linking methodology, developing and advanced data linking techniques and algorithms including frequency matching and phonetic string comparators. At each stage of the project the data linking was sequential, ranging from exact to more fuzzy matching. During the code development the approach was uniquely tailored to each data set and constantly fine-tuned to ensure the highest possible match rate while reducing potential for false matches. The resulting linked education data sets are used in a number of ways for statistical and research purposes to support the formation of evidence-based policies. This has included research into raising the compulsory education age and the evaluation of learner journeys during the pandemic. Robust linked data facilitates analysis examining the progression of learners through the education system in Wales. There is a broad scope of future analysis planned and the matched education outputs will be used extensively in the evaluation of learner journeys post pandemic to inform Welsh Government policy. The matched education dataset project involved researching and upskilling in data linkage methodology, we hope to disseminate the lessons learned widely.

基于扫描上下文和 NDT-ICP 融合方案的果园同步定位与绘图方法研究

Simultaneous localization and mapping (SLAM) is one of the key technologies for agricultural robots to build maps and localize in complex orchard environments and realize unmanned autonomous operations. Due to the complexity of the orchard environment, the single canopy feature and the diffuse reflection of light caused by the leaves, etc., the map construction process of the orchard environment leads to mismatch and increases the cumulative error of the map construction. Aiming at the above problems, this paper propose a navigation map construction method for orchard environment based on the fusion of Scan Context and NDT-ICP. The method firstly searches the Ring key quickly to get the candidate frames, and scores the similarity between the candidate frames and the current frame, and effectively detects the loopbacks by two-stage searching algorithm to reduce the false matches in the map of orchard environment. Meanwhile, a point cloud alignment method based on the fusion of normal distribution transform coarse alignment and iterative nearest point exact alignment is used to reduce the cumulative error of the orchard environment map. The results show that the improved algorithm compensates the drift of the point cloud map with higher mapping accuracy, better real-time performance, lower resource utilization, higher overlap between the trajectory estimation and the real trajectory, smoother loops, and a 4% reduction in CPU occupancy. In the complex orchard environment, the root mean square error and standard deviation of the trajectories of this paper's algorithm are 0.57 m and 0.19 m, which are 68% and 83% higher than those of the loop detection algorithms in the Lightweight Ground Optimized Lidar Trajectory Measurement and Multivariate Terrain Mapping (LeGO-LOAM), respectively. Accurate map construction and low drift pose estimation can be performed.The research algorithm effectively reduces the influence of mis-matching and large cumulative error in the process of map construction in the orchard environment, meets the demand for high-precision environmental mapping in the orchard environment, and provides technical support for promoting unmanned operation in the orchard environment.

A general framework for regression with mismatched data based on mixture modelling

Abstract The advent of the information age has revolutionized data collection and has led to a rapid expansion of available data sources. Methods of data integration are indispensable when a question of interest cannot be addressed using a single data source. Record linkage (RL) is at the forefront of such data integration efforts. Incentives for sharing linked data for secondary analysis have prompted the need for methodology accounting for possible errors at the RL stage. Mismatch error is a common consequence resulting from the use of nonunique or noisy identifiers at that stage. In this paper, we present a framework to enable valid postlinkage inference in the secondary analysis setting in which only the linked file is given. The proposed framework covers a variety of statistical models and can flexibly incorporate information about the underlying RL process. We propose a mixture model for linked records whose two components reflect distributions conditional on match status, i.e. correct or false match. Regarding inference, we develop a method based on composite likelihood and the expectation-maximization algorithm that is implemented in the R package pldamixture. Extensive simulations and case studies involving contemporary RL applications corroborate the effectiveness of our framework.

DNA sequences alignment method using sparse index on pan-genome graph.

The graph of sequences represents the genetic variations of pan-genome concisely and space-efficiently than multiple linear reference genome. In order to accelerate aligning reads to the graph, an index of graph-based reference genomes is used to obtain candidate locations. However, the potential combinatorial explosion of nodes on the sequence graph leads to increasing the index space and maximum memory usage of alignment process considerably, especially for large-scale datasets. For this, existing methods typically attempt to prune complex regions, or extend the length of seeds, which sacrifices the recall of alignment algorithm despite reducing space usage slightly. We present the Sparse-index of Graph (SIG) and alignment algorithm SIG-Aligner, capable of indexing and aligning at the lower memory cost. SIG builds the non-overlapping minimizers index inside nodes of sequence graph and SIG-Aligner filters out most of the false positive matches by the method based on the pigeonhole principle. Compared to Giraffe, the results of computational experiments show that SIG achieves a significant reduction in index memory space ranging from 50% to 75% for the human pan-genome graphs, while still preserving superior or comparable accuracy of alignment and the faster alignment time.

Evaluation of GLOCK 9 mm Firing Pin Aperture Shear Mark Individuality Based on 3,156 Different Pistols (Manufactured Over a 30 Year Period in Two Countries) Using Additional Pattern Matching and IBIS Pattern Recognition

Over a period of 30 years, a number of fired GLOCK cartridge cases have been evaluated. A total of 3156 GLOCK firearms were used to generate a sample of the same size. Our research hypothesis was that no cartridge cases fired from different 9 mm semiautomatic GLOCK pistols would be mistaken as coming from the same gun (a false match). Using optical comparison microscopy, two separate experiments were carried out to test this hypothesis. A subsample of 617 test-fired cartridge cases were subjected to algorithmic comparison by the Integrated Ballistics Identification System (IBIS). The second experiment subjected the full set of 3,156 cases to manual comparisons using traditional pattern matching. None of the cartridge cases were “matched” by either of these two experiments. Using these empirical findings, an established conservative Bayesian probability model was used to estimate the chance that a 9 mm cartridge case, fired from a GLOCK, could be mistaken as coming from the same firearm when in fact it did not (i.e., the false match probability).

Enhanced Strapdown Inertial Navigation System (SINS)/LiDAR Tightly Integrated Simultaneous Localization and Mapping (SLAM) for Urban Structural Feature Weaken Occasions in Vehicular Platform

LiDAR-based simultaneous localization and mapping (SLAM) offer robustness against illumination changes, but the inherent sparsity of LiDAR point clouds poses challenges for continuous tracking and navigation, especially in feature-deprived scenarios. This paper proposes a novel LiDAR/SINS tightly integrated SLAM algorithm designed to address the localization challenges in urban environments characterized in sparse structural features. Firstly, the method extracts edge points from the LiDAR point cloud using a traditional segmentation method and clusters them to form distinctive edge lines. Then, a rotation-invariant feature—line distance—is calculated based on the edge line properties that were inspired by the traditional tightly integrated navigation system. This line distance is utilized as the observation in a Kalman filter that is integrated into a tightly coupled LiDAR/SINS system. This system tracks the same edge lines across multiple frames for filtering and correction instead of tracking points or LiDAR odometry results. Meanwhile, for loop closure, the method modifies the common SCANCONTEXT algorithm by designating all bins that do not reach the maximum height as special loop keys, which reduce false matches. Finally, the experimental validation conducted in urban environments with sparse structural features demonstrated a 17% improvement in positioning accuracy when compared to the conventional point-based methods.

Generating synthetic identifiers to support development and evaluation of data linkage methods

IntroductionCareful development and evaluation of data linkage methods is limited by researcher access to personal identifiers. One solution is to generate synthetic identifiers, which do not pose equivalent privacy concerns, but can form a 'gold-standard' linkage algorithm training dataset. Such data could help inform choices about appropriate linkage strategies in different settings. ObjectivesWe aimed to develop and demonstrate a framework for generating synthetic identifier datasets to support development and evaluation of data linkage methods. We evaluated whether replicating associations between attributes and identifiers improved the utility of the synthetic data for assessing linkage error. MethodsWe determined the steps required to generate synthetic identifiers that replicate the properties of real-world data collection. We then generated synthetic versions of a large UK cohort study (the Avon Longitudinal Study of Parents and Children; ALSPAC), according to the quality and completeness of identifiers recorded over several waves of the cohort. We evaluated the utility of the synthetic identifier data in terms of assessing linkage quality (false matches and missed matches). ResultsComparing data from two collection points in ALSPAC, we found within-person disagreement in identifiers (differences in recording due to both natural change and non-valid entries) in 18% of surnames and 12% of forenames. Rates of disagreement varied by maternal age and ethnic group. Synthetic data provided accurate estimates of linkage quality metrics compared with the original data (within 0.13-0.55% for missed matches and 0.00-0.04% for false matches). Incorporating associations between identifier errors and maternal age/ethnicity improved synthetic data utility. ConclusionsWe show that replicating dependencies between attribute values (e.g. ethnicity), values of identifiers (e.g. name), identifier disagreements (e.g. missing values, errors or changes over time), and their patterns and distribution structure enables generation of realistic synthetic data that can be used for robust evaluation of linkage methods.

Enhancing Map Matching Accuracy Using Backtracking Technique

Map matching integrates positioning data with road network information to accurately identify a vehicle exact location on a specific road. Modern location-based services heavily rely on accurate map matching techniques to match observed trajectories with road networks. This paper presents a novel map matching approach that leverages topological information and integrates the backtracking technique to enhance the accuracy and robustness of map matching. By considering the logical relationships between road segments, our method achieves excellent results in scenarios with complex road geometries and challenging urban environments. We integrate the backtracking technique to address noise and ambiguity in trajectory data, resulting in improved precision and a reduced likelihood of false matches. Extensive experimentation using real-world trajectory datasets in Beijing validates the effectiveness of our approach, highlighting its superiority over traditional methods. The algorithm accuracy achieved as high as 99.92%. The results of the evaluation demonstrate that the proposed approach exhibits superior performance compared to earlier map-matching algorithms in both simple and complex road networks. Additionally, it can be effectively implemented on GPS datasets with different time intervals. In addition to the obvious map matching applications of our technology, it has the potential to be adapted across domains, with implications in transportation, urban planning, and location-based services. This paper not only presents a substantial advancement in map matching but also opens new avenues for location-based analyses enhancing and services across various domains.

An algorithm for decoy-free false discovery rate estimation in XL-MS/MS proteomics.

Cross-linking tandem mass spectrometry (XL-MS/MS) is an established analytical platform used to determine distance constraints between residues within a protein or from physically interacting proteins, thus improving our understanding of protein structure and function. To aid biological discovery with XL-MS/MS, it is essential that pairs of chemically linked peptides be accurately identified, a process that requires: (i) database search, that creates a ranked list of candidate peptide pairs for each experimental spectrum and (ii) false discovery rate (FDR) estimation, that determines the probability of a false match in a group of top-ranked peptide pairs with scores above a given threshold. Currently, the only available FDR estimation mechanism in XL-MS/MS is the target-decoy approach (TDA). However, despite its simplicity, TDA has both theoretical and practical limitations that impact the estimation accuracy and increase run time over potential decoy-free approaches (DFAs). We introduce a novel decoy-free framework for FDR estimation in XL-MS/MS. Our approach relies on multi-sample mixtures of skew normal distributions, where the latent components correspond to the scores of correct peptide pairs (both peptides identified correctly), partially incorrect peptide pairs (one peptide identified correctly, the other incorrectly), and incorrect peptide pairs (both peptides identified incorrectly). To learn these components, we exploit the score distributions of first- and second-ranked peptide-spectrum matches for each experimental spectrum and subsequently estimate FDR using a novel expectation-maximization algorithm with constraints. We evaluate the method on ten datasets and provide evidence that the proposed DFA is theoretically sound and a viable alternative to TDA owing to its good performance in terms of accuracy, variance of estimation, and run time. https://github.com/shawn-peng/xlms.

Improvement methods for feature matching in Basalt SLAM

Abstract Visual simultaneous localization and mapping (SLAM) is widely used to provide localization information for mobile robots, but most visual SLAM algorithms lack effective data association methods, resulting in unstable position tracking results. In this paper, relevant improvement schemes for global feature matching in the Basalt SLAM algorithm are proposed to enhance the effectiveness of its data association method. Firstly, the more efficient box average difference (BAD) descriptor is used instead of the ORB descriptor. Secondly, secondary matching is performed by narrowing the matching range with the epipolar geometric constraint to expand matching pairs. Finally, grid-based motion statistics (GMS) and histogram-based rotation consistency constraints are introduced to eliminate false matches. Experiments on the EuRoC dataset demonstrate that the proposed methods significantly enhance the effectiveness and stability of visual data association and improve the localization accuracy of the Basalt SLAM system.

A Robust Mismatch Removal Method for Image Matching Based on the Fusion of the Local Features and the Depth

Feature point matching is a fundamental task in computer vision such as vision simultaneous localization and mapping (VSLAM) and structure from motion (SFM). Due to the similarity or interference of features, mismatches are often unavoidable. Therefore, how to eliminate mismatches is important for robust matching. Smoothness constraint is widely used to remove mismatch, but it cannot effectively deal with the issue in the rapidly changing scene. In this paper, a novel LCS-SSM (Local Cell Statistics and Structural Similarity Measurement) mismatch removal method is proposed. LCS-SSM integrates the motion consistency and structural similarity of a local image block as the statistical likelihood of matched key points. Then, the Random Sampling Consensus (RANSAC) algorithm is employed to preserve the isolated matches that do not satisfy the statistical likelihood. Experimental and comparative results on the public dataset show that the proposed LCS-SSM can effectively and reliably differentiate true and false matches compared with state-of-the-art methods, and can be used for robust matching in scenes with fast motion, blurs, and clustered noise.

EMC+GD_C: circle-based enhanced motion consistency and guided diffusion feature matching for 3D reconstruction

Robust matching, especially the number, precision and distribution of feature point matching, directly affects the effect of 3D reconstruction. However, the existing methods rarely consider these three aspects comprehensively to improve the quality of feature matching, which in turn affects the effect of 3D reconstruction. Therefore, to effectively improve the quality of 3D reconstruction, we propose a circle-based enhanced motion consistency and guided diffusion feature matching algorithm for 3D reconstruction named EMC+GD_C. Firstly, a circle-based neighborhood division method is proposed, which increases the number of initial matching points. Secondly, to improve the precision of feature point matching, on the one hand, we put forward the idea of enhancing motion consistency, reducing the mismatch of high similarity feature points by enhancing the judgment conditions of true and false matching points; on the other hand, we combine the RANSAC optimization method to filter out the outliers and further improve the precision of feature point matching. Finally, a novel guided diffusion idea combining guided matching and motion consistency is proposed, which expands the distribution range of feature point matching and improves the stability of 3D models. Experiments on 8 sets of 908 pairs of images in the public 3D reconstruction datasets demonstrate that our method can achieve better matching performance and show stronger stability in 3D reconstruction. Specifically, EMC+GD_C achieves an average improvement of 24.07% compared to SIFT-based ratio test, 9.18% to GMS and 1.94% to EMC+GD_G in feature matching precision.

Asesmen Sumatif dalam Pembelajaran Bahasa Indonesia Fase D Kurikulum Merdeka

The aim of this research is to describe the implementation of summative assessment in the Indonesian language learning reading element phase D. Data collection uses observation, interviews and documentation. The object of this research is summative assessment in Indonesian language learning. The subjects of this research were Indonesian language teachers and class VIII students at SMP Muhammadiyah Surakarta. The data used is a summative assessment in the Indonesian language learning material for reading element phase D. The data source was obtained from the implementation of summative assessment in Indonesian language learning for reading element phase D class VIII at SMP Muhammadiyah Surakarta. Data analysis uses descriptive methods. The data analysis technique used includes three stages, namely 1. Data reduction; 2. Data display; 3. Drawing conclusion. The findings show that summative assessments are carried out at the end of every semester. Summative assessment is carried out at the end of the odd semester through the Final Semester Assessment (PAS). There are four types of questions in the Final Semester Assessment (PAS), namely multiple choice questions, true or false statements, matching, and essays. The total number of questions is 50 questions which are divided into several types of questions. There are 25 multiple choice questions, 10 true and false statements, 10 matching questions, and 5 essay questions.

MatchDet: A Collaborative Framework for Image Matching and Object Detection

Image matching and object detection are two fundamental and challenging tasks, while many related applications consider them two individual tasks (i.e. task-individual). In this paper, a collaborative framework called MatchDet (i.e. task-collaborative) is proposed for image matching and object detection to obtain mutual improvements. To achieve the collaborative learning of the two tasks, we propose three novel modules, including a Weighted Spatial Attention Module (WSAM) for Detector, and Weighted Attention Module (WAM) and Box Filter for Matcher. Specifically, the WSAM highlights the foreground regions of target image to benefit the subsequent detector, the WAM enhances the connection between the foreground regions of pair images to ensure high-quality matches, and Box Filter mitigates the impact of false matches. We evaluate the approaches on a new benchmark with two datasets called Warp-COCO and miniScanNet. Experimental results show our approaches are effective and achieve competitive improvements.