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Found 19 papers, 5 papers with code
Multiple Instance Learning for Cheating Detection and Localization in Online Examinations
However, cheating behavior is rare, and most researchers do not comprehensively take into account features such as head posture, gaze angle, body posture, and background information in the task of cheating behavior detection.
Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges
Anomaly analytics is a popular and vital task in various research contexts, which has been studied for several decades.
Smooth Non-Rigid Shape Matching via Effective Dirichlet Energy Optimization
We introduce pointwise map smoothness via the Dirichlet energy into the functional map pipeline, and propose an algorithm for optimizing it efficiently, which leads to high-quality results in challenging settings.
Learning to Construct 3D Building Wireframes from 3D Line Clouds
The network takes a line cloud as input , i. e., a nonstructural and unordered set of 3D line segments extracted from multi-view images, and outputs a 3D wireframe of the underlying building, which consists of a sparse set of 3D junctions connected by line segments.
Gaussian Blue Noise
Among the various approaches for producing point distributions with blue noise spectrum, we argue for an optimization framework using Gaussian kernels.
REALY: Rethinking the Evaluation of 3D Face Reconstruction
The evaluation of 3D face reconstruction results typically relies on a rigid shape alignment between the estimated 3D model and the ground-truth scan.
Deep Graph Learning for Anomalous Citation Detection
To address this open issue, we propose a novel deep graph learning model, namely GLAD (Graph Learning for Anomaly Detection), to identify anomalies in citation networks.
Web of Scholars: A Scholar Knowledge Graph
In this work, we demonstrate a novel system, namely Web of Scholars, which integrates state-of-the-art mining techniques to search, mine, and visualize complex networks behind scholars in the field of Computer Science.
Heterogeneous Graph Learning for Explainable Recommendation over Academic Networks
We devise an unsupervised learning model called HAI (Heterogeneous graph Attention InfoMax) which aggregates attention mechanism and mutual information for institution recommendation.
Deep Video Anomaly Detection: Opportunities and Challenges
Moreover, we summarise the characteristics and technical problems in current deep learning methods for video anomaly detection.
Fast Sinkhorn Filters: Using Matrix Scaling for Non-Rigid Shape Correspondence With Functional Maps
In this paper, we provide a theoretical foundation for pointwise map recovery from functional maps and highlight its relation to a range of shape correspondence methods based on spectral alignment.
Matching Algorithms: Fundamentals, Applications and Challenges
Based on the preference list access, matching problems are divided into two categories, i. e., explicit matching and implicit matching.
Information Retrieval
Recommendation Systems
Social and Information Networks
Coarse-to-Fine Entity Representations for Document-level Relation Extraction
In classification, we combine the entity representations from both two levels into more comprehensive representations for relation extraction.
Ranked #34 on
Relation Extraction
on DocRED
Multivariate Relations Aggregation Learning in Social Networks
Therefore, in graph learning tasks of social networks, the identification and utilization of multivariate relationship information are more important.
MODEL: Motif-based Deep Feature Learning for Link Prediction
Link prediction plays an important role in network analysis and applications.
Quantum nucleation of up-down quark matter and astrophysical implications
Quark matter with only $u$ and $d$ quarks ($ud$QM) might be the ground state of baryonic matter at large baryon number $A>A_{\rm min}$.
High Energy Physics - Phenomenology High Energy Astrophysical Phenomena General Relativity and Quantum Cosmology Nuclear Theory
MGCN: Descriptor Learning using Multiscale GCNs
Second, we propose a new multiscale graph convolutional network (MGCN) to transform a non-learned feature to a more discriminative descriptor.
ZoomOut: Spectral Upsampling for Efficient Shape Correspondence
Our main observation is that high quality maps can be obtained even if the input correspondences are noisy or are encoded by a small number of coefficients in a spectral basis.
Graphics
Neuro-Fuzzy Algorithmic (NFA) Models and Tools for Estimation
We made use of the Constructive Cost Model (COCOMO), Analysis of Variance (ANOVA), and Function Point Analysis as the algorithmic models and validated the accuracy of the Neuro-Fuzzy Algorithmic (NFA) Model in software cost estimation using industrial project data.
