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Found 19 papers, 4 papers with code
Authentic Emotion Mapping: Benchmarking Facial Expressions in Real News
In this paper, we present a novel benchmark for Emotion Recognition using facial landmarks extracted from realistic news videos.
Dual-Path Coupled Image Deraining Network via Spatial-Frequency Interaction
Transformers have recently emerged as a significant force in the field of image deraining.
LRDif: Diffusion Models for Under-Display Camera Emotion Recognition
This study introduces LRDif, a novel diffusion-based framework designed specifically for facial expression recognition (FER) within the context of under-display cameras (UDC).
Using Scene and Semantic Features for Multi-modal Emotion Recognition
The semantic feature can include objects that are present in the environment, as well as their attributes and relationships with the target person.
HTNet for micro-expression recognition
The transformer layer is used to focus on representing local minor muscle movement with local self-attention in each area.
Ranked #1 on
Micro-Expression Recognition
on CASME II
Knowledge Graph Enhanced Intelligent Tutoring System Based on Exercise Representativeness and Informativeness
These components encompass the informativeness component, exercise representation component, knowledge importance component, and exercise representativeness component.
Knowledge Relation Rank Enhanced Heterogeneous Learning Interaction Modeling for Neural Graph Forgetting Knowledge Tracing
In this work, a novel knowledge tracing model, named Knowledge Relation Rank Enhanced Heterogeneous Learning Interaction Modeling for Neural Graph Forgetting Knowledge Tracing(NGFKT), is proposed to reduce the impact of the subjective labeling by calibrating the skill relation matrix and the Q-matrix and apply the Graph Convolutional Network(GCN) to model the heterogeneous interactions between students, exercises, and skills.
Learning Behavior Recognition in Smart Classroom with Multiple Students Based on YOLOv5
The use of computer vision technology to identify students' learning behavior in the classroom can reduce the workload of traditional teachers in supervising students in the classroom, and ensure greater accuracy and comprehensiveness.
Graph Neural Networks Enhanced Smart Contract Vulnerability Detection of Educational Blockchain
With the development of blockchain technology, more and more attention has been paid to the intersection of blockchain and education, and various educational evaluation systems and E-learning systems are developed based on blockchain technology.
Photovoltaic Panel Defect Detection Based on Ghost Convolution with BottleneckCSP and Tiny Target Prediction Head Incorporating YOLOv5
To ensure better accuracy on multiscale targets, the BottleneckCSP module is introduced to add a prediction head for tiny target detection to alleviate tiny defect misses, using Ghost convolution to improve the model inference speed and reduce the number of parameters.
DKT-STDRL: Spatial and Temporal Representation Learning Enhanced Deep Knowledge Tracing for Learning Performance Prediction
Then, the spatial features are connected with the original students' exercise features as joint learning features.
JSRNN: Joint Sampling and Reconstruction Neural Networks for High Quality Image Compressed Sensing
Most Deep Learning (DL) based Compressed Sensing (DCS) algorithms adopt a single neural network for signal reconstruction, and fail to jointly consider the influences of the sampling operation for reconstruction.
Image Compressed Sensing with Multi-scale Dilated Convolutional Neural Network
During the measurement period, we directly obtain all measurements from a trained measurement network, which employs fully convolutional structures and is jointly trained with the reconstruction network from the input image.
Spatio-Temporal Representation Learning Enhanced Source Cell-phone Recognition from Speech Recordings
In this paper, we propose a source cell-phone recognition method based on spatio-temporal representation learning, which includes two main parts: extraction of sequential Gaussian mean matrix features and construction of a recognition model based on spatio-temporal representation learning.
Abs-CAM: A Gradient Optimization Interpretable Approach for Explanation of Convolutional Neural Networks
However, when this method uses backpropagation to obtain gradients, it will cause noise in the saliency map, and even locate features that are irrelevant to decisions.
UIILD: A Unified Interpretable Intelligent Learning Diagnosis Framework for Intelligent Tutoring Systems
Intelligent learning diagnosis is a critical engine of intelligent tutoring systems, which aims to estimate learners' current knowledge mastery status and predict their future learning performance.
Analysis of Generalized Bregman Surrogate Algorithms for Nonsmooth Nonconvex Statistical Learning
Modern statistical applications often involve minimizing an objective function that may be nonsmooth and/or nonconvex.
Gaining Outlier Resistance with Progressive Quantiles: Fast Algorithms and Theoretical Studies
Outliers widely occur in big-data applications and may severely affect statistical estimation and inference.
Group Regularized Estimation under Structural Hierarchy
We give the minimax lower bounds for strong and weak hierarchical variable selection and show that the proposed estimators enjoy sharp rate oracle inequalities.
