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Found 48 papers, 13 papers with code
The CRECIL Corpus: a New Dataset for Extraction of Relations between Characters in Chinese Multi-party Dialogues
We describe a new freely available Chinese multi-party dialogue dataset for automatic extraction of dialogue-based character relationships.
Do Deep Neural Nets Display Human-like Attention in Short Answer Scoring?
Deep Learning (DL) techniques have been increasingly adopted for Automatic Text Scoring in education.
Markov $α$-Potential Games: Equilibrium Approximation and Regret Analysis
This paper proposes a new framework to study multi-agent interaction in Markov games: Markov $\alpha$-potential games.
Functional sufficient dimension reduction through information maximization with application to classification
Considering the case where the response variable is a categorical variable and the predictor is a random function, two novel functional sufficient dimensional reduction (FSDR) methods are proposed based on mutual information and square loss mutual information.
LooPy: A Research-Friendly Mix Framework for Music Information Retrieval on Electronic Dance Music
Music information retrieval (MIR) has gone through an explosive development with the advancement of deep learning in recent years.
Revisiting Multimodal Representation in Contrastive Learning: From Patch and Token Embeddings to Finite Discrete Tokens
However, direct aligning cross-modal information using such representations is challenging, as visual patches and text tokens differ in semantic levels and granularities.
Bifurcation analysis of the Keynesian cross model
This study rigorously investigates the Keynesian cross model of a national economy with a focus on the dynamic relationship between government spending and economic equilibrium.
Practical and Ethical Challenges of Large Language Models in Education: A Systematic Literature Review
Educational technology innovations that have been developed based on large language models (LLMs) have shown the potential to automate the laborious process of generating and analysing textual content.
Nearest-Neighbor Inter-Intra Contrastive Learning from Unlabeled Videos
Contrastive learning has recently narrowed the gap between self-supervised and supervised methods in image and video domain.
The impact of moving expenses on social segregation: a simulation with RL and ABM
Over the past decades, breakthroughs such as Reinforcement Learning (RL) and Agent-based modeling (ABM) have made simulations of economic models feasible.
Robust Direct Learning for Causal Data Fusion
In the era of big data, the explosive growth of multi-source heterogeneous data offers many exciting challenges and opportunities for improving the inference of conditional average treatment effects.
Sentiment Analysis on Inflation after Covid-19
We implement traditional machine learning and deep learning methods for global tweets from 2017-2022 to build a high-frequency measure of the public's sentiment index on inflation and analyze its correlation with other online data sources such as google trend and market-oriented inflation index.
A New Knowledge Distillation Network for Incremental Few-Shot Surface Defect Detection
The proposed DKAN method follows a pretraining-finetuning transfer learning paradigm and a knowledge distillation framework is designed for fine-tuning.
HouseX: A Fine-grained House Music Dataset and its Potential in the Music Industry
Machine sound classification has been one of the fundamental tasks of music technology.
Position Encoding Enhanced Feature Mapping for Image Anomaly Detection
Image anomaly detection is an important stage for automatic visual inspection in intelligent manufacturing systems.
Ranked #5 on
Anomaly Detection
on MVTec AD
(Segmentation AUPRO metric)
What to look at and where: Semantic and Spatial Refined Transformer for detecting human-object interactions
We propose a novel one-stage Transformer-based semantic and spatial refined transformer (SSRT) to solve the Human-Object Interaction detection task, which requires to localize humans and objects, and predicts their interactions.
Stochastic Backpropagation: A Memory Efficient Strategy for Training Video Models
We propose a memory efficient method, named Stochastic Backpropagation (SBP), for training deep neural networks on videos.
Integrated Human Activity Sensing and Communications
In this article, we provide a review of human related sensing in the context of ISAC.
Improving short-term bike sharing demand forecast through an irregular convolutional neural network
As an important task for the management of bike sharing systems, accurate forecast of travel demand could facilitate dispatch and relocation of bicycles to improve user satisfaction.
Id-Free Person Similarity Learning
In this work, we explore training such a model by only using person box annotations, thus removing the necessity of manually labeling a training dataset with additional person identity annotation as these are expensive to collect.
A new neighborhood structure for job shop scheduling problems
According to the above finding, this paper proposes a new N8 neighborhood structure considering the movement of critical operations within a critical block and the movement of critical operations outside the critical block.
Unsupervised Image Anomaly Detection and Segmentation Based on Pre-trained Feature Mapping
This paper proposes a novel framework, named as Pre-trained Feature Mapping (PFM), for unsupervised image anomaly detection and segmentation.
Ranked #35 on
Anomaly Detection
on MVTec AD
Video Contrastive Learning with Global Context
Our formulation is able to capture global context in a video, thus robust to temporal content change.
Long Short-Term Transformer for Online Action Detection
We present Long Short-term TRansformer (LSTR), a temporal modeling algorithm for online action detection, which employs a long- and short-term memory mechanism to model prolonged sequence data.
Ranked #2 on
Online Action Detection
on TVSeries
SSCAP: Self-supervised Co-occurrence Action Parsing for Unsupervised Temporal Action Segmentation
However, it is quite expensive to annotate every frame in a large corpus of videos to construct a comprehensive supervised training dataset.
SiamMOT: Siamese Multi-Object Tracking
In this paper, we focus on improving online multi-object tracking (MOT).
Adversarial Training for Gradient Descent: Analysis Through its Continuous-time Approximation
Adversarial training has gained great popularity as one of the most effective defenses for deep neural network and more generally for gradient-based machine learning models against adversarial perturbations on data points.
VidTr: Video Transformer Without Convolutions
We first introduce the vanilla video transformer and show that transformer module is able to perform spatio-temporal modeling from raw pixels, but with heavy memory usage.
Ranked #14 on
Action Classification
on Charades
TubeR: Tubelet Transformer for Video Action Detection
We propose TubeR: a simple solution for spatio-temporal video action detection.
Selective Feature Compression for Efficient Activity Recognition Inference
In this work, we focus on improving the inference efficiency of current action recognition backbones on trimmed videos, and illustrate that one action model can also cover then informative region by dropping non-informative features.
Industrial Image Anomaly Localization Based on Gaussian Clustering of Pretrained Feature
Anomaly localization is valuable for improvement of complex production processing in smart manufacturing system.
Ranked #52 on
Anomaly Detection
on MVTec AD
Learning-based Prediction and Uplink Retransmission for Wireless Virtual Reality (VR) Network
While for the online learning algorithm, based on the VR user's actual viewpoint delivered through uplink transmission, we compare it with the predicted viewpoint and update the parameters of the online learning algorithm to further improve the prediction accuracy.
NUTA: Non-uniform Temporal Aggregation for Action Recognition
In the world of action recognition research, one primary focus has been on how to construct and train networks to model the spatial-temporal volume of an input video.
A Comprehensive Study of Deep Video Action Recognition
Video action recognition is one of the representative tasks for video understanding.
Multi-Label Activity Recognition using Activity-specific Features and Activity Correlations
Multi-label activity recognition is designed for recognizing multiple activities that are performed simultaneously or sequentially in each video.
Directional Temporal Modeling for Action Recognition
Many current activity recognition models use 3D convolutional neural networks (e. g. I3D, I3D-NL) to generate local spatial-temporal features.
Vortices and waves in light dark matter
(4) The density near a vortex scales as $r^2$ while the velocity goes as $1/r$, where $r$ is the distance to vortex.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies General Relativity and Quantum Cosmology High Energy Physics - Phenomenology High Energy Physics - Theory
BERTSel: Answer Selection with Pre-trained Models
we are the first to explore the performance of fine-tuning BERT for answer selection.
Everyone is a Cartoonist: Selfie Cartoonization with Attentive Adversarial Networks
Selfie and cartoon are two popular artistic forms that are widely presented in our daily life.
Tri-axial Self-Attention for Concurrent Activity Recognition
We present a system for concurrent activity recognition.
Differential Evolution with Better and Nearest Option for Function Optimization
Differential evolution(DE) is a conventional algorithm with fast convergence speed.
Hybrid Attention based Multimodal Network for Spoken Language Classification
The proposed hybrid attention architecture helps the system focus on learning informative representations for both modality-specific feature extraction and model fusion.
Multimodal Affective Analysis Using Hierarchical Attention Strategy with Word-Level Alignment
Multimodal affective computing, learning to recognize and interpret human affects and subjective information from multiple data sources, is still challenging because: (i) it is hard to extract informative features to represent human affects from heterogeneous inputs; (ii) current fusion strategies only fuse different modalities at abstract level, ignoring time-dependent interactions between modalities.
Whale swarm algorithm with the mechanism of identifying and escaping from extreme points for multimodal function optimization
However, there are two difficulties urgently to be solved for most existing niching metaheuristic algorithms: how to set the optimal values of niching parameters for different optimization problems, and how to jump out of the local optima efficiently.
Progress Estimation and Phase Detection for Sequential Processes
For the Olympic swimming dataset, our system achieved an accuracy of 88%, an F1-score of 0. 58, a completeness estimation error of 6. 3% and a remaining-time estimation error of 2. 9 minutes.
Whale swarm algorithm for function optimization
Increasing nature-inspired metaheuristic algorithms are applied to solving the real-world optimization problems, as they have some advantages over the classical methods of numerical optimization.
Online People Tracking and Identification with RFID and Kinect
We introduce a novel, accurate and practical system for real-time people tracking and identification.
Concurrent Activity Recognition with Multimodal CNN-LSTM Structure
Our system is the first to address the concurrent activity recognition with multisensory data using a single model, which is scalable, simple to train and easy to deploy.
