Search Results for author:
Found 36 papers, 6 papers with code
Two-stage generative adversarial networks for document image binarization with color noise and background removal
Document image enhancement and binarization methods are often used to improve the accuracy and efficiency of document image analysis tasks such as text recognition.
Ranked #1 on
Binarization
on LRDE DBD
Generalized multiscale feature extraction for remaining useful life prediction of bearings with generative adversarial networks
The experimental results show that the proposed feature extraction method can effectively predict the RUL and outperforms the conventional RUL prediction approaches based on deep neural networks.
Discriminative feature generation for classification of imbalanced data
In this paper, we propose a novel supervised discriminative feature generation (DFG) method for a minority class dataset.
Proxy Anchor-based Unsupervised Learning for Continuous Generalized Category Discovery
Existing methods for novel category discovery are limited by their reliance on labeled datasets and prior knowledge about the number of novel categories and the proportion of novel samples in the batch.
ContextMix: A context-aware data augmentation method for industrial visual inspection systems
With the minimal additional computation cost of image resizing, ContextMix enhances performance compared to existing augmentation techniques.
Fusion of Global-Local Features for Image Quality Inspection of Shipping Label
These results are expected to improve the shipping address recognition and verification system by applying different image preprocessing steps based on the classified conditions.
Sequential Targeting: an incremental learning approach for data imbalance in text classification
Classification tasks require a balanced distribution of data to ensure the learner to be trained to generalize over all classes.
Supervised Segmentation with Domain Adaptation for Small Sampled Orbital CT Images
Deep neural networks (DNNs) have been widely used for medical image analysis.
Adversarial Deep Feature Extraction Network for User Independent Human Activity Recognition
The proposed network is based on the adversarial encoder-decoder structure with the MMD realign the data distribution over multiple subjects.
Human-Centric Artificial Intelligence Architecture for Industry 5.0 Applications
Human-centricity is the core value behind the evolution of manufacturing towards Industry 5. 0.
Estimation of 3D Body Shape and Clothing Measurements from Frontal- and Side-view Images
The estimation of 3D human body shape and clothing measurements is crucial for virtual try-on and size recommendation problems in the fashion industry but has always been a challenging problem due to several conditions, such as lack of publicly available realistic datasets, ambiguity in multiple camera resolutions, and the undefinable human shape space.
TASKED: Transformer-based Adversarial learning for human activity recognition using wearable sensors via Self-KnowledgE Distillation
In this work, we propose a novel Transformer-based Adversarial learning framework for human activity recognition using wearable sensors via Self-KnowledgE Distillation (TASKED), that accounts for individual sensor orientations and spatial and temporal features.
Smart-Badge: A wearable badge with multi-modal sensors for kitchen activity recognition
Meanwhile, we evaluate the impact of the infrared array sensor on the recognition accuracy of these activities.
Learning from the Best: Contrastive Representations Learning Across Sensor Locations for Wearable Activity Recognition
To mitigate this problem we propose a method that facilitates the use of information from sensors that are only present during the training process and are unavailable during the later use of the system.
AI-KD: Adversarial learning and Implicit regularization for self-Knowledge Distillation
Our model not only distills the deterministic and progressive knowledge which are from the pre-trained and previous epoch predictive probabilities but also transfers the knowledge of the deterministic predictive distributions using adversarial learning.
PresSim: An End-to-end Framework for Dynamic Ground Pressure Profile Generation from Monocular Videos Using Physics-based 3D Simulation
Ground pressure exerted by the human body is a valuable source of information for human activity recognition (HAR) in unobtrusive pervasive sensing.
InMyFace: Inertial and Mechanomyography-Based Sensor Fusion for Wearable Facial Activity Recognition
The sensors were placed unobtrusively in a sports cap to monitor facial muscle activities related to facial expressions.
A Knowledge Distillation framework for Multi-Organ Segmentation of Medaka Fish in Tomographic Image
Morphological atlases are an important tool in organismal studies, and modern high-throughput Computed Tomography (CT) facilities can produce hundreds of full-body high-resolution volumetric images of organisms.
Learning Graph Patterns of Reflection Coefficient for Non-destructive Diagnosis of Cu Interconnects
Fault detection and diagnosis of the interconnects are crucial for prognostics and health management (PHM) of electronics.
FieldHAR: A Fully Integrated End-to-end RTL Framework for Human Activity Recognition with Neural Networks from Heterogeneous Sensors
In this work, we propose an open-source scalable end-to-end RTL framework FieldHAR, for complex human activity recognition (HAR) from heterogeneous sensors using artificial neural networks (ANN) optimized for FPGA or ASIC integration.
Chemical Property-Guided Neural Networks for Naphtha Composition Prediction
In this paper, we propose a neural network framework that utilizes chemical property information to improve the performance of naphtha composition prediction.
CaptAinGlove: Capacitive and Inertial Fusion-Based Glove for Real-Time on Edge Hand Gesture Recognition for Drone Control
We present CaptAinGlove, a textile-based, low-power (1. 15Watts), privacy-conscious, real-time on-the-edge (RTE) glove-based solution with a tiny memory footprint (2MB), designed to recognize hand gestures used for drone control.
Unsupervised Statistical Feature-Guided Diffusion Model for Sensor-based Human Activity Recognition
The proposed method aims to generate synthetic time-series sensor data without relying on labeled data, addressing the scarcity and annotation difficulties associated with real-world sensor data.
Ranked #1 on
Human Activity Recognition
on MM-Fit
Generative Adversarial Network
Human Activity Recognition
+1
ClothFit: Cloth-Human-Attribute Guided Virtual Try-On Network Using 3D Simulated Dataset
The proposed method utilizes a U-Net-based network architecture that incorporates cloth and human attributes to guide the realistic virtual try-on synthesis.
MeciFace: Mechanomyography and Inertial Fusion-based Glasses for Edge Real-Time Recognition of Facial and Eating Activities
The increasing prevalence of stress-related eating behaviors and their impact on overall health highlights the importance of effective and ubiquitous monitoring systems.
SynthCal: A Synthetic Benchmarking Pipeline to Compare Camera Calibration Algorithms
Accurate camera calibration is crucial for various computer vision applications.
Selecting the motion ground truth for loose-fitting wearables: benchmarking optical MoCap methods
To help smart wearable researchers choose the optimal ground truth methods for motion capturing (MoCap) for all types of loose garments, we present a benchmark, DrapeMoCapBench (DMCB), specifically designed to evaluate the performance of optical marker-based and marker-less MoCap.
PressureTransferNet: Human Attribute Guided Dynamic Ground Pressure Profile Transfer using 3D simulated Pressure Maps
We propose PressureTransferNet, a novel method for Human Activity Recognition (HAR) using ground pressure information.
Two-stage Early Prediction Framework of Remaining Useful Life for Lithium-ion Batteries
Early prediction of remaining useful life (RUL) is crucial for effective battery management across various industries, ranging from household appliances to large-scale applications.
Worker Activity Recognition in Manufacturing Line Using Near-body Electric Field
This paper presents a novel wearable sensing prototype that combines IMU and body capacitance sensing modules to recognize worker activities in the manufacturing line.
In situ Fault Diagnosis of Indium Tin Oxide Electrodes by Processing S-Parameter Patterns
In the field of optoelectronics, indium tin oxide (ITO) electrodes play a crucial role in various applications, such as displays, sensors, and solar cells.
A Novel Local-Global Feature Fusion Framework for Body-weight Exercise Recognition with Pressure Mapping Sensors
We present a novel local-global feature fusion framework for body-weight exercise recognition with floor-based dynamic pressure maps.
Remaining Useful Life Prediction of Lithium-ion Batteries using Spatio-temporal Multimodal Attention Networks
The proposed ST-MAN is to capture the complex spatio-temporal dependencies in the battery data, including the features that are often neglected in existing works.
The Power of Training: How Different Neural Network Setups Influence the Energy Demand
This work offers a heuristic evaluation of the effects of variations in machine learning training regimes and learning paradigms on the energy consumption of computing, especially HPC hardware with a life-cycle aware perspective.
CoSS: Co-optimizing Sensor and Sampling Rate for Data-Efficient AI in Human Activity Recognition
Recent advancements in Artificial Neural Networks have significantly improved human activity recognition using multiple time-series sensors.
Text me the data: Generating Ground Pressure Sequence from Textual Descriptions for HAR
We show that the combination of vector quantization of sensor data along with simple text conditioned auto regressive strategy allows us to obtain high-quality generated pressure sequences from textual descriptions with the help of discrete latent correlation between text and pressure maps.
