35 citations · 102 across the 51 of their papers we have counts for
14 papers · 1 filter
OV-HHIR: Open Vocabulary Human Interaction Recognition Using Cross-modal Integration of Large Language Models
Lala Shakti Swarup Ray, Bo Zhou, Sungho Suh +1
Understanding human-to-human interactions, especially in contexts like public security surveillance, is critical for monitoring and maintaining safety. Traditional activity recogni…
Beyond Confusion: A Fine-grained Dialectical Examination of Human Activity Recognition Benchmark Datasets
Daniel Geissler, Dominique Nshimyimana, Vitor Fortes Rey +3
The research of machine learning (ML) algorithms for human activity recognition (HAR) has made significant progress with publicly available datasets. However, most research priorit…
Spend More to Save More (SM2): An Energy-Aware Implementation of Successive Halving for Sustainable Hyperparameter Optimization
Daniel Geissler, Bo Zhou, Sungho Suh +1
A fundamental step in the development of machine learning models commonly involves the tuning of hyperparameters, often leading to multiple model training runs to work out the best…
RAD: Region-Aware Diffusion Models for Image Inpainting
Sora Kim, Sungho Suh, Minsik Lee
Diffusion models have achieved remarkable success in image generation, with applications broadening across various domains. Inpainting is one such application that can benefit sign…
A Wearable Multi-Modal Edge-Computing System for Real-Time Kitchen Activity Recognition
Mengxi Liu, Sungho Suh, Juan Felipe Vargas +3
In the human activity recognition research area, prior studies predominantly concentrate on leveraging advanced algorithms on public datasets to enhance recognition performance, li…
DisQu: Investigating the Impact of Disorder in Quantum Generative Models
Yannick Werner, Jasmin Frkatovic, Vitor Fortes Rey +5
Disordered Quantum many-body Systems (DQS) and Quantum Neural Networks (QNN) have many structural features in common. However, a DQS is essentially an initialized QNN with random w…
TSAK: Two-Stage Semantic-Aware Knowledge Distillation for Efficient Wearable Modality and Model Optimization in Manufacturing Lines
Hymalai Bello, Daniel Geißler, Sungho Suh +2
Smaller machine learning models, with less complex architectures and sensor inputs, can benefit wearable sensor-based human activity recognition (HAR) systems in many ways, from co…
ALS-HAR: Harnessing Wearable Ambient Light Sensors to Enhance IMU-based Human Activity Recogntion
Lala Shakti Swarup Ray, Daniel Geißler, Mengxi Liu +3
Despite the widespread integration of ambient light sensors (ALS) in smart devices commonly used for screen brightness adaptation, their application in human activity recognition (…
LLM-Generated Tips Rival Expert-Created Tips in Helping Students Answer Quantum-Computing Questions
Lars Krupp, Jonas Bley, Isacco Gobbi +11
Individual teaching is among the most successful ways to impart knowledge. Yet, this method is not always feasible due to large numbers of students per educator. Quantum computing…
Sensor Data Augmentation from Skeleton Pose Sequences for Improving Human Activity Recognition
Parham Zolfaghari, Vitor Fortes Rey, Lala Ray +3
The proliferation of deep learning has significantly advanced various fields, yet Human Activity Recognition (HAR) has not fully capitalized on these developments, primarily due to…
BeSound: Bluetooth-Based Position Estimation Enhancing with Cross-Modality Distillation
Hymalai Bello, Sungho Suh, Bo Zhou +1
Smart factories leverage advanced technologies to optimize manufacturing processes and enhance efficiency. Implementing worker tracking systems, primarily through camera-based meth…
Text me the data: Generating Ground Pressure Sequence from Textual Descriptions for HAR
Lala Shakti Swarup Ray, Bo Zhou, Sungho Suh +3
In human activity recognition (HAR), the availability of substantial ground truth is necessary for training efficient models. However, acquiring ground pressure data through physic…
CoSS: Co-optimizing Sensor and Sampling Rate for Data-Efficient AI in Human Activity Recognition
Mengxi Liu, Zimin Zhao, Daniel Geißler +3
Recent advancements in Artificial Neural Networks have significantly improved human activity recognition using multiple time-series sensors. While employing numerous sensors with h…
The Power of Training: How Different Neural Network Setups Influence the Energy Demand
Daniel Geißler, Bo Zhou, Mengxi Liu +2
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…