Period: 2019 – 2021
Orbit segmentation and 3D image classification for objective diagnosis and severity assessment of Graves’ orbitopathy.
Techniques: Orbit Segmentation, 3D Image Classification
Related Paper: Neural Network-based Method for Diagnosing Graves’ Orbitopathy Using Orbital Computed Tomography (Scientific Reports, 2022)
Code:
Period: 2020 – 2022
A self-assembly framework for on-device deep learning, focused on hardware-aware NAS and real-time human activity recognition with multi-scale temporal sampling.
Techniques:
Code:
Period: 2020 – 2021
Medical image analysis pipeline for polyp segmentation and wall thickening assessment in gallbladder ultrasound and CT images.
Techniques: Polyp Segmentation, Medical Image Analysis
Period: 2022 – 2023
A no-code framework for end-to-end neural network creation and operation, covering object detection, hardware-aware NAS, and post-training quantization.
Techniques: YOLO, Hardware-Aware NAS, Post-Training Quantization, Docker
Code & Resources:
Period: Jan. 2024 – Apr. 2024
A vision-language retrieval system that aligns video and text representations along an emotion-aware axis.
Period: Mar. 2024 – Jun. 2024
Latent-space optimization for molecular generation using a VAE with Bayesian optimization.
Techniques: Variational Autoencoder (VAE), Bayesian Optimization, Latent Space Analysis
Materials:
Period: 2023 – Present
Building deployable on-device learning systems through on-device training, online continual learning, hardware-aware NAS, multimodal learning, and OOD detection.
Techniques:
Code:
Published in IEEE Access, 2022
A multi-temporal sampling module that captures activities at varying time scales to enable accurate, real-time human activity recognition.
Recommended citation: J. Park, W. Lim, D. Kim, J. Lee. (2022). "Multi-Temporal Sampling Module for Real-Time Human Activity Recognition." IEEE Access.
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Published in Electronics Letters, 2022
A method to search for an optimal group convolution structure within neural architecture search, improving accuracy-efficiency trade-offs.
Recommended citation: W. Seo, J. Park, W. Lim, D. Kim, J. Lee. (2022). "Achieving an Optimal Group Structure in a Neural Architecture Search." Electronics Letters.
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Published in Scientific Reports, 2022
A neural network pipeline for diagnosis and severity assessment of Graves’ orbitopathy from orbital CT scans.
Recommended citation: J. Lee, W. Seo, J. Park, W. Lim, J. Oh, N. Moon, J. Lee. (2022). "Neural Network-based Method for Diagnosing Graves' Orbitopathy Using Orbital Computed Tomography." Scientific Reports.
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Published in IEEE Access, 2023
A lookup table-based neural architecture search method that rapidly identifies efficient HAR models tailored to mobile device latency profiles.
Recommended citation: W. Lim, W. Seo, D. Kim, J. Lee. (2023). "Efficient Human Activity Recognition Using Lookup Table-based Neural Architecture Search for Mobile Devices." IEEE Access.
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Published in Engineering Applications of Artificial Intelligence, 2023
A budget-aware flexible architecture for real-time human activity recognition from wearable sensors, balancing accuracy and on-device latency constraints.
Recommended citation: J. Park, W. Lim, D. Kim, J. Lee. (2023). "GTSNet: Flexible Architecture Under Budget Constraint for Real-Time Human Activity Recognition from Wearable Sensor." Engineering Applications of Artificial Intelligence.
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Published in IEEE Access, 2024
A mixup-based experience replay strategy that mitigates catastrophic forgetting in online class-incremental learning by interpolating samples across past and current tasks.
Recommended citation: W. Lim, Y. Zhou, D. Kim, J. Lee. (2024). "MixER: Mixup-based Experience Replay for Online Class-Incremental Learning." IEEE Access.
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Published in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026
A patch-aware sparse prompting method with decoupled training that enables continual learning on resource-constrained edge devices.
Recommended citation: W. Lim, J. Lee, D. Kim. (2026). "Critical Patch-Aware Sparse Prompting with Decoupled Training for Continual Learning on the Edge." IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
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Published in ICML 2026 Workshop on Epistemic Intelligence in Machine Learning, 2026
A modality-aware novelty detection framework for open-world egocentric activity recognition that leverages multimodal cues to distinguish novel activities from known ones.
Recommended citation: H. Im*, W. Lim*, D. Kim. (2026). "MAND: Modality-Aware Novelty Detection for Open-World Egocentric Activity Recognition." ICML 2026 Workshop on Epistemic Intelligence in Machine Learning. (* Equal contribution)
Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.