AnoViT: Unsupervised Anomaly Detection and Localization With Vision Transformer-Based Encoder-Decoder. - Yunseung Lee; Pilsung Kang 001 (IEEE Access, 2022)

RCV2023 Challenges: Benchmarking Model Training and Inference for Resource-Constrained Deep Learning. - Rishabh Tiwari; Arnav Chavan; Deepak K. Gupta; Gowreesh Mago; Animesh Gupta; Akash Gupta; Suraj Sharan; Yukun Yang; Shanwei Zhao; Shihao Wang; Youngjun Kwak; Seonghun Jeong; et al. (ICCV (Workshops), 2023)

Robust Face Anti-Spoofing Framework with Convolutional Vision Transformer. - Yunseung Lee; Youngjun Kwak; Jinho Shin (ICIP, 2023)

1st Place in ICCV 2023 Workshop Challenge Track 1 on Resource Efficient Deep Learning for Computer Vision: Budgeted Model Training Challenge. - Youngjun Kwak; Seonghun Jeong; Yunseung Lee; Changick Kim (CoRR, 2023)

Cost-free adversarial defense: Distance-based optimization for model robustness without adversarial training. - Seungwan Seo; Yunseung Lee; Pilsung Kang 001 (Comput. Vis. Image Underst., 2023)

KorSmishing Explainer: A Korean-centric LLM-based Framework for Smishing Detection and Explanation Generation. - Yunseung Lee; Daehee Han (EMNLP (Industry Track), 2024)