1 citations · 3 across the 7 of their papers we have counts for
9 papers
Program-Verified Self-Evolution for Vision-Language Models
Ahmed Heakl, Sungik Choi, Moontae Lee +1
Self-evolving vision-language models train on questions they generate from unlabeled images. Since these questions have no gold answers, prior methods label them by majority vote o…
Early Decisions Matter: Proximity Bias and Initial Trajectory Shaping in Non-Autoregressive Diffusion Language Models
Jiyeon Kim, Sungik Choi, Yongrae Jo +2
Diffusion-based language models (dLLMs) have emerged as a promising alternative to autoregressive language models, offering the potential for parallel token generation and bidirect…
Training-free Detection of AI-generated images via Cropping Robustness
Sungik Choi, Hankook Lee, Moontae Lee
AI-generated image detection has become crucial with the rapid advancement of vision-generative models. Instead of training detectors tailored to specific datasets, we study a trai…
HFI: A unified framework for training-free detection and implicit watermarking of latent diffusion model generated images
Sungik Choi, Hankook Lee, Jaehoon Lee +3
Dramatic advances in the quality of the latent diffusion models (LDMs) also led to the malicious use of AI-generated images. While current AI-generated image detection methods assu…
Partial-Multivariate Model for Forecasting
Jaehoon Lee, Hankook Lee, Sungik Choi +2
When solving forecasting problems including multiple time-series features, existing approaches often fall into two extreme categories, depending on whether to utilize inter-feature…
Diffusion based Semantic Outlier Generation via Nuisance Awareness for Out-of-Distribution Detection
Suhee Yoon, Sanghyu Yoon, Ye Seul Sim +5
Out-of-distribution (OOD) detection, which determines whether a given sample is part of the in-distribution (ID), has recently shown promising results through training with synthet…
Learning Equi-angular Representations for Online Continual Learning
Minhyuk Seo, Hyunseo Koh, Wonje Jeung +7
Online continual learning suffers from an underfitted solution due to insufficient training for prompt model update (e.g., single-epoch training). To address the challenge, we prop…
Projection Regret: Reducing Background Bias for Novelty Detection via Diffusion Models
Sungik Choi, Hankook Lee, Honglak Lee +1
Novelty detection is a fundamental task of machine learning which aims to detect abnormal ( out-of-distribution (OOD)) samples. Since diffusion models have recently…
Observation-Guided Diffusion Probabilistic Models
Junoh Kang, Jinyoung Choi, Sungik Choi +1
We propose a novel diffusion-based image generation method called the observation-guided diffusion probabilistic model (OGDM), which effectively addresses the tradeoff between qual…