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20242026
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cs.CV2026

MDS-DETR: DETR with Masked Duplicate Suppressor

Chanho Lee, Seunghee Koh, Yunho Jeon +1

The DEtection TRansformer (DETR) is a powerful end-to-end object detector, yet its one-to-one matching strategy suffers from slow convergence and low recall. A common approach to a…

cs.CV2026

ConceptPrism: Concept Disentanglement in Personalized Diffusion Models via Residual Token Optimization

Minseo Kim, Minchan Kwon, Dongyeun Lee +2

Personalized text-to-image (T2I) generation has emerged as a key application for creating user-specific concepts from a few reference images. The core challenge is concept disentan…

cs.CV2025

Comparison Reveals Commonality: Customized Image Generation through Contrastive Inversion

Minseo Kim, Minchan Kwon, Dongyeun Lee +2

The recent demand for customized image generation raises a need for techniques that effectively extract the common concept from small sets of images. Existing methods typically rel…

cs.CV2025

SFLD: Reducing the content bias for AI-generated Image Detection

Seoyeon Gye, Junwon Ko, Hyounguk Shon +2

Identifying AI-generated content is critical for the safe and ethical use of generative AI. Recent research has focused on developing detectors that generalize to unknown generator…

cs.CV2024

Unlocking the Capabilities of Masked Generative Models for Image Synthesis via Self-Guidance

Jiwan Hur, Dong-Jae Lee, Gyojin Han +3

Masked generative models (MGMs) have shown impressive generative ability while providing an order of magnitude efficient sampling steps compared to continuous diffusion models. How…