4 papers · 1 filter
Decoupled Guidance: Disentangling Subject and Context Pathways in Text-to-Image Personalization
Seongmin Kim, Kyucheol Shin, Heesun Jung +2
Text-to-image personalization aims to generate a user-provided subject in novel scenes described by text. However, most existing methods encode subject identity (fidelity) and cont…
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization
Jungwook Seo, Minjeong Kim, Younkwan Lee +2
Detecting subtle visual anomalies in images remains challenging, particularly when only normal samples are available a priori. Such unsupervised anomaly detection is typically solv…
SeeDiff: Off-the-Shelf Seeded Mask Generation from Diffusion Models
Joon Hyun Park, Kumju Jo, Sungyong Baik
Entrusted with the goal of pixel-level object classification, the semantic segmentation networks entail the laborious preparation of pixel-level annotation masks. To obtain pixel-l…
CoAPT: Context Attribute words for Prompt Tuning
Gun Lee, Subin An, Sungyong Baik +1
We propose a novel prompt tuning method called CoAPT(Context Attribute words in Prompt Tuning) for few/zero-shot image classification. The core motivation is that attributes are de…