5 papers · 1 filter
ReState: Noise-Level-Aware State Correction for Training-Free Object Removal in Few-Step Diffusion Models
Donghwan Lee, Sanghyun Jo, Sihyun Kim +2
Training-free object removal commonly redirects self-attention away from the target region, but on few-step distilled diffusion models this often leaves object-shaped residuals. We…
Value-Informed Schedule Trimming for Accelerated Sequential Monte Carlo Guidance in Discrete Diffusion
Jaehyeon Kim, Ziseok Lee, Siyoung Choi +1
Reward-guided discrete diffusion enables test-time alignment with downstream objectives, but applying Sequential Monte Carlo (SMC) guidance at every denoising step is computational…
Risk-Calibrated Proposal Transport for Finite-Particle Diffusion Steering
Ziseok Lee, Jaehyeon Kim, Seungwon Kim +6
Inference-time steering combines pretrained diffusion experts or rewards without retraining by changing the dynamics that transport noise to data. Feynman-Kac correction compensate…
SurfSpec: Enhancing Off-Target-Agnostic Specificity by Bounding Pocket-Ligand Geometric Mismatch
Minyeong Hwang, Yoorim Gang, Ziseok Lee +5
Lead optimization in structure-based drug design aims to improve target binding while avoiding unintended interactions with off-target pockets. However, existing affinity-driven me…
PatchGate: Narrowing the Verbalization Gap with Intrinsic Object Inventories in Frozen Vision-Language Models
Jihyung Ko, Eunji Jung, Hyeongsub Kim +4
Reliable image captioning in Vision-Language Models (VLMs) requires captions to be both precise and complete, avoiding unsupported object mentions while covering visible objects. E…