9 papers
EvBS: Event-guided Blur Synthesis for Domain-adaptive Motion Deblurring
Junsik Jung, Seokryun Choi, Yoonki Cho +3
Motion deblurring has achieved remarkable progress with deep learning, yet pre-trained deblurring models often suffer from performance degradation in real-world scenarios due to th…
RoME: Robust Mixture of Low-Rank Experts against Multiple Adversarial Perturbations
Woo Jae Kim, Kyle Min, Suhyeon Ha +2
Multi-perturbation adversarial training (MAT) aims to achieve robustness against multiple perturbations but suffers from robustness trade-offs between different threats. T…
Radiometrically Consistent Gaussian Surfels for Inverse Rendering
Kyu Beom Han, Jaeyoon Kim, Woo Jae Kim +2
Inverse rendering with Gaussian Splatting has advanced rapidly, but accurately disentangling material properties from complex global illumination effects, particularly indirect ill…
No Caption, No Problem: Caption-Free Membership Inference via Model-Fitted Embeddings
Joonsung Jeon, Woo Jae Kim, Suhyeon Ha +2
Latent diffusion models have achieved remarkable success in high-fidelity text-to-image generation, but their tendency to memorize training data raises critical privacy and intelle…
Learning Event-guided Exposure-agnostic Video Frame Interpolation via Adaptive Feature Blending
Junsik Jung, Yoonki Cho, Woo Jae Kim +2
Exposure-agnostic video frame interpolation (VFI) is a challenging task that aims to recover sharp, high-frame-rate videos from blurry, low-frame-rate inputs captured under unknown…
AegisRF: Adversarial Perturbations Guided with Sensitivity for Protecting Intellectual Property of Neural Radiance Fields
Woo Jae Kim, Kyu Beom Han, Yoonki Cho +4
As Neural Radiance Fields (NeRFs) have emerged as a powerful tool for 3D scene representation and novel view synthesis, protecting their intellectual property (IP) from unauthorize…