180 citations
- NAVER Cloud (South Korea)KR39 papers
- Korea Advanced Institute of Science and TechnologyKR12 papers
- Sungkyunkwan UniversityKR9 papers
- Seoul National UniversityKR8 papers
- Yonsei UniversityKR8 papers
- Virginia TechUS5 papers
- Line Corporation (Japan)JP4 papers
- University of TorontoCA4 papers
- Inha UniversityKR3 papers
- Kootenay Association for Science & TechnologyCA3 papers
- Korea UniversityKR3 papers
- Daegu Gyeongbuk Institute of Science and TechnologyKR2 papers
22 papers · 1 filter
Domain-generalizable Face Anti-Spoofing with Patch-based Multi-tasking and Artifact Pattern Conversion
Seungjin Jung, Yonghyun Jeong, Minha Kim +3
Face Anti-Spoofing (FAS) algorithms, designed to secure face recognition systems against spoofing, struggle with limited dataset diversity, impairing their ability to handle unseen…
Factorized Multi-Resolution HashGrid for Efficient Neural Radiance Fields: Execution on Edge-Devices
Kim Jun-Seong, Mingyu Kim, GeonU Kim +2
We introduce Fact-Hash, a novel parameter-encoding method for training on-device neural radiance fields. Neural Radiance Fields (NeRF) have proven pivotal in 3D representations, bu…
EBDM: Exemplar-guided Image Translation with Brownian-bridge Diffusion Models
Eungbean Lee, Somi Jeong, Kwanghoon Sohn
Exemplar-guided image translation, synthesizing photo-realistic images that conform to both structural control and style exemplars, is attracting attention due to its ability to en…
Blind-Match: Efficient Homomorphic Encryption-Based 1:N Matching for Privacy-Preserving Biometric Identification
Hyunmin Choi, Jiwon Kim, Chiyoung Song +2
We present Blind-Match, a novel biometric identification system that leverages homomorphic encryption (HE) for efficient and privacy-preserving 1:N matching. Blind-Match introduces…
Camera Agnostic Two-Head Network for Ego-Lane Inference
Chaehyeon Song, Sungho Yoon, Minhyeok Heo +2
Vision-based ego-lane inference using High-Definition (HD) maps is essential in autonomous driving and advanced driver assistance systems. The traditional approach necessitates wel…
DiffBlender: Composable and Versatile Multimodal Text-to-Image Diffusion Models
Sungnyun Kim, Junsoo Lee, Kibeom Hong +2
In this study, we aim to enhance the capabilities of diffusion-based text-to-image (T2I) generation models by integrating diverse modalities beyond textual descriptions within a un…