1 citations · 2 across the 7 of their papers we have counts for
10 papers
Learning Neural Deformation Representation for 4D Dynamic Shape Generation
Gyojin Han, Jiwan Hur, Jaehyun Choi +1
Recent developments in 3D shape representation opened new possibilities for generating detailed 3D shapes. Despite these advances, there are few studies dealing with the generation…
SSG: Scaled Spatial Guidance for Multi-Scale Visual Autoregressive Generation
Youngwoo Shin, Jiwan Hur, Junmo Kim
Visual autoregressive (VAR) models generate images through next-scale prediction, naturally achieving coarse-to-fine, fast, high-fidelity synthesis mirroring human perception. In p…
Inlier-Centric Post-Training Quantization for Object Detection Models
Minsu Kim, Dongyeun Lee, Jaemyung Yu +3
Object detection is pivotal in computer vision, yet its immense computational demands make deployment slow and power-hungry, motivating quantization. However, task-irrelevant morph…
Frequency-Aware Token Reduction for Efficient Vision Transformer
Dong-Jae Lee, Jiwan Hur, Jaehyun Choi +2
Vision Transformers have demonstrated exceptional performance across various computer vision tasks, yet their quadratic computational complexity concerning token length remains a s…
DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization
Dongyeun Lee, Jiwan Hur, Hyounguk Shon +2
Diffusion models have achieved remarkable success in image generation but come with significant computational costs, posing challenges for deployment in resource-constrained enviro…
PRISM: Video Dataset Condensation with Progressive Refinement and Insertion for Sparse Motion
Jaehyun Choi, Jiwan Hur, Gyojin Han +2
Video dataset condensation aims to reduce the immense computational cost of video processing. However, it faces a fundamental challenge regarding the inseparable interdependence be…
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…
Expanding Expressiveness of Diffusion Models with Limited Data via Self-Distillation based Fine-Tuning
Jiwan Hur, Jaehyun Choi, Gyojin Han +2
Training diffusion models on limited datasets poses challenges in terms of limited generation capacity and expressiveness, leading to unsatisfactory results in various downstream t…
Deep Cross-Modal Steganography Using Neural Representations
Gyojin Han, Dong-Jae Lee, Jiwan Hur +2
Steganography is the process of embedding secret data into another message or data, in such a way that it is not easily noticeable. With the advancement of deep learning, Deep Neur…
I See-Through You: A Framework for Removing Foreground Occlusion in Both Sparse and Dense Light Field Images
Jiwan Hur, Jae Young Lee, Jaehyun Choi +1
Light field (LF) camera captures rich information from a scene. Using the information, the LF de-occlusion (LF-DeOcc) task aims to reconstruct the occlusion-free center view image.…