14 citations · 19 across the 9 of their papers we have counts for
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…
Towards Test-time Efficient Visual Place Recognition via Asymmetric Query Processing
Jaeyoon Kim, Yoonki Cho, Sung-Eui Yoon
Visual Place Recognition (VPR) has advanced significantly with high-capacity foundation models like DINOv2, achieving remarkable performance. Nonetheless, their substantial computa…
Pinpointing Trigger Moment for Grounded Video QA: Enhancing Spatio-temporal Grounding in Multimodal Large Language Models
Jinhwan Seo, Yoonki Cho, Junhyug Noh +1
In this technical report, we introduce a framework to address Grounded Video Question Answering (GVQA) task for the ICCV 2025 Perception Test Challenge. The GVQA task demands robus…
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…
Enhancing Visual Re-ranking through Denoising Nearest Neighbor Graph via Continuous CRF
Jaeyoon Kim, Yoonki Cho, Taeyoung Kim +1
Nearest neighbor (NN) graph based visual re-ranking has emerged as a powerful approach for improving retrieval accuracy, offering the advantages of effectively exploring high-dimen…
Generalizable Person Re-identification via Balancing Alignment and Uniformity
Yoonki Cho, Jaeyoon Kim, Woo Jae Kim +2
Domain generalizable person re-identification (DG re-ID) aims to learn discriminative representations that are robust to distributional shifts. While data augmentation is a straigh…
Feature Separation and Recalibration for Adversarial Robustness
Woo Jae Kim, Yoonki Cho, Junsik Jung +1
Deep neural networks are susceptible to adversarial attacks due to the accumulation of perturbations in the feature level, and numerous works have boosted model robustness by deact…
Part-based Pseudo Label Refinement for Unsupervised Person Re-identification
Yoonki Cho, Woo Jae Kim, Seunghoon Hong +1
Unsupervised person re-identification (re-ID) aims at learning discriminative representations for person retrieval from unlabeled data. Recent techniques accomplish this task by us…