most citedGeneralizable Person Re-identification via Balancing Alignment and Uniformity

3 citations · 3 across the 3 of their papers we have counts for

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cs.CV2025

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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025

AdvPaint: Protecting Images from Inpainting Manipulation via Adversarial Attention Disruption

Joonsung Jeon, Woo Jae Kim, Suhyeon Ha +2

The outstanding capability of diffusion models in generating high-quality images poses significant threats when misused by adversaries. In particular, we assume malicious adversari…

cs.CV2024

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…

cs.CV20243 cited

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…