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20222026
most citedPart-based Pseudo Label Refinement for Unsupervised Person Re-identification

14 citations · 17 across the 4 of their papers we have counts for

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7 papers · 1 filter

cs.CV2026

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…

cs.CV2026

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…

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

Pose-free 3D Gaussian splatting via shape-ray estimation

Youngju Na, Taeyeon Kim, Jumin Lee +3

While generalizable 3D Gaussian splatting enables efficient, high-quality rendering of unseen scenes, it heavily depends on precise camera poses for accurate geometry. In real-worl…

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.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…