Search Results for author: Minchul Kim

Found 12 papers, 7 papers with code

KeyPoint Relative Position Encoding for Face Recognition

no code implementations21 Mar 2024 Minchul Kim, Yiyang Su, Feng Liu, Anil Jain, Xiaoming Liu

By anchoring the significance of pixels around keypoints, the model can more effectively retain spatial relationships, even when those relationships are disrupted by affine transformations.

Face Recognition Gait Recognition +1

Token Fusion: Bridging the Gap between Token Pruning and Token Merging

no code implementations2 Dec 2023 Minchul Kim, Shangqian Gao, Yen-Chang Hsu, Yilin Shen, Hongxia Jin

In this paper, we introduce "Token Fusion" (ToFu), a method that amalgamates the benefits of both token pruning and token merging.

Computational Efficiency Image Generation

DCFace: Synthetic Face Generation with Dual Condition Diffusion Model

1 code implementation CVPR 2023 Minchul Kim, Feng Liu, Anil Jain, Xiaoming Liu

Our novel Patch-wise style extractor and Time-step dependent ID loss enables DCFace to consistently produce face images of the same subject under different styles with precise control.

Face Generation Synthetic Face Recognition

Cluster and Aggregate: Face Recognition with Large Probe Set

1 code implementation19 Oct 2022 Minchul Kim, Feng Liu, Anil Jain, Xiaoming Liu

Advances in attention and recurrent modules have led to feature fusion that can model the relationship among the images in the input set.

 Ranked #1 on Face Verification on IJB-B (TAR @ FAR=0.001 metric)

Face Recognition Face Verification +4

Controllable and Guided Face Synthesis for Unconstrained Face Recognition

2 code implementations20 Jul 2022 Feng Liu, Minchul Kim, Anil Jain, Xiaoming Liu

To address this problem, we propose a controllable face synthesis model (CFSM) that can mimic the distribution of target datasets in a style latent space.

Face Generation Face Recognition +1

Information-Theoretic Privacy in Federated Submodel learning

no code implementations17 Aug 2020 Minchul Kim, Jungwoo Lee

We consider information-theoretic privacy in federated submodel learning, where a global server has multiple submodels.

Information Theory Information Theory

Learning Visual Context by Comparison

2 code implementations ECCV 2020 Minchul Kim, Jongchan Park, Seil Na, Chang Min Park, Donggeun Yoo

Current methods for solving this task exploit various characteristics of the chest X-ray image, but one of the most important characteristics is still missing: the necessity of comparison between related regions in an image.

object-detection Object Detection

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