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20202026
most citedTeaching Where to Look: Attention Similarity Knowledge Distillation for Low Resolution Face Recognition

4 citations · 8 across the 8 of their papers we have counts for

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6 papers · 1 filter

cs.CV2026

CD-FKD: Cross-Domain Feature Knowledge Distillation for Robust Single-Domain Generalization in Object Detection

Junseok Lee, Sungho Shin, Seongju Lee +1

Single-domain generalization is essential for object detection, particularly when training models on a single source domain and evaluating them on unseen target domains. Domain shi…

cs.CV2025

3rd Workshop on Maritime Computer Vision (MaCVi) 2025: Challenge Results

Benjamin Kiefer, Lojze Žust, Jon Muhovič +43

The 3rd Workshop on Maritime Computer Vision (MaCVi) 2025 addresses maritime computer vision for Unmanned Surface Vehicles (USV) and underwater. This report offers a comprehensive…

cs.CV20241 cited

SoccerNet 2024 Challenges Results

Anthony Cioppa, Silvio Giancola, Vladimir Somers +81

The SoccerNet 2024 challenges represent the fourth annual video understanding challenges organized by the SoccerNet team. These challenges aim to advance research across multiple t…

cs.CV20224 cited

Teaching Where to Look: Attention Similarity Knowledge Distillation for Low Resolution Face Recognition

Sungho Shin, Joosoon Lee, Junseok Lee +2

Deep learning has achieved outstanding performance for face recognition benchmarks, but performance reduces significantly for low resolution (LR) images. We propose an attention si…

cs.CV2021

Automatic Detection of Injection and Press Mold Parts on 2D Drawing Using Deep Neural Network

Junseok Lee, Jongwon Kim, Jumi Park +3

This paper proposes a method to automatically detect the key feature parts in a CAD of commercial TV and monitor using a deep neural network. We developed a deep learning pipeline…

cs.CV20212 cited

Deep Learning based Food Instance Segmentation using Synthetic Data

D. Park, J. Lee, K. Lee

In the process of intelligently segmenting foods in images using deep neural networks for diet management, data collection and labeling for network training are very important but…