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20172022
most citedLarge-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55

53 citations · 133 across the 19 of their papers we have counts for

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28 papers · 1 filter

cs.CV20221 cited

RainUNet for Super-Resolution Rain Movie Prediction under Spatio-temporal Shifts

Jinyoung Park, Minseok Son, Seungju Cho +2

This paper presents a solution to the Weather4cast 2022 Challenge Stage 2. The goal of the challenge is to forecast future high-resolution rainfall events obtained from ground rada…

cs.CV20222 cited

Supervised Contrastive Learning on Blended Images for Long-tailed Recognition

Minki Jeong, Changick Kim

Real-world data often have a long-tailed distribution, where the number of samples per class is not equal over training classes. The imbalanced data form a biased feature space, wh…

cs.CV2021

Geometrically Adaptive Dictionary Attack on Face Recognition

Junyoung Byun, Hyojun Go, Changick Kim

CNN-based face recognition models have brought remarkable performance improvement, but they are vulnerable to adversarial perturbations. Recent studies have shown that adversaries…

cs.CV2021

Residual-Guided Learning Representation for Self-Supervised Monocular Depth Estimation

Byeongjun Park, Taekyung Kim, Hyojun Go +1

Photometric consistency loss is one of the representative objective functions commonly used for self-supervised monocular depth estimation. However, this loss often causes unstable…

cs.CV2021

DnD: Dense Depth Estimation in Crowded Dynamic Indoor Scenes

Dongki Jung, Jaehoon Choi, Yonghan Lee +4

We present a novel approach for estimating depth from a monocular camera as it moves through complex and crowded indoor environments, e.g., a department store or a metro station. O…

cs.CV20211 cited

Improving Few-shot Learning with Weakly-supervised Object Localization

Inyong Koo, Minki Jeong, Changick Kim

Few-shot learning often involves metric learning-based classifiers, which predict the image label by comparing the distance between the extracted feature vector and class represent…