Wheemyung Shin — accepted AI-conference papers

2022

GLAMD: Global and Local Attention Mask Distillation for Object Detectors

Younho Jang, Wheemyung Shin, Jinbeom Kim, Simon Woo, Sung-Ho Bae

ECCV 2022poster

"Knowledge distillation (KD) is a well-known model compression strategy to improve models’ performance with fewer parameters. However, recent KD approaches for object detection have faced two limitations. First, they distill nearby foreground regions, ignoring potentially useful background informati…

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2021

SaliencyMix: A Saliency Guided Data Augmentation Strategy for Better Regularization

A F M Shahab Uddin, Mst. Sirazam Monira, Wheemyung Shin, TaeChoong Chung, Sung-Ho Bae

ICLR 2021poster

Advanced data augmentation strategies have widely been studied to improve the generalization ability of deep learning models. Regional dropout is one of the popular solutions that guides the model to focus on less discriminative parts by randomly removing image regions, resulting in improved regular…

Cited by 246 Source ↗PDF ↗code ↗Scholar ↗