Jinbeom Kim — 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…

Cited by 10 Source ↗PDF ↗Scholar ↗

2021

VFP290K: A Large-Scale Benchmark Dataset for Vision-based Fallen Person Detection

Jaeju An, Jeongho Kim, Hanbeen Lee, Jinbeom Kim, Junhyung Kang, Minha Kim +3

NeurIPS 2021poster

Detection of fallen persons due to, for example, health problems, violence, or accidents, is a critical challenge. Accordingly, detection of these anomalous events is of paramount importance for a number of applications, including but not limited to CCTV surveillance, security, and health care. Give…

Cited by 20 Source ↗Scholar ↗