Search Results for author: Seungho Lee

Found 10 papers, 5 papers with code

Self-Supervised Vision Transformers Are Efficient Segmentation Learners for Imperfect Labels

no code implementations23 Jan 2024 Seungho Lee, Seoungyoon Kang, Hyunjung Shim

This study demonstrates a cost-effective approach to semantic segmentation using self-supervised vision transformers (SSVT).

Language Modelling Segmentation +1

Weakly Supervised Semantic Segmentation for Driving Scenes

1 code implementation21 Dec 2023 Dongseob Kim, Seungho Lee, Junsuk Choe, Hyunjung Shim

Notably, the proposed method achieves 51. 8\% mIoU on the Cityscapes test dataset, showcasing its potential as a strong WSSS baseline on driving scene datasets.

Weakly supervised Semantic Segmentation Weakly-Supervised Semantic Segmentation

Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report

2 code implementations7 Nov 2022 Andrey Ignatov, Radu Timofte, Maurizio Denna, Abdel Younes, Ganzorig Gankhuyag, Jingang Huh, Myeong Kyun Kim, Kihwan Yoon, Hyeon-Cheol Moon, Seungho Lee, Yoonsik Choe, Jinwoo Jeong, Sungjei Kim, Maciej Smyl, Tomasz Latkowski, Pawel Kubik, Michal Sokolski, Yujie Ma, Jiahao Chao, Zhou Zhou, Hongfan Gao, Zhengfeng Yang, Zhenbing Zeng, Zhengyang Zhuge, Chenghua Li, Dan Zhu, Mengdi Sun, Ran Duan, Yan Gao, Lingshun Kong, Long Sun, Xiang Li, Xingdong Zhang, Jiawei Zhang, Yaqi Wu, Jinshan Pan, Gaocheng Yu, Jin Zhang, Feng Zhang, Zhe Ma, Hongbin Wang, Hojin Cho, Steve Kim, Huaen Li, Yanbo Ma, Ziwei Luo, Youwei Li, Lei Yu, Zhihong Wen, Qi Wu, Haoqiang Fan, Shuaicheng Liu, Lize Zhang, Zhikai Zong, Jeremy Kwon, Junxi Zhang, Mengyuan Li, Nianxiang Fu, Guanchen Ding, Han Zhu, Zhenzhong Chen, Gen Li, Yuanfan Zhang, Lei Sun, Dafeng Zhang, Neo Yang, Fitz Liu, Jerry Zhao, Mustafa Ayazoglu, Bahri Batuhan Bilecen, Shota Hirose, Kasidis Arunruangsirilert, Luo Ao, Ho Chun Leung, Andrew Wei, Jie Liu, Qiang Liu, Dahai Yu, Ao Li, Lei Luo, Ce Zhu, Seongmin Hong, Dongwon Park, Joonhee Lee, Byeong Hyun Lee, Seunggyu Lee, Se Young Chun, Ruiyuan He, Xuhao Jiang, Haihang Ruan, Xinjian Zhang, Jing Liu, Garas Gendy, Nabil Sabor, Jingchao Hou, Guanghui He

While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints.

Image Super-Resolution

Railroad is not a Train: Saliency as Pseudo-pixel Supervision for Weakly Supervised Semantic Segmentation

1 code implementation CVPR 2021 Seungho Lee, Minhyun Lee, Jongwuk Lee, Hyunjung Shim

Existing studies in weakly-supervised semantic segmentation (WSSS) using image-level weak supervision have several limitations: sparse object coverage, inaccurate object boundaries, and co-occurring pixels from non-target objects.

Object Saliency Detection +2

Evaluation for Weakly Supervised Object Localization: Protocol, Metrics, and Datasets

2 code implementations8 Jul 2020 Junsuk Choe, Seong Joon Oh, Sanghyuk Chun, Seungho Lee, Zeynep Akata, Hyunjung Shim

In this paper, we argue that WSOL task is ill-posed with only image-level labels, and propose a new evaluation protocol where full supervision is limited to only a small held-out set not overlapping with the test set.

Few-Shot Learning Model Selection +1

Evaluating Weakly Supervised Object Localization Methods Right

2 code implementations CVPR 2020 Junsuk Choe, Seong Joon Oh, Seungho Lee, Sanghyuk Chun, Zeynep Akata, Hyunjung Shim

In this paper, we argue that WSOL task is ill-posed with only image-level labels, and propose a new evaluation protocol where full supervision is limited to only a small held-out set not overlapping with the test set.

Few-Shot Learning Model Selection +3

Resource Optimized Neural Architecture Search for 3D Medical Image Segmentation

no code implementations2 Sep 2019 Woong Bae, Seungho Lee, Yeha Lee, Beomhee Park, Minki Chung, Kyu-Hwan Jung

We propose the resource-optimized neural architecture search method which can be applied to 3D medical segmentation tasks in a short training time (1. 39 days for 1GB dataset) using a small amount of computation power (one RTX 2080Ti, 10. 8GB GPU memory).

Image Segmentation Medical Image Segmentation +3

Cooperative Multi-Agent Reinforcement Learning Framework for Scalping Trading

no code implementations31 Mar 2019 Uk Jo, Taehyun Jo, Wanjun Kim, Iljoo Yoon, Dongseok Lee, Seungho Lee

We explore deep Reinforcement Learning(RL) algorithms for scalping trading and knew that there is no appropriate trading gym and agent examples.

Multi-agent Reinforcement Learning reinforcement-learning +1

Multiple profiles sensor-based monitoring and anomaly detection

no code implementations JOURNAL OF QUALITY TECHNOLOGY 2018 Chen Zhang, Hao Yan, Seungho Lee, Jianjun Shi

However, there are several challenges in developing an effective process monitoring system: (i) data streams generated by multiple sensors are high-dimensional profiles; (ii) sensor signals are affected by noise due to system-inherent variations; (iii) signals of different sensors have cluster-wise features; and (iv) an anomaly may cause only sparse changes of sensor signals.

Anomaly Detection

Dynamic Multivariate Functional Data Modeling via Sparse Subspace Learning

no code implementations11 Apr 2018 Chen Zhang, Hao Yan, Seungho Lee, Jianjun Shi

Multivariate functional data from a complex system are naturally high-dimensional and have complex cross-correlation structure.

regression

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