no code implementations • 19 May 2023 • Mingle Xu, Hyongsuk Kim, Jucheng Yang, Alvaro Fuentes, Yao Meng, Sook Yoon, Taehyun Kim, Dong Sun Park
We believe that our paper sheds light on the importance of embracing poor datasets, enhances the understanding of the associated challenges, and contributes to the ambitious objective of deploying deep learning in real-world applications.
1 code implementation • 17 May 2023 • Talha Ilyas, Dewa Made Sri Arsa, Khubaib Ahmad, Yong Chae Jeong, Okjae Won, Jong Hoon Lee, Hyongsuk Kim
To address these issues, we present the CWD30 dataset, a large-scale, diverse, holistic, and hierarchical dataset tailored for crop-weed recognition tasks in precision agriculture.
no code implementations • 25 Jan 2023 • Mingle Xu, Jaehwan Lee, Sook Yoon, Hyongsuk Kim, Dong Sun Park
Semantic image synthesis (SIS) aims to produce photorealistic images aligning to given conditional semantic layout and has witnessed a significant improvement in recent years.
1 code implementation • Elsevier Neural Networks 2022 • Talha Ilyas, Zubaer Ibna Mannan, Abbas Khan, Sami Azam, Hyongsuk Kim, Friso De Boer
Nuclei segmentation and classification of hematoxylin and eosin-stained histology images is a challenging task due to a variety of issues, such as color inconsistency that results from the non-uniform manual staining operations, clustering of nuclei, and blurry and overlapping nuclei boundaries.
Ranked #2 on
Panoptic Segmentation
on PanNuke
no code implementations • 10 Dec 2019 • Mohammad Ibrahim Sarker, Hyongsuk Kim, Denis Tarasov, Dinar Akhmetzanov
This paper presents results of applying Inception v4 deep convolutional neural network to ICIAR-2018 Breast Cancer Classification Grand Challenge, part a.
no code implementations • 5 Jun 2019 • Mohammad Ibrahim Sarker, Zubaer Ibna Mannan, Hyongsuk Kim
In this paper, we proposed a methodology by combining the RWC and GA, namely Genetic Random Weight Change (GRWC), as well as demonstrate a seminal way to reduce the complexity of the neural network by removing weak weights of GRWC.
no code implementations • 5 Jun 2019 • Mohammad Ibrahim Sarker, Heechan Yang, Hyongsuk Kim
Based on results of experiments conducted with several state-of-the-art models adopted by CNN, a region-based method has been proposed as a faster and more accurate method of corn leaf detection.
no code implementations • 5 Jun 2019 • Mohammad Ibrahim Sarker, Hyongsuk Kim
Being motivated with such unique attributes of ResNet, this paper evaluates the performance of fine-tuned ResNet for object classification of our weeds dataset.
no code implementations • 5 Jun 2019 • Mohammad Ibraim Sarker, Yali Nie, Hong Yongki, Hyongsuk Kim
A new method to improve the performance of Random weight change (RWC) algorithm based on a simple genetic algorithm, namely, Genetic random weight change (GRWC) is proposed.