no code implementations • 22 Nov 2022 • Minki Jeong, Changick Kim
The imbalanced data form a biased feature space, which deteriorates the performance of the recognition model.
Ranked #19 on
Long-tail Learning
on CIFAR-100-LT (ρ=10)
no code implementations • 16 Jul 2022 • Minki Jeong, Wanyeong Jung
Furthermore, a MAC-DO array efficiently reuses three types of data (input, weight and output), minimizing data movement.
1 code implementation • 25 Jan 2022 • Sangmin Woo, Jinyoung Park, Inyong Koo, Sumin Lee, Minki Jeong, Changick Kim
To our surprise, we found that training schedule shows divide-and-conquer-like pattern: time segments are first diversified regardless of the target, then coupled with each target, and fine-tuned to the target again.
no code implementations • 25 May 2021 • Inyong Koo, Minki Jeong, Changick Kim
In this work, we propose a novel framework that generates class representations by extracting features from class-relevant regions of the images.
1 code implementation • CVPR 2021 • Minki Jeong, Seokeon Choi, Changick Kim
Based on the transformation consistency, our method measures the difference between the transformed prototypes and a modified prototype set.
1 code implementation • CVPR 2021 • Seokeon Choi, Taekyung Kim, Minki Jeong, Hyoungseob Park, Changick Kim
To this end, we combine learnable batch-instance normalization layers with meta-learning and investigate the challenging cases caused by both batch and instance normalization layers.
no code implementations • 1 Aug 2019 • Jaehoon Choi, Minki Jeong, Taekyung Kim, Changick Kim
To learn target discriminative representations, using pseudo-labels is a simple yet effective approach for unsupervised domain adaptation.
no code implementations • CVPR 2019 • Taekyung Kim, Minki Jeong, Seunghyeon Kim, Seokeon Choi, Changick Kim
We construct a structured domain adaptation framework for our learning paradigm and introduce a practical way of DD for implementation.
1 code implementation • 17 Oct 2017 • Li Yi, Lin Shao, Manolis Savva, Haibin Huang, Yang Zhou, Qirui Wang, Benjamin Graham, Martin Engelcke, Roman Klokov, Victor Lempitsky, Yuan Gan, Pengyu Wang, Kun Liu, Fenggen Yu, Panpan Shui, Bingyang Hu, Yan Zhang, Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Minki Jeong, Jaehoon Choi, Changick Kim, Angom Geetchandra, Narasimha Murthy, Bhargava Ramu, Bharadwaj Manda, M. Ramanathan, Gautam Kumar, P Preetham, Siddharth Srivastava, Swati Bhugra, Brejesh lall, Christian Haene, Shubham Tulsiani, Jitendra Malik, Jared Lafer, Ramsey Jones, Siyuan Li, Jie Lu, Shi Jin, Jingyi Yu, Qi-Xing Huang, Evangelos Kalogerakis, Silvio Savarese, Pat Hanrahan, Thomas Funkhouser, Hao Su, Leonidas Guibas
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database.