Chang-Dong Yoo

Computer Vision and Pattern Recognition · 2007–2022

A researcher in the Science corpus: 12 papers; trusts 958 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 283 citations into it; #11,155 of 21,575 by trust here, #7,852 by citations
• Artificial Intelligence — 254 citations into it; #32,391 of 35,345 by trust here, #10,566 by citations
• Representation & Self-Supervised Learning — 73 citations into it
• Meta & Continual Learning — 57 citations into it
• Multimodal Learning — 39 citations into it
• Natural Language Processing — 39 citations into it
• Computational Mechanics — 32 citations into it
• Model Architectures — 31 citations into it

Whom Chang-Dong Yoo trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Alex Krizhevsky
4. Kaiming He
5. Yoshua Bengio
6. Ross B. Girshick
7. Yann LeCun
8. Ilya Sutskever
9. Jimmy Ba
10. Shaoqing Ren

Who trusts Chang-Dong Yoo overall

1. Xiaolong Li
2. Y. Shi
3. Yongjian Hu
4. Eduardo Fonseca
5. Vasiliy Sachnev

Papers from Chang-Dong Yoo's perspective

Ordered by how strongly the researchers Chang-Dong Yoo trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Modality Shifting Attention Network for Multi-Modal Video Question Answering (2020) — 80 citations
• Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution (2019) — 209 citations
• Dual Temperature Helps Contrastive Learning Without Many Negative Samples: Towards Understanding and Simplifying MoCo (2022) — 74 citations
• Progressive Attention Memory Network for Movie Story Question Answering (2019) — 80 citations
• Edge-Labeling Graph Neural Network for Few-Shot Learning (2019) — 529 citations
• Flexible Piezoelectric Acoustic Sensors and Machine Learning for Speech Processing (2019) — 307 citations
• CNN-Based Learnable Gammatone Filterbank and Equal-Loudness Normalization for Environmental Sound Classification (2020) — 50 citations
• On the Soft-Subnetwork for Few-shot Class Incremental Learning (2022) — 65 citations
• Forget-free Continual Learning with Winning Subnetworks (2022) — 173 citations
• SoftGroup for 3D Instance Segmentation on Point Clouds (2022) — 350 citations
• Gaining Extra Supervision via Multi-task learning for Multi-Modal Video Question Answering (2019) — 28 citations
• Reversible Image Watermarking Based on Integer-to-Integer Wavelet Transform (2007) — 448 citations