Jungong Han

Computer Vision and Pattern Recognition · 2015–2024

A researcher in the Science corpus: 14 papers; trusts 1,426 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 495 citations into it; #5,391 of 21,575 by trust here, #4,344 by citations
• Artificial Intelligence — 240 citations into it; #8,178 of 35,345 by trust here, #8,157 by citations
• Model Architectures — 126 citations into it; #1,732 of 2,278 by trust here, #1,693 by citations
• Aerospace Engineering — 58 citations into it
• Media Technology — 57 citations into it; #2,402 of 3,621 by trust here, #1,171 by citations
• Efficiency & Compression — 56 citations into it
• Optimization & Training — 48 citations into it
• Representation & Self-Supervised Learning — 35 citations into it

Whom Jungong Han trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Karen Simonyan
5. Yoshua Bengio
6. Ross B. Girshick
7. Yann LeCun
8. Christian Szegedy
9. Ilya Sutskever
10. Andrew Zisserman

Who trusts Jungong Han overall

1. Pavan Kumar Anasosalu Vasu
2. Chien-Yao Wang
3. Xiaohan Ding
4. Gong Cheng
5. J. Gabriel
6. Ao Wang
7. Alexey Bochkovskiy
8. Chang-Bin Zhang
9. X. Zhang
10. Jianqiang Xiao

Papers from Jungong Han's perspective

Ordered by how strongly the researchers Jungong Han 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks (2019) — 870 citations
• RepVGG: Making VGG-style ConvNets Great Again (2021) — 2,415 citations
• Auto-Balanced Filter Pruning for Efficient Convolutional Neural Networks (2018) — 127 citations
• Scaling Up Your Kernels to 31×31: Revisiting Large Kernel Design in CNNs (2022) — 781 citations
• IMRAM: Iterative Matching With Recurrent Attention Memory for Cross-Modal Image-Text Retrieval (2020) — 395 citations
• Cosaliency Detection Based on Intrasaliency Prior Transfer and Deep Intersaliency Mining (2016) — 158 citations
• Sparse representation based multi-sensor image fusion for multi-focus and multi-modality images: A review (2018) — 446 citations
• YOLOv10: Real-Time End-to-End Object Detection (2024) — 5,041 citations
• Diverse Branch Block: Building a Convolution as an Inception-like Unit (2021) — 436 citations
• Deep Attentive Video Summarization With Distribution Consistency Learning (2020) — 83 citations
• Incremental Few-Shot Learning for Pedestrian Attribute Recognition (2019) — 33 citations
• Gabor Convolutional Networks (2017) — 395 citations
• RGB-D datasets using microsoft kinect or similar sensors: a survey (2016) — 145 citations
• Auto-encoder-based shared mid-level visual dictionary learning for scene classification using very high resolution remote sensing images (2015) — 63 citations