Dongliang He
Computer Vision and Pattern Recognition · 2018–2021
A researcher in the Science corpus: 7 papers; trusts 802 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 226 citations into it; #19,688 of 21,575 by trust here, #8,831 by citations
- Artificial Intelligence — 100 citations into it; #32,139 of 35,345 by trust here, #14,181 by citations
- Model Architectures — 38 citations into it
- Evaluation & Benchmarks — 23 citations into it
- Representation & Self-Supervised Learning — 21 citations into it
- Media Technology — 16 citations into it
- Optimization & Training — 14 citations into it
- Endocrinology, Diabetes and Metabolism — 11 citations into it
Whom Dongliang He trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Karen Simonyan
- Andrew Zisserman
- Geoffrey E. Hinton
- Ross B. Girshick
- Alex Krizhevsky
- Diederik P. Kingma
- Yoshua Bengio
- Christian Szegedy
- Yann LeCun
Who trusts Dongliang He overall
- Wenhao Wu
- Limin Wang
- Zhaofan Qiu
- Christoph Feichtenhofer
- Zhaoyang Liu
- Yan Li
- Hengduo Li
- Yulin Wang
- Amir Ghodrati
- Guillaume Vaudaux-Ruth
Papers from Dongliang He's perspective
Ordered by how strongly the researchers Dongliang He trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Multi-Agent Reinforcement Learning Based Frame Sampling for Effective Untrimmed Video Recognition (2019) — 135 citations
- Exploiting Spatial-Temporal Modelling and Multi-Modal Fusion for Human Action Recognition (2018) — 19 citations
- Dynamic Inference: A New Approach Toward Efficient Video Action Recognition (2020) — 37 citations
- StNet: Local and Global Spatial-Temporal Modeling for Action Recognition (2018) — 145 citations
- RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning (2020) — 143 citations
- AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer (2021) — 442 citations
- Image Inpainting by End-to-End Cascaded Refinement With Mask Awareness (2021) — 140 citations