Bert De Brabandere
Computer Vision and Pattern Recognition · 2016–2019
A researcher in the Science corpus: 8 papers; trusts 1,098 researchers and is trusted by 1,029 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 201 citations into it; #1,339 of 21,575 by trust here, #2,263 by citations
- Artificial Intelligence — 104 citations into it; #18,510 of 35,345 by trust here, #22,463 by citations
- Model Architectures — 44 citations into it
- Representation & Self-Supervised Learning — 23 citations into it
- Optimization & Training — 16 citations into it
- Media Technology — 12 citations into it; #722 of 3,621 by trust here, #872 by citations
- Efficiency & Compression — 11 citations into it
- Environmental Engineering — 11 citations into it; #1,950 of 5,479 by trust here, #2,025 by citations
Whom Bert De Brabandere trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Alex Krizhevsky
- Geoffrey E. Hinton
- Yann LeCun
- Ross B. Girshick
- Karen Simonyan
- Yoshua Bengio
- Diederik P. Kingma
- Ilya Sutskever
- Andrew Zisserman
Who trusts Bert De Brabandere overall
- Daniel Bolya
- Huiyu Wang
- Xinlong Wang
- Liang-Chieh Chen
- Davy Neven
- Ujwal D. Bonde
- Luc Van Gool
- Lizhe Liu
- Yukun Zhu
- Chunhua Shen
Papers from Bert De Brabandere's perspective
Ordered by how strongly the researchers Bert De Brabandere trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Dynamic Filter Networks (2016) — 1,102 citations
- Semantic Instance Segmentation with a Discriminative Loss Function (2017) — 479 citations
- Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth (2019) — 256 citations
- Branched Multi-Task Networks: Deciding what layers to share (2019) — 163 citations
- Fast Scene Understanding for Autonomous Driving (2017) — 77 citations
- Towards End-to-End Lane Detection: an Instance Segmentation Approach (2018) — 804 citations
- Energy-efficient ConvNets through approximate computing (2016) — 105 citations
- Dynamic Steerable Blocks in Deep Residual Networks (2017) — 28 citations