Mennatullah Siam
Computer Vision and Pattern Recognition · 2016–2019
A researcher in the Science corpus: 8 papers; trusts 1,081 researchers and is trusted by 11 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 179 citations into it; #10,076 of 21,575 by trust here, #8,502 by citations
- Artificial Intelligence — 68 citations into it; #16,054 of 35,345 by trust here, #15,542 by citations
- Model Architectures — 27 citations into it
- Evaluation & Benchmarks — 26 citations into it
- Representation & Self-Supervised Learning — 22 citations into it
- Aerospace Engineering — 18 citations into it; #5,779 of 6,421 by trust here, #6,012 by citations
- Media Technology — 10 citations into it
- Biophysics — 7 citations into it
Whom Mennatullah Siam trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Karen Simonyan
- Geoffrey E. Hinton
- Ross B. Girshick
- Kaiming He
- Alex Krizhevsky
- Andrew Zisserman
- Diederik P. Kingma
- Yann LeCun
- Yoshua Bengio
- Evan Shelhamer
Who trusts Mennatullah Siam overall
- Khaled El Madawy
- Yongfei Liu
- Sangyong Park
- Senthil Kumar Yogamani
- Juhong Min
- Ali Ayub
- Wenguan Wang
- Chunbo Lang
- Boyi Li
- Fabio Cermelli
Papers from Mennatullah Siam's perspective
Ordered by how strongly the researchers Mennatullah Siam 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
- AMP: Adaptive Masked Proxies for Few-Shot Segmentation (2019) — 213 citations
- Video Object Segmentation using Teacher-Student Adaptation in a Human Robot Interaction (HRI) Setting (2018) — 80 citations
- Deep semantic segmentation for automated driving: Taxonomy, roadmap and challenges (2017) — 181 citations
- Adaptive Masked Weight Imprinting for Few-Shot Segmentation (2019) — 35 citations
- MODNet: Motion and Appearance based Moving Object Detection Network for Autonomous Driving (2018) — 92 citations
- Online Object and Task Learning via Human Robot Interaction (2018) — 33 citations
- RTSeg: Real-Time Semantic Segmentation Comparative Study (2018) — 121 citations
- Parking-stall vacancy indicator system, based on deep convolutional neural networks (2016) — 86 citations