Iro Laina

Computer Vision and Pattern Recognition · 2016–2023

A researcher in the Science corpus: 6 papers; trusts 779 researchers and is trusted by 143 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 213 citations into it; #2,946 of 21,575 by trust here, #3,538 by citations
• Artificial Intelligence — 67 citations into it
• Generative Models — 55 citations into it
• Multimodal Learning — 47 citations into it
• Aerospace Engineering — 28 citations into it; #3,107 of 6,421 by trust here, #2,193 by citations
• 3D Vision & Pose — 19 citations into it
• Model Architectures — 18 citations into it
• Evaluation & Benchmarks — 17 citations into it

Whom Iro Laina trusts overall

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

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

Who trusts Iro Laina overall

1. Xinjing Cheng
2. Dan Xu
3. Zhengqi Li
4. Daniel Gordon
5. Chunhua Shen
6. Fangchang Ma
7. Ibraheem Alhashim
8. Zhenyu Zhang
9. Vladimir Nekrasov
10. Yinda Zhang

Papers from Iro Laina's perspective

Ordered by how strongly the researchers Iro Laina trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Deeper Depth Prediction with Fully Convolutional Residual Networks (2016) — 2,021 citations
• CNN-SLAM: Real-Time Dense Monocular SLAM with Learned Depth Prediction (2017) — 781 citations
• RealFusion 360° Reconstruction of Any Object from a Single Image (2023) — 376 citations
• Guide Me: Interacting with Deep Networks (2018) — 39 citations
• 2017 Robotic Instrument Segmentation Challenge (2019) — 310 citations
• Training-Free Layout Control with Cross-Attention Guidance (2023) — 373 citations