Kiana Ehsani

Computer Vision and Pattern Recognition · 2017–2024

A researcher in the Science corpus: 11 papers; trusts 950 researchers and is trusted by 156 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 383 citations into it; #2,863 of 21,575 by trust here, #2,423 by citations
• Artificial Intelligence — 220 citations into it; #13,790 of 35,345 by trust here, #4,682 by citations
• Reinforcement Learning — 78 citations into it; #1,742 of 2,242 by trust here, #535 by citations
• Evaluation & Benchmarks — 67 citations into it
• Aerospace Engineering — 64 citations into it
• Control and Systems Engineering — 59 citations into it
• Robotics & Control — 59 citations into it; #591 of 609 by trust here, #442 by citations
• Representation & Self-Supervised Learning — 57 citations into it; #2,951 of 3,145 by trust here, #2,404 by citations

Whom Kiana Ehsani trusts overall

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

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

Who trusts Kiana Ehsani overall

1. S. Gadre
2. Luca Weihs
3. Roozbeh Mottaghi
4. Matt Deitke
5. Matthew Wallingford
6. Sam Powers
7. Apoorv Khandelwal
8. Xin Ye
9. Aniruddha Kembhavi
10. Changan Chen

Papers from Kiana Ehsani's perspective

Ordered by how strongly the researchers Kiana Ehsani 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
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

• AI2-THOR: An Interactive 3D Environment for Visual AI (2017) — 1,486 citations
• ManipulaTHOR: A Framework for Visual Object Manipulation (2021) — 164 citations
• Watching the World Go By: Representation Learning from Unlabeled Videos (2020) — 96 citations
• Objaverse: A Universe of Annotated 3D Objects (2022) — 1,762 citations
• Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning (2018) — 264 citations
• ProcTHOR: Large-Scale Embodied AI Using Procedural Generation (2022) — 516 citations
• Continuous Scene Representations for Embodied AI (2022) — 62 citations
• Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Multimodal Models (2024) — 410 citations
• Who Let the Dogs Out? Modeling Dog Behavior from Visual Data (2018) — 63 citations
• Objaverse-XL: A Universe of 10M+ 3D Objects (2023) — 873 citations
• Act the Part: Learning Interaction Strategies for Articulated Object Part Discovery (2021) — 65 citations