Berk Çalli
Computer Vision and Pattern Recognition · 2015–2021
A researcher in the Science corpus: 5 papers; trusts 63 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 84 citations into it; #16,814 of 21,575 by trust here, #9,563 by citations
- Artificial Intelligence — 31 citations into it
- Aerospace Engineering — 29 citations into it
- Robotics & Control — 22 citations into it
- Evaluation & Benchmarks — 20 citations into it; #2,310 of 3,147 by trust here, #1,764 by citations
- Control and Systems Engineering — 15 citations into it; #3,971 of 8,257 by trust here, #759 by citations
- 3D Vision & Pose — 10 citations into it
- Geology — 9 citations into it
Whom Berk Çalli trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Rosen Diankov
- Morgan Quigley
- Arjun Singh
- Corey Goldfeder
- Ioan Alexandru Sucan
- M. Kazhdan
- Michael Garland
- James J. Kuffner Jr.
- Siddhartha S. Srinivasa
- Dmitry Berenson
Who trusts Berk Çalli overall
- Douglas Morrison
- Joshua Tobin
- Yu Xiang
- Adithyavairavan Murali
- Dhruv Batra
- Dieter Fox
- Priyanka Mandikal
- Martin Rünz
- Chris Paxton
- Peter Corke
Papers from Berk Çalli's perspective
Ordered by how strongly the researchers Berk Çalli 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
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Distinctive Image Features from Scale-Invariant Keypoints
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Yale-CMU-Berkeley dataset for robotic manipulation research (2017) — 453 citations
- Benchmarking in Manipulation Research: Using the Yale-CMU-Berkeley Object and Model Set (2015) — 600 citations
- Benchmarking in Manipulation Research: The YCB Object and Model Set and Benchmarking Protocols (2015) — 326 citations
- ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenes (2021) — 90 citations
- The YCB object and Model set: Towards common benchmarks for manipulation research (2015) — 1,019 citations