Adriana Kovashka

Computer Vision and Pattern Recognition · with Grauman · 2010–2018

A researcher in the Science corpus: 7 papers; trusts 1,168 researchers and is trusted by 546 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 238 citations into it; #1,810 of 21,575 by trust here, #4,726 by citations
• Artificial Intelligence — 126 citations into it; #5,016 of 35,345 by trust here, #12,244 by citations
• Evaluation & Benchmarks — 37 citations into it
• Representation & Self-Supervised Learning — 27 citations into it
• Multimodal Learning — 24 citations into it
• Natural Language Processing — 20 citations into it
• Generative Models — 13 citations into it; #1,649 of 1,956 by trust here, #1,946 by citations
• Media Technology — 11 citations into it

Whom Adriana Kovashka trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Ruslan Salakhutdinov
5. Diederik P. Kingma
6. David G. Lowe
7. Yoshua Bengio
8. Ross B. Girshick
9. Ivan Laptev
10. Andrew Zisserman

Who trusts Adriana Kovashka overall

1. Tian Lan
2. Limin Wang
3. Tal Hassner
4. Anan Liu
5. Orit Kliper-Gross
6. Kristen Grauman
7. Genevieve Patterson
8. Sreemanananth Sadanand
9. Yu Qiao
10. Heng Wang

Papers from Adriana Kovashka's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• WhittleSearch: Image search with relative attribute feedback (2012) — 350 citations
• Learning a hierarchy of discriminative space-time neighborhood features for human action recognition (2010) — 565 citations
• Crowdsourcing in Computer Vision (2016) — 168 citations
• Automatic Understanding of Image and Video Advertisements (2017) — 200 citations
• Discovering Attribute Shades of Meaning with the Crowd (2015) — 40 citations
• Persuasive Faces: Generating Faces in Advertisements (2018) — 10 citations
• Equal But Not The Same: Understanding the Implicit Relationship Between Persuasive Images and Text (2018) — 61 citations