Fadime Sener

Computer Vision and Pattern Recognition · 2012–2022

A researcher in the Science corpus: 5 papers; trusts 926 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 146 citations into it; #9,192 of 21,575 by trust here, #10,677 by citations
• Artificial Intelligence — 86 citations into it; #23,043 of 35,345 by trust here, #25,809 by citations
• Natural Language Processing — 28 citations into it
• Evaluation & Benchmarks — 26 citations into it
• Representation & Self-Supervised Learning — 15 citations into it
• Biomedical Engineering — 11 citations into it
• Model Architectures — 11 citations into it
• Control and Systems Engineering — 8 citations into it

Whom Fadime Sener trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Karen Simonyan
5. Alex Krizhevsky
6. Andrew Zisserman
7. David G. Lowe
8. Yoshua Bengio
9. Ross B. Girshick
10. Jimmy Ba

Who trusts Fadime Sener overall

1. Guo-qing Guo
2. Ivan Rodin
3. Antonino Furnari
4. Paul Hongsuck Seo
5. Andrew Rouditchenko
6. Francesco Ragusa
7. Wenlong Huang
8. Alice Lai
9. Enxin Song
10. Giovanni Maria Farinella

Papers from Fadime Sener's perspective

Ordered by how strongly the researchers Fadime Sener 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Gradient-based learning applied to document recognition
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Zero-Shot Anticipation for Instructional Activities (2018) — 75 citations
• Unsupervised Learning and Segmentation of Complex Activities from Video (2018) — 126 citations
• Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural Activities (2022) — 379 citations
• On Recognizing Actions in Still Images via Multiple Features (2012) — 36 citations
• Temporal Aggregate Representations for Long-Range Video Understanding (2020) — 180 citations