Radhakrishna Achanta

Computer Vision and Pattern Recognition · 2008–2018

A researcher in the Science corpus: 8 papers; trusts 748 researchers and is trusted by 851 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 128 citations into it; #1,477 of 21,575 by trust here, #1,510 by citations
• Artificial Intelligence — 19 citations into it; #20,573 of 35,345 by trust here, #30,347 by citations
• Media Technology — 16 citations into it; #1,395 of 3,621 by trust here, #180 by citations
• Biophysics — 9 citations into it
• Aerospace Engineering — 8 citations into it
• Evaluation & Benchmarks — 8 citations into it
• Optimization & Training — 7 citations into it
• Computational Mechanics — 5 citations into it

Whom Radhakrishna Achanta trusts overall

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

1. Jitendra Malik
2. Andrea Vedaldi
3. David G. Lowe
4. Kaiming He
5. Pedro F. Felzenszwalb
6. Jianbo Shi
7. David R. Martin
8. Geoffrey E. Hinton
9. Alex Krizhevsky
10. Karen Simonyan

Who trusts Radhakrishna Achanta overall

1. Ming-Ming Cheng
2. Wenguan Wang
3. Huchuan Lu
4. Deng-Ping Fan
5. Junwei Han
6. Dingwen Zhang
7. Ali Borji
8. Guanbin Li
9. Jianbing Shen
10. Houwen Peng

Papers from Radhakrishna Achanta's perspective

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

Their papers

• SLIC Superpixels Compared to State-of-the-Art Superpixel Methods (2012) — 9,160 citations
• Frequency-tuned salient region detection (2009) — 4,513 citations
• SLIC Superpixels ? (2010)
• Supervoxel-Based Segmentation of Mitochondria in EM Image Stacks With Learned Shape Features (2012) — 295 citations
• Salient Region Detection and Segmentation (2008) — 976 citations
• Saliency detection using maximum symmetric surround (2010) — 416 citations
• Deep Feature Factorization For Concept Discovery (2018) — 111 citations
• Superpixels and Polygons Using Simple Non-iterative Clustering (2017) — 412 citations