Anuj Srivastava
Computer Vision and Pattern Recognition · 2003–2018
A researcher in the Science corpus: 13 papers; trusts 230 researchers and is trusted by 49 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 112 citations into it; #4,031 of 21,575 by trust here, #4,690 by citations
- Human-Computer Interaction — 39 citations into it; #1,839 of 2,253 by trust here, #1,161 by citations
- Artificial Intelligence — 33 citations into it; #13,391 of 35,345 by trust here, #13,544 by citations
- Biomedical Engineering — 28 citations into it; #1,723 of 10,734 by trust here, #4,314 by citations
- 3D Vision & Pose — 17 citations into it; #634 of 689 by trust here, #558 by citations
- Geometry and Topology — 16 citations into it; #630 of 7,422 by trust here, #494 by citations
- Computational Mechanics — 15 citations into it; #5,642 of 10,001 by trust here, #5,813 by citations
- Representation & Self-Supervised Learning — 12 citations into it; #2,367 of 3,145 by trust here, #2,404 by citations
Whom Anuj Srivastava trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Hermann Karcher
- Eric Klassen
- David L. Donoho
- Huiling Le
- Thomas B. Sebastian
- Omar Oreifej
- Zicheng Liu
- Shantanu H. Joshi
- Xiaodong Yang
- Wanqing Li
Who trusts Anuj Srivastava overall
- Jingyong Su
- Boulbaba Ben Amor
- Dean C. Adams
- C. Klingenberg
- Julien Claude
- Victor M. Panaretos
- Yoav Zemel
- Amor Ben Tanfous
- P. Gunz
- P. Mitteroecker
Papers from Anuj Srivastava's perspective
Ordered by how strongly the researchers Anuj Srivastava trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Going deeper with convolutions
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Statistical Computations on Grassmann and Stiefel Manifolds for Image and Video-Based Recognition (2011) — 402 citations
- Analysis of planar shapes using geodesic paths on shape spaces (2004) — 532 citations
- Statistical Shape Analysis (2014) — 1,081 citations
- Statistical shape analysis: clustering, learning, and testing (2005) — 409 citations
- Accurate 3D action recognition using learning on the Grassmann manifold (2015) — 200 citations
- Elastic functional coding of human actions: From vector-fields to latent variables (2015) — 96 citations
- Action Recognition Using Rate-Invariant Analysis of Skeletal Shape Trajectories (2016) — 215 citations
- Statistical analysis of trajectories on Riemannian manifolds: Bird migration, hurricane tracking and video surveillance (2014) — 132 citations
- Are generalized spillover indices overstating connectedness? (2018) — 56 citations
- Elastic Functional Coding of Riemannian Trajectories (2016) — 54 citations
- Rate-Invariant Analysis of Trajectories on Riemannian Manifolds with Application in Visual Speech Recognition (2014) — 46 citations
- Shape Analysis of Elastic Curves in Euclidean Spaces (2011) — 672 citations
- Analysis of a frictional-kinetic model for gas-particle flow (2003) — 388 citations