Makarand Tapaswi
Computer Vision and Pattern Recognition · 2012–2022
A researcher in the Science corpus: 8 papers; trusts 1,088 researchers and is trusted by 1,375 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 232 citations into it; #1,114 of 21,575 by trust here, #2,001 by citations
- Artificial Intelligence — 155 citations into it; #9,908 of 35,345 by trust here, #5,084 by citations
- Multimodal Learning — 83 citations into it; #173 of 1,211 by trust here, #197 by citations
- Natural Language Processing — 61 citations into it
- Representation & Self-Supervised Learning — 41 citations into it; #1,292 of 3,145 by trust here, #801 by citations
- Reinforcement Learning — 29 citations into it
- Evaluation & Benchmarks — 25 citations into it
- Control and Systems Engineering — 18 citations into it
Whom Makarand Tapaswi trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Geoffrey E. Hinton
- Alex Krizhevsky
- Diederik P. Kingma
- Yoshua Bengio
- Andrew Zisserman
- Ross B. Girshick
- Ilya Sutskever
- Yann LeCun
- Karen Simonyan
Who trusts Makarand Tapaswi overall
- Antoine Miech
- Arsha Nagrani
- Jie Lei
- Ivan Laptev
- Max Bain
- Anna Rohrbach
- Andrew Zisserman
- Rohit Girdhar
- Zhou Zhao
- Omkar M. Parkhi
Papers from Makarand Tapaswi's perspective
Ordered by how strongly the researchers Makarand Tapaswi 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
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips (2019) — 1,493 citations
- MovieQA: Understanding Stories in Movies through Question-Answering (2015) — 830 citations
- “Knock! Knock! Who is it?” probabilistic person identification in TV-series (2012) — 145 citations
- Book2Movie: Aligning video scenes with book chapters (2015) — 93 citations
- Instruction-driven history-aware policies for robotic manipulations (2022) — 153 citations
- Aligning plot synopses to videos for story-based retrieval (2015) — 36 citations
- Deep Multimodal Feature Encoding for Video Ordering (2020) — 11 citations
- Visual Reasoning by Progressive Module Networks (2018) — 14 citations