Anurag Arnab
Computer Vision and Pattern Recognition · with Dehghani · 2021–2023
A researcher in the Science corpus: 12 papers; trusts 1,492 researchers and is trusted by 119 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 551 citations into it; #3,111 of 21,575 by trust here, #2,415 by citations
- Artificial Intelligence — 444 citations into it; #13,072 of 35,345 by trust here, #3,797 by citations
- Model Architectures — 141 citations into it
- Multimodal Learning — 100 citations into it
- Evaluation & Benchmarks — 87 citations into it
- Natural Language Processing — 52 citations into it
- Large Language Models — 50 citations into it
- Efficiency & Compression — 48 citations into it
Whom Anurag Arnab trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Ross B. Girshick
- Geoffrey E. Hinton
- Alex Krizhevsky
- Karen Simonyan
- Andrew Zisserman
- Diederik P. Kingma
- Yoshua Bengio
- Ilya Sutskever
- Yann LeCun
Who trusts Anurag Arnab overall
- Mostafa Dehghani
- Kunchang Li
- Shen Yan
- Rohit Girdhar
- Arsha Nagrani
- Zehua Sun
- Alexey A. Gritsenko
- Yi Tay
- Haoqi Fan
- Matthias Minderer
Papers from Anurag Arnab's perspective
Ordered by how strongly the researchers Anurag Arnab 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)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- 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
- ViViT: A Video Vision Transformer (2021) — 3,120 citations
- Scaling Vision Transformers to 22 Billion Parameters (2023) — 911 citations
- Simple Open-Vocabulary Object Detection with Vision Transformers (2022) — 421 citations
- Multiview Transformers for Video Recognition (2022) — 292 citations
- SCENIC: A JAX Library for Computer Vision Research and Beyond (2021) — 78 citations
- TokenLearner: What Can 8 Learned Tokens Do for Images and Videos? (2021) — 181 citations
- End-to-end Generative Pretraining for Multimodal Video Captioning (2022) — 201 citations
- PaLI-X: On Scaling up a Multilingual Vision and Language Model (2023) — 301 citations
- Simple Open-Vocabulary Object Detection (2022) — 437 citations
- The Efficiency Misnomer (2021) — 121 citations
- Unified Graph Structured Models for Video Understanding (2021) — 62 citations
- Attention Bottlenecks for Multimodal Fusion (2021) — 825 citations