Rao Muhammad Anwer
Computer Vision and Pattern Recognition · 2012–2025
A researcher in the Science corpus: 13 papers; trusts 1,271 researchers and is trusted by 10 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 496 citations into it; #9,578 of 21,575 by trust here, #5,806 by citations
- Artificial Intelligence — 330 citations into it; #32,062 of 35,345 by trust here, #12,244 by citations
- Model Architectures — 66 citations into it
- Media Technology — 49 citations into it; #2,689 of 3,621 by trust here, #1,696 by citations
- Evaluation & Benchmarks — 36 citations into it
- Representation & Self-Supervised Learning — 28 citations into it
- Optimization & Training — 25 citations into it
- Meta & Continual Learning — 17 citations into it
Whom Rao Muhammad Anwer trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Ross B. Girshick
- Alex Krizhevsky
- Geoffrey E. Hinton
- Karen Simonyan
- Shaoqing Ren
- Andrew Zisserman
- Yann LeCun
- Yoshua Bengio
- Diederik P. Kingma
Who trusts Rao Muhammad Anwer overall
- Xinlei Chen
- Chien-Yao Wang
- Fahad Shahbaz Khan
- Liang Zheng
- Jiyang Qi
- Jiale Cao
- Yang Li
- Abhinav Shrivastava
- Hyungtae Lee
- Alexey Bochkovskiy
Papers from Rao Muhammad Anwer's perspective
Ordered by how strongly the researchers Rao Muhammad Anwer 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)
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Color attributes for object detection (2012) — 321 citations
- SipMask: Spatial Information Preservation for Fast Image and Video Instance Segmentation (2020) — 195 citations
- EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications (2022) — 374 citations
- Learning Rich Features at High-Speed for Single-Shot Object Detection (2019) — 97 citations
- Enriched Feature Guided Refinement Network for Object Detection (2019) — 81 citations
- Handwriting Transformers (2021) — 71 citations
- Efficient Featurized Image Pyramid Network for Single Shot Detector (2019) — 106 citations
- Foundation Models Defining a New Era in Vision: A Survey and Outlook (2025) — 379 citations
- GLaMM: Pixel Grounding Large Multimodal Model (2023) — 537 citations
- Transformers in Remote Sensing: A Survey (2022) — 379 citations
- Energy-based Latent Aligner for Incremental Learning (2022) — 57 citations
- D2Det: Towards High Quality Object Detection and Instance Segmentation (2020) — 278 citations
- Recognizing Actions Through Action-Specific Person Detection (2015) — 54 citations