Muzammal Naseer

Computer Vision and Pattern Recognition · 2019–2025

A researcher in the Science corpus: 9 papers; trusts 1,229 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 546 citations into it; #18,246 of 21,575 by trust here, #4,834 by citations
• Artificial Intelligence — 464 citations into it; #34,683 of 35,345 by trust here, #14,181 by citations
• Model Architectures — 124 citations into it; #2,205 of 2,278 by trust here, #776 by citations
• Safety, Robustness & Interpretability — 83 citations into it
• Representation & Self-Supervised Learning — 79 citations into it
• Large Language Models — 59 citations into it
• Evaluation & Benchmarks — 51 citations into it
• Media Technology — 38 citations into it

Whom Muzammal Naseer trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Christian Szegedy
6. Ross B. Girshick
7. Yoshua Bengio
8. Ilya Sutskever
9. Yann LeCun
10. Karen Simonyan

Who trusts Muzammal Naseer overall

1. Salman Hameed Khan
2. Xiaosen Wang
3. Naveed Akhtar
4. Kanchana Ranasinghe
5. Fahad Shahbaz Khan
6. Syed Waqas Zamir
7. Jinghao Zhou
8. Kun He
9. Yi Liao
10. Wele Gedara Chaminda Bandara

Papers from Muzammal Naseer's perspective

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

Their papers

• Transformers in Vision: A Survey (2021) — 3,683 citations
• Intriguing Properties of Vision Transformers (2021) — 825 citations
• On Improving Adversarial Transferability of Vision Transformers (2021) — 111 citations
• A Self-supervised Approach for Adversarial Robustness (2020) — 371 citations
• Foundation Models Defining a New Era in Vision: A Survey and Outlook (2025) — 379 citations
• Self-regulating Prompts: Foundational Model Adaptation without Forgetting (2023) — 413 citations
• GeoChat:Grounded Large Vision-Language Model for Remote Sensing (2023) — 484 citations
• Orthogonal Projection Loss (2021) — 100 citations
• Cross-Domain Transferability of Adversarial Perturbations (2019) — 183 citations