Noel C. F. Codella
Computer Vision and Pattern Recognition · 2015–2023
A researcher in the Science corpus: 22 papers; trusts 866 researchers and is trusted by 19 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 408 citations into it; #8,295 of 21,575 by trust here, #2,148 by citations
- Artificial Intelligence — 375 citations into it; #19,264 of 35,345 by trust here, #2,436 by citations
- Representation & Self-Supervised Learning — 102 citations into it
- Model Architectures — 91 citations into it; #1,225 of 2,278 by trust here, #393 by citations
- Oncology — 60 citations into it; #473 of 1,698 by trust here, #244 by citations
- Evaluation & Benchmarks — 53 citations into it
- Natural Language Processing — 48 citations into it
- Epidemiology — 31 citations into it; #1,861 of 2,148 by trust here, #511 by citations
Whom Noel C. F. Codella trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Alex Krizhevsky
- Geoffrey E. Hinton
- Yoshua Bengio
- Yann LeCun
- Ross B. Girshick
- Diederik P. Kingma
- Ilya Sutskever
- Karen Simonyan
- Andrew Zisserman
Who trusts Noel C. F. Codella overall
- Jianwei Yang
- Yanyang Gu
- Yuexiang Li
- Jiancheng Yang
- Erico Tjoa
- Reza Azad
- Jeremy Kawahara
- Jianpeng Zhang
- Amitojdeep Singh
- Matthew Groh
Papers from Noel C. F. Codella's perspective
Ordered by how strongly the researchers Noel C. F. Codella 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
- Florence: A New Foundation Model for Computer Vision (2021) — 1,142 citations
- CvT: Introducing Convolutions to Vision Transformers (2021) — 2,478 citations
- RegionCLIP: Region-based Language-Image Pretraining (2021) — 921 citations
- DaViT: Dual Attention Vision Transformers (2022) — 406 citations
- A Broader Study of Cross-Domain Few-Shot Learning (2019) — 423 citations
- A New Benchmark for Evaluation of Cross-Domain Few-Shot Learning (2019) — 45 citations
- Human–computer collaboration for skin cancer recognition (2020) — 912 citations
- A patient-centric dataset of images and metadata for identifying melanomas using clinical context (2020) — 562 citations
- Deep learning ensembles for melanoma recognition in dermoscopy images (2016) — 600 citations
- i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data (2023) — 4 citations
- Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC) (2019) — 1,802 citations
- Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study (2019) — 455 citations
- BCN20000: Dermoscopic Lesions in the Wild (2019) — 653 citations
- P2L: Predicting Transfer Learning for Images and Semantic Relations (2019)
- Segmentation of Both Diseased and Healthy Skin From Clinical Photographs in a Primary Care Setting (2018) — 12 citations
- TED: Teaching AI to Explain its Decisions (2018) — 115 citations
- Collaborative Human-AI (CHAI): Evidence-Based Interpretable Melanoma Classification in Dermoscopic Images (2018) — 45 citations
- Results of the 2016 International Skin Imaging Collaboration ISBI Challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images (2017) — 294 citations
- Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC) (2016) — 2,727 citations
- Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC) (2016) — 870 citations