Maxwell D. Collins

Computer Vision and Pattern Recognition · 2014–2022

A researcher in the Science corpus: 9 papers; trusts 1,421 researchers and is trusted by 585 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 541 citations into it; #1,768 of 21,575 by trust here, #2,141 by citations
• Artificial Intelligence — 285 citations into it; #3,608 of 35,345 by trust here, #2,914 by citations
• Model Architectures — 129 citations into it; #2,223 of 2,278 by trust here, #694 by citations
• Optimization & Training — 45 citations into it; #1,939 of 2,601 by trust here, #1,766 by citations
• Evaluation & Benchmarks — 44 citations into it
• Efficiency & Compression — 26 citations into it; #188 of 1,264 by trust here, #329 by citations
• Representation & Self-Supervised Learning — 26 citations into it
• Aerospace Engineering — 19 citations into it

Whom Maxwell D. Collins trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Ross B. Girshick
5. Yann LeCun
6. Yoshua Bengio
7. Diederik P. Kingma
8. Liang-Chieh Chen
9. Ilya Sutskever
10. L. Eon Bottou

Who trusts Maxwell D. Collins overall

1. José Manuel Álvarez
2. Liang-Chieh Chen
3. Bowen Cheng
4. Huiyu Wang
5. Sara Hooker
6. Mathieu Salzmann
7. Sajid Anwar
8. X. Zhang
9. Guillaume Bellec
10. Yukun Zhu

Papers from Maxwell D. Collins's perspective

Ordered by how strongly the researchers Maxwell D. Collins trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Spatially Adaptive Computation Time for Residual Networks (2016) — 382 citations
• Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation (2019) — 690 citations
• Searching for Efficient Multi-Scale Architectures for Dense Image Prediction (2018) — 425 citations
• DeeperLab: Single-Shot Image Parser (2019) — 197 citations
• Memory Bounded Deep Convolutional Networks (2014) — 174 citations
• CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation (2022) — 112 citations
• Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation (2020) — 121 citations
• k-means Mask Transformer (2022) — 164 citations
• Efficient Non-greedy Optimization of Decision Trees (2015) — 115 citations