Amit K. Roy-Chowdhury

Computer Vision and Pattern Recognition · 2004–2019

A researcher in the Science corpus: 13 papers; trusts 1,237 researchers and is trusted by 73 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 292 citations into it; #3,643 of 21,575 by trust here, #3,582 by citations
• Artificial Intelligence — 161 citations into it; #9,508 of 35,345 by trust here, #7,413 by citations
• Multimodal Learning — 56 citations into it; #910 of 1,211 by trust here, #758 by citations
• Representation & Self-Supervised Learning — 33 citations into it
• Natural Language Processing — 32 citations into it
• Evaluation & Benchmarks — 30 citations into it
• Model Architectures — 28 citations into it
• Optimization & Training — 25 citations into it

Whom Amit K. Roy-Chowdhury trusts overall

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

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

Who trusts Amit K. Roy-Chowdhury overall

1. Antonino Furnari
2. Yui Man Lui
3. Zongyi Joe Liu
4. Giorgio Battistelli
5. Rushil Anirudh
6. Giovanni Maria Farinella
7. Pavan K. Turaga
8. Zhiwu Huang
9. Yan Song
10. Sudeep Sarkar

Papers from Amit K. Roy-Chowdhury's perspective

Ordered by how strongly the researchers Amit K. Roy-Chowdhury 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. Long Short-Term Memory
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
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

• A large-scale benchmark dataset for event recognition in surveillance video (2011) — 442 citations
• Learning Joint Embedding with Multimodal Cues for Cross-Modal Video-Text Retrieval (2018) — 286 citations
• Webly Supervised Joint Embedding for Cross-Modal Image-Text Retrieval (2018) — 81 citations
• AVSS 2011 demo session: A large-scale benchmark dataset for event recognition in surveillance video (2011) — 435 citations
• Detecting GAN generated Fake Images using Co-occurrence Matrices (2019) — 368 citations
• W-TALC: Weakly-supervised Temporal Activity Localization and Classification (2018) — 340 citations
• Learning Temporal Regularity in Video Sequences (2016) — 1,362 citations
• Identification of humans using gait (2004) — 647 citations
• Hybrid LSTM and Encoder–Decoder Architecture for Detection of Image Forgeries (2019) — 422 citations
• Joint Prediction of Activity Labels and Starting Times in Untrimmed Videos (2017) — 90 citations
• Generating Diverse Image Datasets with Limited Labeling (2016) — 12 citations
• Information Weighted Consensus Filters and Their Application in Distributed Camera Networks (2013) — 295 citations
• Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Matching Shape Sequences in Video with Applications in Human Movement Analysis. Ieee Transactions on Pattern Analysis and Machine Intelligence 2