Bangalore S. Manjunath

Computer Vision and Pattern Recognition · 1996–2019

A researcher in the Science corpus: 16 papers; trusts 668 researchers and is trusted by 60 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 141 citations into it; #3,781 of 21,575 by trust here, #4,402 by citations
• Biophysics — 36 citations into it
• Molecular Biology — 29 citations into it
• Artificial Intelligence — 24 citations into it
• Media Technology — 22 citations into it; #984 of 3,621 by trust here, #1,171 by citations
• Aerospace Engineering — 9 citations into it
• Model Architectures — 8 citations into it
• Cognitive Neuroscience — 7 citations into it

Whom Bangalore S. Manjunath trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Karen Simonyan
4. Kaiming He
5. David G. Lowe
6. Andrew Zisserman
7. Cordelia Schmid
8. Diederik P. Kingma
9. Yann LeCun
10. Ross B. Girshick

Who trusts Bangalore S. Manjunath overall

1. Ritendra Datta
2. Riccardo Satta
3. Guanghai Liu
4. Nitish Srivastava
5. Dong-ping Tian
6. James Ze Wang
7. Yi Yang
8. Savvas A Chatzichristofis
9. Jia Li
10. Kihyuk Sohn

Papers from Bangalore S. Manjunath's perspective

Ordered by how strongly the researchers Bangalore S. Manjunath 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
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

• Color and texture descriptors (2001) — 1,884 citations
• Detecting GAN generated Fake Images using Co-occurrence Matrices (2019) — 368 citations
• The multiRANSAC algorithm and its application to detect planar homographies (2005) — 344 citations
• Unsupervised Segmentation of Color-Texture Regions in Images and Video (2001) — 1,563 citations
• Hybrid LSTM and Encoder–Decoder Architecture for Detection of Image Forgeries (2019) — 422 citations
• Weakly Supervised Manifold Learning for Dense Semantic Object Correspondence (2017) — 14 citations
• Malware images: visualization and automatic classification (2011) — 1,333 citations
• The iPlant Collaborative: Cyberinfrastructure for Plant Biology (2011) — 475 citations
• Biological Imaging Software Tools (2012) — 513 citations
• Eye tracking assisted extraction of attentionally important objects from videos (2015) — 57 citations
• Modeling and Detection of Geospatial Objects Using Texture Motifs (2006) — 105 citations
• Using texture to analyze and manage large collections of remote sensed image and video data. (2004) — 56 citations
• Introduction to MPEG-7: Multimedia Content Description Interface (2002) — 1,357 citations
• An efficient color representation for image retrieval (2001) — 360 citations
• EdgeFlow: a technique for boundary detection and image segmentation (2000) — 378 citations
• Texture features and learning similarity (1996)