Fumin Shen
Computer Vision and Pattern Recognition · 2013–2021
A researcher in the Science corpus: 12 papers; trusts 965 researchers and is trusted by 37 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 247 citations into it; #4,187 of 21,575 by trust here, #4,245 by citations
- Artificial Intelligence — 117 citations into it; #19,462 of 35,345 by trust here, #20,836 by citations
- Representation & Self-Supervised Learning — 45 citations into it
- Information Retrieval & Search — 34 citations into it
- Media Technology — 23 citations into it
- Evaluation & Benchmarks — 19 citations into it
- Aerospace Engineering — 18 citations into it; #2,157 of 6,421 by trust here, #2,086 by citations
- Multimodal Learning — 16 citations into it
Whom Fumin Shen trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Wei David Liu
- Alex Krizhevsky
- Kaiming He
- Diederik P. Kingma
- Yoshua Bengio
- Antonio Torralba
- Svetlana Lazebnik
- Brian Kulis
- Yunchao Gong
Who trusts Fumin Shen overall
- Shiv Ram Dubey
- Li Liu
- Hanwang Zhang
- Xiangnan He
- Jingdong Wang
- Heng Tao Shen
- Wei David Liu
- Jun Xiao
- Ayan Kumar Bhunia
- Li Yuan
Papers from Fumin Shen's perspective
Ordered by how strongly the researchers Fumin Shen trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Supervised Discrete Hashing (2015) — 1,164 citations
- Learning Binary Codes for Maximum Inner Product Search (2015) — 154 citations
- Deep Sketch Hashing: Fast Free-Hand Sketch-Based Image Retrieval (2017) — 298 citations
- Zero-Shot Sketch-Image Hashing (2018) — 172 citations
- Exact Adversarial Attack to Image Captioning via Structured Output Learning With Latent Variables (2019) — 61 citations
- Inductive Hashing on Manifolds (2013) — 36 citations
- Unsupervised Deep Hashing with Similarity-Adaptive and Discrete Optimization (2018) — 372 citations
- Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation (2021) — 220 citations
- Binary Multi-View Clustering (2019) — 542 citations
- Learning Discriminative Binary Codes for Large-scale Cross-modal Retrieval (2017) — 427 citations
- Towards enhancing the last-mile delivery: An effective crowd-tasking model with scalable solutions (2016) — 338 citations
- Discrete Collaborative Filtering (2016) — 278 citations