What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
His main research concerns Theoretical computer science, Artificial intelligence, Feature hashing, Binary code and Dynamic perfect hashing.
His Theoretical computer science research is multidisciplinary, incorporating perspectives in Hamming space, Nearest neighbor search, Image retrieval and Euclidean distance.
His Image retrieval research is multidisciplinary, incorporating elements of Sketch, Regularization and Cyclic coordinate descent.
His Artificial intelligence study integrates concerns from other disciplines, such as Machine learning and Pattern recognition.
His Feature hashing study necessitates a more in-depth grasp of Double hashing.
His research integrates issues of Universal hashing and Locality-sensitive hashing in his study of Dynamic perfect hashing.
His most cited work include:
- Supervised Discrete Hashing (630 citations)
- Learning Discriminative Binary Codes for Large-scale Cross-modal Retrieval (206 citations)
- Unsupervised Deep Hashing with Similarity-Adaptive and Discrete Optimization (201 citations)
What are the main themes of his work throughout his whole career to date?
Fumin Shen spends much of his time researching Artificial intelligence, Pattern recognition, Machine learning, Theoretical computer science and Binary code.
His Computer vision research extends to Artificial intelligence, which is thematically connected.
The concepts of his Pattern recognition study are interwoven with issues in Embedding, Facial recognition system and Subspace topology.
His Machine learning study combines topics in areas such as Visualization and Training set.
His study of Theoretical computer science brings together topics like Feature hashing, Dynamic perfect hashing, Universal hashing and Hash table.
Fumin Shen combines subjects such as K-independent hashing and Locality-sensitive hashing with his study of Feature hashing.
He most often published in these fields:
- Artificial intelligence (67.46%)
- Pattern recognition (38.28%)
- Machine learning (24.88%)
What were the highlights of his more recent work (between 2020-2021)?
- Artificial intelligence (67.46%)
- Pattern recognition (38.28%)
- Segmentation (3.35%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Pattern recognition, Segmentation, Object and Machine learning.
His Artificial intelligence study frequently links to related topics such as Computer vision.
His Pattern recognition research includes themes of RGB color model, Visual recognition and Skeleton.
The Machine learning study combines topics in areas such as Field, Generator, Hamming distance and Image retrieval.
His biological study spans a wide range of topics, including Annotation, Salient and Pascal.
His work carried out in the field of Data set brings together such families of science as Deep learning, Autoencoder, Feature extraction and Intrusion detection system.
Between 2020 and 2021, his most popular works were:
- Toward Effective Intrusion Detection Using Log-Cosh Conditional Variational Autoencoder (7 citations)
- Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation. (2 citations)
- Semantically Meaningful Class Prototype Learning for One-Shot Image Segmentation (1 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
His primary areas of study are Artificial intelligence, Segmentation, Object, Pattern recognition and Data modeling.
His work in Inference and Pascal is related to Artificial intelligence.
Fumin Shen has researched Inference in several fields, including Classifier, Image segmentation, Leverage and Pyramid.
His Pascal research incorporates themes from Annotation, Pixel, Image and Salient.
He has included themes like Deep learning, Intrusion detection system, Feature extraction, Convolutional neural network and Data set in his Data modeling study.
He interconnects Autoencoder and Data mining in the investigation of issues within Data set.
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