What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Artificial intelligence, Pattern recognition, Convolutional neural network, Computer vision and Ischemia are his primary areas of study.
His Artificial intelligence study typically links adjacent topics like Machine learning.
His Pattern recognition research is multidisciplinary, relying on both Facial recognition system, Boosting, Subspace topology and Graph.
His Convolutional neural network study integrates concerns from other disciplines, such as Contextual image classification, Algorithm, Residual and Structured prediction.
His work in Computer vision addresses issues such as Robustness, which are connected to fields such as Regularization, Bayesian inference and Eye tracking.
His studies deal with areas such as Anesthesia, Endocrinology and Pharmacology as well as Ischemia.
His most cited work include:
- Quantized Convolutional Neural Networks for Mobile Devices (623 citations)
- Additive Margin Softmax for Face Verification (392 citations)
- NormFace: L 2 Hypersphere Embedding for Face Verification (249 citations)
What are the main themes of his work throughout his whole career to date?
Jian Cheng mostly deals with Artificial intelligence, Pattern recognition, Machine learning, Computer vision and Discriminative model.
His study in Convolutional neural network, Feature extraction, Contextual image classification, Deep learning and Classifier falls within the category of Artificial intelligence.
His study connects Algorithm and Convolutional neural network.
His Pattern recognition research incorporates themes from Subspace topology, Facial recognition system, Feature and Image retrieval.
Jian Cheng studies Relevance feedback, a branch of Image retrieval.
Segmentation, Pixel and Image segmentation are subfields of Computer vision in which his conducts study.
He most often published in these fields:
- Artificial intelligence (54.14%)
- Pattern recognition (30.65%)
- Machine learning (14.77%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (54.14%)
- Pattern recognition (30.65%)
- Discriminative model (10.07%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Pattern recognition, Discriminative model, Algorithm and Confidence interval.
His Artificial intelligence research includes elements of Machine learning and Computer vision.
His work on Cross modality as part of general Pattern recognition study is frequently connected to Process, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His Discriminative model research incorporates elements of Data modeling, Visualization, Convolutional neural network and Gesture recognition.
While the research belongs to areas of Algorithm, Jian Cheng spends his time largely on the problem of Object detection, intersecting his research to questions surrounding Feature.
His work on Relative risk as part of general Confidence interval research is frequently linked to Mean radiant temperature, bridging the gap between disciplines.
Between 2019 and 2021, his most popular works were:
- Skeleton-Based Action Recognition With Shift Graph Convolutional Network (36 citations)
- AIM 2020: Scene Relighting and Illumination Estimation Challenge (26 citations)
- Cross-Modality Paired-Images Generation for RGB-Infrared Person Re-Identification (23 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Discriminative model, Image and Graph.
His work in Artificial intelligence is not limited to one particular discipline; it also encompasses Computer vision.
His Pattern recognition research includes themes of RGB color model, Modality, Convolution and Code.
His study in Discriminative model is interdisciplinary in nature, drawing from both Visualization, Convolutional neural network, Constraint and Robustness.
He has included themes like Standard illuminant and Color temperature in his Image study.
His Graph research is multidisciplinary, relying on both Graph and Theoretical computer science.
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