What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His main research concerns Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Object detection.
His research on Artificial intelligence often connects related areas such as Ranking.
His Pattern recognition research incorporates themes from Cognitive neuroscience of visual object recognition, M-estimator, Image processing, Hierarchical clustering and Affinity propagation.
His work on Image resolution, Stereoscopy, Stereo camera and Orientation as part of his general Computer vision study is frequently connected to Common point, thereby bridging the divide between different branches of science.
His work is dedicated to discovering how Object detection, Pixel are connected with Hierarchy and Segmentation and other disciplines.
His Image retrieval research is multidisciplinary, incorporating elements of Artificial neural network, Hash function, Deep learning and Convolutional neural network.
His most cited work include:
- Deep learning of binary hash codes for fast image retrieval (400 citations)
- RANSAC-based DARCES: a new approach to fast automatic registration of partially overlapping range images (310 citations)
- Ordinal hyperplanes ranker with cost sensitivities for age estimation (252 citations)
What are the main themes of his work throughout his whole career to date?
Chu-Song Chen spends much of his time researching Artificial intelligence, Computer vision, Pattern recognition, Feature extraction and Machine learning.
His study involves Object detection, Facial recognition system, Deep learning, Video tracking and Contextual image classification, a branch of Artificial intelligence.
The Facial recognition system study combines topics in areas such as Subspace topology and Speech recognition.
His work carried out in the field of Deep learning brings together such families of science as Artificial neural network and Image retrieval.
His Computer vision study typically links adjacent topics like Computer graphics.
His Pattern recognition study integrates concerns from other disciplines, such as Histogram, Cognitive neuroscience of visual object recognition and Invariant.
He most often published in these fields:
- Artificial intelligence (92.68%)
- Computer vision (53.17%)
- Pattern recognition (40.00%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (92.68%)
- Deep learning (11.71%)
- Pattern recognition (40.00%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Deep learning, Pattern recognition, Artificial neural network and Machine learning.
His Artificial intelligence research focuses on Computer vision and how it connects with Model compression.
His research in Deep learning tackles topics such as Facial expression which are related to areas like Transfer of learning and Ordinal regression.
When carried out as part of a general Pattern recognition research project, his work on Classifier is frequently linked to work in Daytime, therefore connecting diverse disciplines of study.
His Machine learning research incorporates elements of Facial recognition system, Feature extraction, Key and Conditional random field.
His research in the fields of Visual Word overlaps with other disciplines such as Binary code.
Between 2015 and 2021, his most popular works were:
- Learning Compact Binary Descriptors with Unsupervised Deep Neural Networks (225 citations)
- Supervised Learning of Semantics-Preserving Hash via Deep Convolutional Neural Networks (181 citations)
- MVC: A Dataset for View-Invariant Clothing Retrieval and Attribute Prediction (42 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His primary scientific interests are in Artificial intelligence, Deep learning, Machine learning, Pattern recognition and Artificial neural network.
His research integrates issues of Information retrieval and Computer vision in his study of Artificial intelligence.
His study looks at the relationship between Deep learning and fields such as Facial expression, as well as how they intersect with chemical problems.
He combines subjects such as Contextual image classification, Multimedia search and Visual Word with his study of Machine learning.
His Pattern recognition study frequently links to related topics such as Image retrieval.
Chu-Song Chen interconnects Object detection, Hash function, Quantization and Feature in the investigation of issues within Image retrieval.
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