What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Ying Shan spends much of his time researching Artificial intelligence, Computer vision, Bundle adjustment, Pattern recognition and Computer hardware.
In his works, he performs multidisciplinary study on Artificial intelligence and Set.
Ying Shan mostly deals with Object detection in his studies of Computer vision.
His Bundle adjustment research is multidisciplinary, incorporating perspectives in Matching, Motion estimation, Motion field and Bundle.
His Pattern recognition research incorporates themes from Search engine indexing and Pattern matching.
In the field of Computer hardware, his study on Input device, Pointer and Virtual mouse overlaps with subjects such as Joystick.
His most cited work include:
- Expressive expression mapping with ratio images (236 citations)
- Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features (179 citations)
- Visual panel: virtual mouse, keyboard and 3D controller with an ordinary piece of paper (121 citations)
What are the main themes of his work throughout his whole career to date?
Ying Shan mainly focuses on Artificial intelligence, Computer vision, Pattern recognition, Object and Feature.
His study brings together the fields of Machine learning and Artificial intelligence.
His studies examine the connections between Computer vision and genetics, as well as such issues in Computer graphics, with regards to Point location.
The various areas that Ying Shan examines in his Pattern recognition study include Contextual image classification and Histogram.
He interconnects Deep learning, Data mining and Computer engineering in the investigation of issues within Embedding.
His Face study integrates concerns from other disciplines, such as Facial expression and Bundle adjustment.
He most often published in these fields:
- Artificial intelligence (85.14%)
- Computer vision (58.11%)
- Pattern recognition (29.73%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (85.14%)
- Computer vision (58.11%)
- Feature (16.22%)
In recent papers he was focusing on the following fields of study:
Ying Shan mostly deals with Artificial intelligence, Computer vision, Feature, Object and Frame.
His Artificial intelligence research includes themes of Natural language processing and Pattern recognition.
His study in Pattern recognition is interdisciplinary in nature, drawing from both Attention network and Function.
His work on Feature extraction as part of general Computer vision research is frequently linked to Process, bridging the gap between disciplines.
His biological study focuses on Object detection.
His Segmentation research includes elements of Embedding and Machine learning.
Between 2018 and 2021, his most popular works were:
- Fast Video Object Segmentation Using the Global Context Module. (4 citations)
- Fast Video Object Segmentation using the Global Context Module (3 citations)
- Distilling Audio-Visual Knowledge by Compositional Contrastive Learning. (1 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Artificial intelligence, Computer vision, Object, Frame and Feature are his primary areas of study.
Artificial intelligence is closely attributed to Machine learning in his work.
The Machine learning study combines topics in areas such as Sentence and State.
His Optical flow study combines topics in areas such as Object detection and Noise.
There are a combination of areas like Representation, Segmentation, Context and Computation integrated together with his Position study.
You can notice a mix of various disciplines of study, such as Variety, Benchmark, Feature learning, Modalities and Modality, in his Semantic gap studies.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
