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, Computer vision, Convolutional neural network, Machine learning and Image.
His studies in Artificial intelligence integrate themes in fields like Radiance and Natural language processing.
His work on Image-based modeling and rendering, Image segmentation and Image stabilization as part of general Computer vision research is often related to Personal computer, thus linking different fields of science.
His research integrates issues of Artificial neural network, Social media, Social media analytics and Sentiment analysis in his study of Convolutional neural network.
His biological study spans a wide range of topics, including Classifier, Deep learning and Feature learning.
His work in the fields of Closed captioning overlaps with other areas such as Selection.
His most cited work include:
- Image Captioning with Semantic Attention (788 citations)
- Content-preserving warps for 3D video stabilization (399 citations)
- Fine-grained recognition without part annotations (343 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Computer vision, Pattern recognition, Image and Artificial neural network.
The study incorporates disciplines such as Machine learning and Natural language processing in addition to Artificial intelligence.
His Computer vision study frequently draws connections between related disciplines such as Computer graphics.
His Feature vector and Classifier study, which is part of a larger body of work in Pattern recognition, is frequently linked to Rank, bridging the gap between disciplines.
His work on Closed captioning as part of general Image study is frequently connected to Stylized fact, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His Artificial neural network study incorporates themes from Deep learning and Information retrieval.
He most often published in these fields:
- Artificial intelligence (81.64%)
- Computer vision (38.65%)
- Pattern recognition (31.40%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (81.64%)
- Pattern recognition (31.40%)
- Artificial neural network (18.36%)
In recent papers he was focusing on the following fields of study:
Hailin Jin spends much of his time researching Artificial intelligence, Pattern recognition, Artificial neural network, Image and Font.
In most of his Artificial intelligence studies, his work intersects topics such as Natural language processing.
Many of his research projects under Pattern recognition are closely connected to Initialization with Initialization, tying the diverse disciplines of science together.
Hailin Jin studied Artificial neural network and Information retrieval that intersect with Domain knowledge.
His research on Image concerns the broader Computer vision.
His work in Font covers topics such as Theoretical computer science which are related to areas like Electronic document.
Between 2017 and 2021, his most popular works were:
- Synthetically Supervised Feature Learning for Scene Text Recognition (65 citations)
- Towards Privacy-Preserving Visual Recognition via Adversarial Training: A Pilot Study (53 citations)
- Multi-task Adversarial Network for Disentangled Feature Learning (44 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Artificial intelligence, Image, Artificial neural network, Feature learning and Feature are his primary areas of study.
Hailin Jin combines subjects such as Natural language processing, Machine learning, Computer vision and Pattern recognition with his study of Artificial intelligence.
His Computer vision study integrates concerns from other disciplines, such as Dependency and Leverage.
Hailin Jin interconnects Embedding and Autoencoder in the investigation of issues within Image.
The Artificial neural network study combines topics in areas such as Image segmentation, Dynamic web page and Robustness.
His Feature research incorporates themes from Pixel, Segmentation and Image editing.
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.
