Jianchao Yang

What is he best known for?

The fields of study he is best known for:

• Artificial intelligence
• Machine learning
• Computer vision

His primary areas of study are Artificial intelligence, Pattern recognition, Machine learning, Artificial neural network and Neural coding. Jianchao Yang regularly links together related areas like Computer vision in his Artificial intelligence studies. His work carried out in the field of Pattern recognition brings together such families of science as Image resolution and Contextual image classification.

His work deals with themes such as Coding and Support vector machine, which intersect with Contextual image classification. In his research on the topic of Machine learning, Sentiment analysis, Social media analytics and Social media is strongly related with Image. Jianchao Yang usually deals with Artificial neural network and limits it to topics linked to Deep learning and Range and Visualization.

His most cited work include:

• Image Super-Resolution Via Sparse Representation (3620 citations)
• Linear spatial pyramid matching using sparse coding for image classification (2669 citations)
• Locality-constrained Linear Coding for image classification (2464 citations)

What are the main themes of his work throughout his whole career to date?

Jianchao Yang focuses on Artificial intelligence, Pattern recognition, Computer vision, Image and Convolutional neural network. Many of his studies on Artificial intelligence apply to Machine learning as well. His Pattern recognition research includes elements of Contextual image classification and Facial recognition system.

His work on Superresolution as part of general Image research is frequently linked to Set, Domain and Process, bridging the gap between disciplines. His research in Convolutional neural network intersects with topics in Smoothing, Sentiment analysis, Parsing, Font and Tree traversal. His Neural coding research is multidisciplinary, incorporating elements of K-SVD and Bilevel optimization.

He most often published in these fields:

• Artificial intelligence (81.97%)
• Pattern recognition (45.90%)
• Computer vision (34.43%)

What were the highlights of his more recent work (between 2016-2021)?

• Artificial intelligence (81.97%)
• Convolutional neural network (21.86%)
• Pattern recognition (45.90%)

In recent papers he was focusing on the following fields of study:

His primary scientific interests are in Artificial intelligence, Convolutional neural network, Pattern recognition, Computer vision and Artificial neural network. His research on Artificial intelligence frequently links to adjacent areas such as Machine learning. His Convolutional neural network research integrates issues from Algorithm and Convolution.

He has included themes like Reduction and Code in his Pattern recognition study. His study in the field of Object and Region of interest is also linked to topics like Trajectory and Frame based. His work investigates the relationship between Compressed sensing and topics such as Sparse approximation that intersect with problems in Neural coding.

Between 2016 and 2021, his most popular works were:

• Learning from Noisy Labels with Distillation (234 citations)
• Efficient Video Object Segmentation via Network Modulation (171 citations)
• Proposal-Free Network for Instance-Level Object Segmentation (121 citations)

In his most recent research, the most cited papers focused on:

• Artificial intelligence
• Machine learning
• Computer vision

His primary areas of investigation include Artificial intelligence, Pattern recognition, Segmentation, Computer vision and Benchmark. His research brings together the fields of Machine learning and Artificial intelligence. His research in the fields of Feature extraction overlaps with other disciplines such as Compression artifact.

His study in Object and Image falls within the category of Computer vision. He works mostly in the field of Benchmark, limiting it down to topics relating to Outlier and, in certain cases, Task, Algorithm design and Noise. His work in Image segmentation addresses issues such as Convolutional neural network, which are connected to fields such as Tree traversal, RGB color model, Kernel and Robustness.