What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Jianbing Shen focuses on Artificial intelligence, Pattern recognition, Computer vision, Segmentation and Object.
His research combines Machine learning and Artificial intelligence.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Video tracking, Pixel, Image and Benchmark.
His study explores the link between Computer vision and topics such as Salient that cross with problems in Contrast.
His Segmentation research is multidisciplinary, relying on both Frame, Complete graph, Aggregate and Task.
His work carried out in the field of Object brings together such families of science as Margin, Representation, Discriminative model and Pyramid.
His most cited work include:
- Video Salient Object Detection via Fully Convolutional Networks (390 citations)
- Saliency-aware geodesic video object segmentation (372 citations)
- Deep Visual Attention Prediction (354 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Computer vision, Pattern recognition, Segmentation and Object.
In his work, Robustness and Benchmark is strongly intertwined with Machine learning, which is a subfield of Artificial intelligence.
His Computer vision research includes themes of Salient and Salience.
His research in Pattern recognition intersects with topics in Video tracking, Visualization and Cluster analysis.
His Segmentation study incorporates themes from Complete graph, Artificial neural network, Supervised learning, Margin and Histogram.
His work on Object detection as part of general Object study is frequently connected to Context and Field, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
He most often published in these fields:
- Artificial intelligence (87.39%)
- Computer vision (38.26%)
- Pattern recognition (37.39%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (87.39%)
- Pattern recognition (37.39%)
- Object (20.00%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Object, Segmentation and Computer vision.
His Artificial intelligence study frequently intersects with other fields, such as Machine learning.
His work on Training set as part of general Pattern recognition research is frequently linked to Modal, Lung infection and Process, bridging the gap between disciplines.
His study looks at the relationship between Object and fields such as Pyramid, as well as how they intersect with chemical problems.
His Segmentation research incorporates themes from Visualization and Complete graph.
His work on Motion, Video tracking and Minimum bounding box as part of general Computer vision research is often related to Message passing, thus linking different fields of science.
Between 2019 and 2021, his most popular works were:
- Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images (139 citations)
- Revisiting Video Saliency Prediction in the Deep Learning Era (92 citations)
- Deep Learning for Person Re-identification: A Survey and Outlook (85 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Artificial intelligence, Pattern recognition, Segmentation, Visualization and Object are his primary areas of study.
His Artificial intelligence research focuses on subjects like Machine learning, which are linked to Heuristic.
In Pattern recognition, Jianbing Shen works on issues like Embedding, which are connected to Feature vector, Discriminative model and Probabilistic logic.
His Segmentation research includes elements of Object detection and Feature.
His research integrates issues of Eye tracking and Computer vision in his study of Visualization.
His studies examine the connections between Deep learning and genetics, as well as such issues in Visual attention, with regards to Salient objects.
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