What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
His primary scientific interests are in Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Feature extraction.
His Artificial intelligence study frequently draws connections to adjacent fields such as Natural language processing.
His studies in Pattern recognition integrate themes in fields like Pixel, Feature, Deep learning and Image retrieval.
His work focuses on many connections between Machine learning and other disciplines, such as Training set, that overlap with his field of interest in Selection and Identification.
His work deals with themes such as Image resolution, Multiview Video Coding, Decoding methods and Interpolation, which intersect with Feature extraction.
As part of the same scientific family, Rongrong Ji usually focuses on Contextual image classification, concentrating on Data mining and intersecting with Overfitting.
His most cited work include:
- Supervised hashing with kernels (1111 citations)
- Large-scale visual sentiment ontology and detectors using adjective noun pairs (472 citations)
- 3-D Object Retrieval and Recognition With Hypergraph Analysis (453 citations)
What are the main themes of his work throughout his whole career to date?
Rongrong Ji mainly focuses on Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Discriminative model.
Feature extraction, Image retrieval, Feature, Convolutional neural network and Deep learning are among the areas of Artificial intelligence where the researcher is concentrating his efforts.
His work in Image retrieval addresses subjects such as Information retrieval, which are connected to disciplines such as Sentiment analysis.
His research in Pattern recognition intersects with topics in Contextual image classification, Object detection, Representation and Face.
The concepts of his Computer vision study are interwoven with issues in Visualization and Robustness.
His Machine learning research incorporates themes from Image and Benchmark.
He most often published in these fields:
- Artificial intelligence (72.46%)
- Pattern recognition (33.63%)
- Computer vision (20.77%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (72.46%)
- Pattern recognition (33.63%)
- Machine learning (20.54%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Machine learning, Algorithm and Pruning.
His study focuses on the intersection of Artificial intelligence and fields such as Computer vision with connections in the field of Visualization.
His work on Feature extraction is typically connected to Detector as part of general Pattern recognition study, connecting several disciplines of science.
Rongrong Ji has researched Machine learning in several fields, including Channel, Image and Benchmark.
As part of one scientific family, he deals mainly with the area of Algorithm, narrowing it down to issues related to the Convolutional neural network, and often Deep learning.
His Pruning research incorporates elements of Gradient descent, Bilinear interpolation, Reduction and Speedup.
Between 2019 and 2021, his most popular works were:
- HRank: Filter Pruning Using High-Rank Feature Map (72 citations)
- Siamese Box Adaptive Network for Visual Tracking (53 citations)
- Toward Compact ConvNets via Structure-Sparsity Regularized Filter Pruning (40 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Rongrong Ji mostly deals with Artificial intelligence, Pattern recognition, Algorithm, Feature extraction and Regularization.
His research integrates issues of Machine learning and Computer vision in his study of Artificial intelligence.
His study in the field of Ensemble learning also crosses realms of Expression.
The Computer vision study combines topics in areas such as Visualization and Triplet loss.
Rongrong Ji combines subjects such as Feature, Deep neural networks, Face, Cluster analysis and Entropy with his study of Pattern recognition.
In his study, Pixel, Pascal and Solver is inextricably linked to Object detection, which falls within the broad field of Regularization.
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