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, Computer vision, Feature extraction and Visualization.
His Artificial intelligence research includes themes of Machine learning and Data mining.
Shuqiang Jiang has included themes like Boosting, Representation and Image retrieval in his Pattern recognition study.
His work on Segmentation, Image segmentation and Image as part of general Computer vision study is frequently linked to Statistical hypothesis testing, therefore connecting diverse disciplines of science.
Shuqiang Jiang focuses mostly in the field of Feature extraction, narrowing it down to matters related to Feature and, in some cases, Range, K-SVD and Categorization.
His Visualization research integrates issues from Topic model, Speech recognition and Cluster analysis.
His most cited work include:
- Building contextual visual vocabulary for large-scale image applications (126 citations)
- Scene Recognition with CNNs: Objects, Scales and Dataset Bias (109 citations)
- Trajectory based event tactics analysis in broadcast sports video (94 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, Feature extraction and Machine learning.
Discriminative model, Visualization, Image retrieval, Object and Feature are subfields of Artificial intelligence in which his conducts study.
His Visualization study integrates concerns from other disciplines, such as Semantics and Multimedia.
His Computer vision study frequently draws connections between adjacent fields such as Support vector machine.
The study incorporates disciplines such as Contextual image classification, Object detection and Feature in addition to Pattern recognition.
He combines subjects such as Semantic similarity and Data mining with his study of Machine learning.
He most often published in these fields:
- Artificial intelligence (71.95%)
- Computer vision (35.37%)
- Pattern recognition (35.37%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (71.95%)
- Discriminative model (15.45%)
- Pattern recognition (35.37%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Discriminative model, Pattern recognition, Deep learning and Benchmark.
His Artificial intelligence study incorporates themes from Machine learning and Computer vision.
His Discriminative model research includes themes of Visualization, Data mining, Feature, Object and Feature extraction.
His study in the field of Classifier is also linked to topics like Focus.
As a member of one scientific family, Shuqiang Jiang mostly works in the field of Deep learning, focusing on Convolutional neural network and, on occasion, Range and Contextual image classification.
His Benchmark research integrates issues from Information retrieval and Image retrieval.
Between 2019 and 2021, his most popular works were:
- Multi-Scale Multi-View Deep Feature Aggregation for Food Recognition (30 citations)
- Image Representations With Spatial Object-to-Object Relations for RGB-D Scene Recognition (11 citations)
- Scene Recognition With Prototype-Agnostic Scene Layout (8 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Shuqiang Jiang focuses on Artificial intelligence, Pattern recognition, Discriminative model, Scale and Deep learning.
Shuqiang Jiang has researched Artificial intelligence in several fields, including Machine learning, Relation and Natural language processing.
His work on Feature extraction and Classifier as part of general Pattern recognition study is frequently connected to Spatial analysis, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
Shuqiang Jiang has included themes like Image processing, Visualization, Cognitive neuroscience of visual object recognition and Convolutional neural network in his Feature extraction study.
The Discriminative model study combines topics in areas such as Ontology, Object detection, RGB color model and Embedding.
His Deep learning research incorporates elements of Feature learning, Feature and Pairwise comparison.
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