What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Jialie Shen mainly investigates Artificial intelligence, Information retrieval, Pattern recognition, Visual Word and Image retrieval.
Jialie Shen has researched Artificial intelligence in several fields, including Data modeling, Theoretical computer science, Universal hashing, Dynamic perfect hashing and Machine learning.
His biological study spans a wide range of topics, including Text mining, Annotation and Knowledge organization.
Jialie Shen combines topics linked to Computer vision with his work on Pattern recognition.
His work carried out in the field of Visual Word brings together such families of science as Visualization and Relevance.
His research in Image retrieval tackles topics such as Hash function which are related to areas like Content-based image retrieval, Unsupervised learning, Watermark and Code.
His most cited work include:
- Visual-Textual Joint Relevance Learning for Tag-Based Social Image Search (360 citations)
- Representative Discovery of Structure Cues for Weakly-Supervised Image Segmentation (194 citations)
- Weakly-supervised hashing in kernel space (147 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Information retrieval, Data mining, Pattern recognition and Machine learning.
Many of his studies involve connections with topics such as Computer vision and Artificial intelligence.
His Information retrieval research incorporates elements of Similarity and Metadata.
His Data mining study incorporates themes from Image, Image retrieval, Neural coding and Feature vector.
Jialie Shen works mostly in the field of Pattern recognition, limiting it down to topics relating to Hash function and, in certain cases, Theoretical computer science, as a part of the same area of interest.
His Feature extraction study deals with Visualization intersecting with Landmark.
He most often published in these fields:
- Artificial intelligence (35.10%)
- Information retrieval (30.46%)
- Data mining (23.18%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (35.10%)
- Recommender system (11.26%)
- Deep learning (2.65%)
In recent papers he was focusing on the following fields of study:
Jialie Shen mainly focuses on Artificial intelligence, Recommender system, Deep learning, Pattern recognition and Information retrieval.
Artificial intelligence is frequently linked to Machine learning in his study.
His studies deal with areas such as Multimedia, Key, User profile and Human–computer interaction as well as Recommender system.
His Deep learning study combines topics in areas such as Hash function, Feature learning and Factor analysis.
The various areas that Jialie Shen examines in his Pattern recognition study include Re ranking and Leverage.
Jialie Shen does research in Information retrieval, focusing on Search engine indexing specifically.
Between 2017 and 2021, his most popular works were:
- Unsupervised deep hashing via binary latent factor models for large-scale cross-modal retrieval (36 citations)
- Enhancing Sketch-Based Image Retrieval by CNN Semantic Re-ranking (28 citations)
- Fashion recommendations through cross-media information retrieval (9 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His scientific interests lie mostly in Deep learning, Artificial intelligence, Pattern recognition, Convolutional neural network and Image retrieval.
The study incorporates disciplines such as Hash function, Feature learning and Factor analysis in addition to Deep learning.
His Re ranking, Feature extraction and Leverage study in the realm of Artificial intelligence connects with subjects such as Noise measurement and Sketch.
His Convolutional neural network research is multidisciplinary, incorporating elements of Similarity measure, Construct, Image and Information retrieval.
His Image retrieval study spans across into fields like Clothing and Product.
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