What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His primary areas of study are Artificial intelligence, Machine learning, Convolutional neural network, Pattern recognition and Information retrieval.
His work in Artificial intelligence addresses issues such as Natural language processing, which are connected to fields such as Semantics.
His Machine learning research incorporates themes from Domain and Context.
The concepts of his Convolutional neural network study are interwoven with issues in Recurrent neural network, Feature learning and Inference.
The various areas that Tao Mei examines in his Pattern recognition study include Image, Representation and Categorization.
His research investigates the connection between Information retrieval and topics such as Ranking that intersect with issues in Visual search, Search engine, Beam search, Incremental heuristic search and Phrase search.
His most cited work include:
- Look Closer to See Better: Recurrent Attention Convolutional Neural Network for Fine-Grained Image Recognition (576 citations)
- Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks (478 citations)
- MSR-VTT: A Large Video Description Dataset for Bridging Video and Language (458 citations)
What are the main themes of his work throughout his whole career to date?
Tao Mei spends much of his time researching Artificial intelligence, Machine learning, Pattern recognition, Information retrieval and Computer vision.
His Artificial intelligence research focuses on Natural language processing and how it connects with Semantics.
The Machine learning study combines topics in areas such as Domain, Training set and TRECVID.
His Pattern recognition study combines topics from a wide range of disciplines, such as Contextual image classification and Parsing.
Tao Mei has researched Feature extraction in several fields, including Visualization and Feature.
Tao Mei has included themes like Encoder, Recurrent neural network, Natural language and Speech recognition in his Closed captioning study.
He most often published in these fields:
- Artificial intelligence (57.69%)
- Machine learning (20.11%)
- Pattern recognition (16.89%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (57.69%)
- Machine learning (20.11%)
- Pattern recognition (16.89%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Pattern recognition, Computer vision and Facial recognition system.
Artificial intelligence is closely attributed to Code in his research.
As part of one scientific family, Tao Mei deals mainly with the area of Machine learning, narrowing it down to issues related to the Domain, and often Feature.
His Pattern recognition research is multidisciplinary, incorporating elements of Artificial neural network, Similarity, Parsing and Generative grammar.
The study incorporates disciplines such as Representation and Polygon mesh in addition to Computer vision.
His biological study spans a wide range of topics, including Margin, Speech recognition and Softmax function.
Between 2019 and 2021, his most popular works were:
- X-Linear Attention Networks for Image Captioning (49 citations)
- Mis-Classified Vector Guided Softmax Loss for Face Recognition (19 citations)
- FastReID: A Pytorch Toolbox for General Instance Re-identification. (16 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Tao Mei focuses on Artificial intelligence, Discriminative model, Machine learning, Pattern recognition and Feature extraction.
His work on Computer vision expands to the thematically related Artificial intelligence.
His Discriminative model research integrates issues from Minimum bounding box and Code.
Tao Mei interconnects Domain, Facial recognition system and Closed captioning in the investigation of issues within Machine learning.
His work on Training set as part of general Pattern recognition study is frequently linked to Dimension, therefore connecting diverse disciplines of science.
Tao Mei works mostly in the field of Feature extraction, limiting it down to concerns involving Task analysis and, occasionally, Contrast and Matching.
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