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, Image retrieval and Feature extraction.
The study of Artificial intelligence is intertwined with the study of Machine learning in a number of ways.
His Pattern recognition research focuses on Object detection and how it relates to Detector, Background subtraction and Segmentation.
As part of the same scientific family, Qi Tian usually focuses on Computer vision, concentrating on Feature vector and intersecting with Salient and Construct.
His Image retrieval research integrates issues from Codebook, Quantization, Information retrieval, Search engine indexing and Visualization.
His Feature extraction study incorporates themes from Weighting, Motion analysis, Histogram, Key and Robustness.
His most cited work include:
- Scalable Person Re-identification: A Benchmark (1581 citations)
- Statistical modeling of complex backgrounds for foreground object detection (904 citations)
- Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline) (717 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Pattern recognition, Computer vision, Image retrieval and Machine learning are his primary areas of study.
His work on Artificial intelligence deals in particular with Feature extraction, Discriminative model, Feature, Contextual image classification and Visual Word.
He has included themes like Visualization and Image in his Pattern recognition study.
His research related to Object detection, Video tracking, Segmentation, Image segmentation and Object might be considered part of Computer vision.
He has researched Image retrieval in several fields, including Data mining and Information retrieval, Search engine indexing.
His research integrates issues of Representation, Training set and Benchmark in his study of Machine learning.
He most often published in these fields:
- Artificial intelligence (72.65%)
- Pattern recognition (40.26%)
- Computer vision (23.21%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (72.65%)
- Pattern recognition (40.26%)
- Machine learning (18.37%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Pattern recognition, Machine learning, Computer vision and Discriminative model.
His study in Artificial intelligence focuses on Feature, Feature extraction, Feature learning, Benchmark and Deep learning.
His research in Feature extraction intersects with topics in Visualization and Convolutional neural network.
Qi Tian interconnects Pixel, Similarity and Quantization in the investigation of issues within Pattern recognition.
The various areas that Qi Tian examines in his Machine learning study include Contextual image classification, Sample, Training set and Key.
In general Computer vision, his work in Object detection, Image, Moiré pattern and Segmentation is often linked to Domain linking many areas of study.
Between 2019 and 2021, his most popular works were:
- GhostNet: More Features From Cheap Operations (93 citations)
- PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search (93 citations)
- Adversarial Domain Adaptation with Domain Mixup (51 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Feature extraction, Algorithm and Machine learning.
Artificial neural network, Discriminative model, Feature, Convolutional neural network and Benchmark are the core of his Artificial intelligence study.
His Pattern recognition research integrates issues from Precision and recall, Image, Object and Pixel.
His research integrates issues of Sequence, Deep learning, Feature learning and Image retrieval in his study of Feature extraction.
His Algorithm research includes elements of Sketch and Robustness.
His Machine learning research incorporates elements of Image, Probabilistic logic, Similarity and Graph.
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