What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
Qingming Huang spends much of his time researching Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Video tracking.
His Machine learning research extends to Artificial intelligence, which is thematically connected.
His Pattern recognition study integrates concerns from other disciplines, such as Feature and Visual Word.
His research integrates issues of Salient, Viterbi algorithm and Identification in his study of Computer vision.
Qingming Huang has included themes like Artificial neural network, Cluster analysis, Robustness and Salience in his Feature extraction study.
His Video tracking research incorporates elements of Object detection, Multimedia and Human–computer interaction.
His most cited work include:
- Hedged Deep Tracking (502 citations)
- The Visual Object Tracking VOT2016 Challenge Results (462 citations)
- The Visual Object Tracking VOT2016 Challenge Results (462 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Feature extraction are his primary areas of study.
His studies in Feature, Discriminative model, Contextual image classification, Object and Video tracking are all subfields of Artificial intelligence research.
His research in Pattern recognition intersects with topics in Image, Representation and Visual Word.
His study in Object detection, Pixel, Image segmentation, Tracking and Motion estimation is done as part of Computer vision.
The study incorporates disciplines such as Crowdsourcing and Data mining in addition to Machine learning.
His Feature extraction study combines topics from a wide range of disciplines, such as Visualization, Robustness and Salience.
He most often published in these fields:
- Artificial intelligence (78.11%)
- Pattern recognition (37.21%)
- Computer vision (31.48%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (78.11%)
- Pattern recognition (37.21%)
- Machine learning (19.87%)
In recent papers he was focusing on the following fields of study:
Qingming Huang focuses on Artificial intelligence, Pattern recognition, Machine learning, Computer vision and Feature extraction.
Artificial intelligence is a component of his Feature, Convolutional neural network, Object, Benchmark and Discriminative model studies.
His work in Pattern recognition addresses subjects such as Image, which are connected to disciplines such as Consistency.
His Machine learning study combines topics in areas such as Crowdsourcing and Path.
When carried out as part of a general Computer vision research project, his work on Video tracking and Motion is frequently linked to work in Drone, therefore connecting diverse disciplines of study.
The Feature extraction study combines topics in areas such as Artificial neural network, Visualization, Deep learning and Salience.
Between 2017 and 2021, his most popular works were:
- CenterNet: Keypoint Triplets for Object Detection (359 citations)
- Cascaded Partial Decoder for Fast and Accurate Salient Object Detection (164 citations)
- The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking (144 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Qingming Huang mainly investigates Artificial intelligence, Pattern recognition, Feature extraction, Computer vision and Benchmark.
Qingming Huang integrates many fields in his works, including Artificial intelligence and Code.
His work on Discriminative model, Classifier and Image segmentation as part of general Pattern recognition study is frequently connected to Invariant, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His Feature extraction study incorporates themes from Data mining, Artificial neural network, Image, Salience and Robustness.
The various areas that Qingming Huang examines in his Benchmark study include Tracking, Representation and Saliency map.
His research investigates the connection between Convolutional neural network and topics such as Eye tracking that intersect with problems in Video tracking.
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