What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His primary scientific interests are in Artificial intelligence, Pattern recognition, Computer vision, Video tracking and Eye tracking.
His studies in Robustness, Feature extraction, Discriminative model, Artificial neural network and Active appearance model are all subfields of Artificial intelligence research.
His Pattern recognition research includes elements of Contextual image classification, Object detection, Facial recognition system and Iterative reconstruction.
As a part of the same scientific study, Ming-Hsuan Yang usually deals with the Computer vision, concentrating on Benchmark and frequently concerns with Machine learning.
His biological study spans a wide range of topics, including Histogram, Tracking system and Motion blur.
The study incorporates disciplines such as Visualization, Particle filter, Correlation and Subspace topology in addition to Eye tracking.
His most cited work include:
- Detecting faces in images: a survey (3268 citations)
- Online Object Tracking: A Benchmark (2687 citations)
- Incremental Learning for Robust Visual Tracking (2683 citations)
What are the main themes of his work throughout his whole career to date?
Ming-Hsuan Yang mainly investigates Artificial intelligence, Pattern recognition, Computer vision, Convolutional neural network and Image.
His study in Artificial intelligence focuses on Segmentation, Object, Video tracking, Feature and Discriminative model.
Ming-Hsuan Yang usually deals with Pattern recognition and limits it to topics linked to Eye tracking and Visualization.
His work on Computer vision is being expanded to include thematically relevant topics such as Benchmark.
Ming-Hsuan Yang regularly ties together related areas like Optical flow in his Convolutional neural network studies.
The Deblurring study combines topics in areas such as Kernel density estimation, Kernel and Motion blur.
He most often published in these fields:
- Artificial intelligence (96.63%)
- Pattern recognition (48.81%)
- Computer vision (46.56%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (96.63%)
- Pattern recognition (48.81%)
- Computer vision (46.56%)
In recent papers he was focusing on the following fields of study:
Ming-Hsuan Yang mainly investigates Artificial intelligence, Pattern recognition, Computer vision, Convolutional neural network and Feature.
His study involves Object, Benchmark, Segmentation, Image and Regularization, a branch of Artificial intelligence.
Ming-Hsuan Yang interconnects Pixel, Semantics and Object detection in the investigation of issues within Pattern recognition.
His work on Image warping, Optical flow and Image editing as part of his general Computer vision study is frequently connected to Focus, thereby bridging the divide between different branches of science.
His Convolutional neural network research is multidisciplinary, incorporating elements of Real image, Deblurring, Face and Feature extraction.
His Feature study combines topics from a wide range of disciplines, such as Image synthesis, Content creation and Selection.
Between 2019 and 2021, his most popular works were:
- Res2Net: A New Multi-Scale Backbone Architecture (265 citations)
- Diverse Image-to-Image Translation via Disentangled Representations (131 citations)
- UA-DETRAC: A new benchmark and protocol for multi-object detection and tracking (118 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Convolutional neural network, Feature and Computer vision.
His Machine learning research extends to Artificial intelligence, which is thematically connected.
His Pattern recognition research incorporates themes from Semantics, Image restoration and Object detection.
His Convolutional neural network study integrates concerns from other disciplines, such as Channel, Pixel, Real image and Artificial neural network.
Ming-Hsuan Yang focuses mostly in the field of Feature, narrowing it down to matters related to Embedding and, in some cases, Linear classifier, Closing and Feature learning.
He works mostly in the field of Computer vision, limiting it down to topics relating to Content creation and, in certain cases, Translation.
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