What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
His main research concerns Artificial intelligence, Machine learning, Pattern recognition, Latent variable and Discriminative model.
His study in Artificial intelligence focuses on Cognitive neuroscience of visual object recognition, Inference, Conditional random field, Training set and Parsing.
In Cognitive neuroscience of visual object recognition, Yang Wang works on issues like Pattern recognition, which are connected to Topic model, Hidden Markov model, Bag-of-words model and Contextual image classification.
His Machine learning research is multidisciplinary, incorporating elements of Linear programming and Classifier.
In his study, Histogram and Boosting is strongly linked to Pose, which falls under the umbrella field of Pattern recognition.
His Latent variable course of study focuses on Feature extraction and Feature.
His most cited work include:
- Human Action Recognition by Semilatent Topic Models (278 citations)
- Discriminative Latent Models for Recognizing Contextual Group Activities (234 citations)
- Discriminative figure-centric models for joint action localization and recognition (214 citations)
What are the main themes of his work throughout his whole career to date?
Yang Wang mainly investigates Artificial intelligence, Pattern recognition, Machine learning, Computer vision and Segmentation.
His is doing research in Benchmark, Object, Image, Discriminative model and Deep learning, both of which are found in Artificial intelligence.
His study in the field of Feature extraction also crosses realms of Modal.
His Machine learning study frequently intersects with other fields, such as Training set.
Yang Wang interconnects Labeled data and Adaptation in the investigation of issues within Computer vision.
His research investigates the connection between Segmentation and topics such as Pascal that intersect with problems in Image labeling.
He most often published in these fields:
- Artificial intelligence (90.84%)
- Pattern recognition (40.46%)
- Machine learning (31.30%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (90.84%)
- Computer vision (25.95%)
- Benchmark (18.32%)
In recent papers he was focusing on the following fields of study:
Yang Wang focuses on Artificial intelligence, Computer vision, Benchmark, Pattern recognition and Shot.
His Artificial intelligence study often links to related topics such as Machine learning.
As part of one scientific family, Yang Wang deals mainly with the area of Computer vision, narrowing it down to issues related to the Adaptation, and often Crowd counting and Code.
His Benchmark study integrates concerns from other disciplines, such as Depth map, Unsupervised learning and Pose.
His Segmentation study in the realm of Pattern recognition connects with subjects such as Modal and Focus.
His biological study spans a wide range of topics, including Anomaly detection and Meta learning.
Between 2019 and 2021, his most popular works were:
- Cross-Modal Weighting Network for RGB-D Salient Object Detection. (12 citations)
- Few-Shot Scene-Adaptive Anomaly Detection (6 citations)
- Few-Shot Scene-Adaptive Anomaly Detection (6 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Yang Wang mainly focuses on Artificial intelligence, Meta learning, Shot, Pattern recognition and Machine learning.
His study in the field of Feature and Pixel is also linked to topics like Weighting, Modal and Block.
His study in Meta learning is interdisciplinary in nature, drawing from both Crowd counting, Adaptation and Computer vision.
His Computer vision study combines topics from a wide range of disciplines, such as Labeled data and Code.
His research investigates the link between Pattern recognition and topics such as Image that cross with problems in Margin, Visualization, Feature extraction and Artificial neural network.
His Anomaly detection research extends to Machine learning, which is thematically connected.
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