What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
His primary areas of study are Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Machine learning.
Yihong Gong combines subjects such as Matrix decomposition and Data mining with his study of Artificial intelligence.
His work focuses on many connections between Pattern recognition and other disciplines, such as Object detection, that overlap with his field of interest in Pruning, Object Class and Simulated annealing.
He usually deals with Computer vision and limits it to topics linked to Metric and Edge detection and Image resolution.
His studies in Feature extraction integrate themes in fields like Object, Motion, Convolutional neural network and Robustness.
His Contextual image classification research integrates issues from Histogram and Neural coding.
His most cited work include:
- Linear spatial pyramid matching using sparse coding for image classification (2669 citations)
- Locality-constrained Linear Coding for image classification (2464 citations)
- Document clustering based on non-negative matrix factorization (1421 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Computer vision, Pattern recognition, Machine learning and Convolutional neural network are his primary areas of study.
His work in Discriminative model, Feature extraction, Image, Contextual image classification and Artificial neural network are all subfields of Artificial intelligence research.
His work on Video tracking, Superresolution, Tracking and Image resolution as part of his general Computer vision study is frequently connected to Trajectory, thereby bridging the divide between different branches of science.
His studies deal with areas such as Object detection and Feature as well as Pattern recognition.
His work in Machine learning addresses issues such as Benchmark, which are connected to fields such as MNIST database.
Yihong Gong interconnects Cognitive neuroscience of visual object recognition, Similarity, Margin, Face and Deep learning in the investigation of issues within Convolutional neural network.
He most often published in these fields:
- Artificial intelligence (81.33%)
- Computer vision (36.10%)
- Pattern recognition (36.10%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (81.33%)
- Pattern recognition (36.10%)
- Machine learning (22.41%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Pattern recognition, Machine learning, Convolutional neural network and Computer vision are his primary areas of study.
His research in the fields of Feature extraction and Anomaly detection overlaps with other disciplines such as Masking.
His work investigates the relationship between Feature extraction and topics such as Contextual image classification that intersect with problems in Softmax function.
His work on Ranking as part of general Machine learning study is frequently connected to Code, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His Convolutional neural network study incorporates themes from Similarity, Feature vector, Probabilistic logic, Statistical model and Range.
His study in the field of Tracking, Matching and RGB color model is also linked to topics like Trajectory.
Between 2018 and 2021, his most popular works were:
- Bayesian Loss for Crowd Count Estimation With Point Supervision (91 citations)
- Infrared-Visible Cross-Modal Person Re-Identification with an X Modality (29 citations)
- Discriminative Feature Learning With Foreground Attention for Person Re-Identification (20 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Feature vector, Margin and Feature extraction.
The study of Artificial intelligence is intertwined with the study of Machine learning in a number of ways.
Yihong Gong is involved in the study of Pattern recognition that focuses on Discriminative model in particular.
The concepts of his Feature vector study are interwoven with issues in Similarity, Convolutional neural network, Hebbian theory and Topology.
His Convolutional neural network research integrates issues from Contextual image classification and Training set.
His Margin research focuses on Ground truth and how it relates to Function, Algorithm, Kernel and Kernel.
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