What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Machine learning, Computer vision and Discriminative model.
In his articles, Yunde Jia combines various disciplines, including Artificial intelligence and Set.
His research in Pattern recognition intersects with topics in Subspace topology, Facial recognition system, Non-negative matrix factorization and Image representation.
His work deals with themes such as Parsing and Hidden Markov model, which intersect with Machine learning.
His study in the field of Pixel, Stereo cameras and Computer stereo vision is also linked to topics like Energy.
His Discriminative model research is multidisciplinary, incorporating perspectives in Artificial neural network, Sparse matrix and Convolutional neural network.
His most cited work include:
- Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration (307 citations)
- Go-ICP: Solving 3D Registration Efficiently and Globally Optimally (228 citations)
- FISHER NON-NEGATIVE MATRIX FACTORIZATION FOR LEARNING LOCAL FEATURES (156 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Pattern recognition, Computer vision, Discriminative model and Machine learning.
His Artificial intelligence and Feature extraction, Object, Tracking, Motion and Classifier investigations all form part of his Artificial intelligence research activities.
His studies deal with areas such as Subspace topology and Robustness as well as Feature extraction.
His work in Pattern recognition tackles topics such as Feature which are related to areas like Projection.
Yunde Jia focuses mostly in the field of Discriminative model, narrowing it down to topics relating to Representation and, in certain cases, Manifold.
The concepts of his Machine learning study are interwoven with issues in Ambiguity and Metric.
He most often published in these fields:
- Artificial intelligence (84.96%)
- Pattern recognition (42.92%)
- Computer vision (40.27%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (84.96%)
- Pattern recognition (42.92%)
- Object (7.96%)
In recent papers he was focusing on the following fields of study:
Yunde Jia mainly investigates Artificial intelligence, Pattern recognition, Object, Image and Face.
His research integrates issues of Computer vision and Natural language processing in his study of Artificial intelligence.
His Computer vision study combines topics from a wide range of disciplines, such as Robot and Head.
His work on Discriminative model and Feature extraction as part of general Pattern recognition study is frequently linked to Volume, bridging the gap between disciplines.
Yunde Jia interconnects Representation and Inference in the investigation of issues within Object.
His Image research is multidisciplinary, relying on both Algorithm, Frequency domain and Projection.
Between 2018 and 2021, his most popular works were:
- Accurate 3D Face Reconstruction With Weakly-Supervised Learning: From Single Image to Image Set (69 citations)
- Joint Syntax Representation Learning and Visual Cue Translation for Video Captioning (27 citations)
- A deep Coarse-to-Fine network for head pose estimation from synthetic data (26 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Artificial intelligence, Pattern recognition, Face, Artificial neural network and Set are his primary areas of study.
The Artificial intelligence study combines topics in areas such as Context and Graph.
His work in the fields of Discriminative model overlaps with other areas such as Message passing.
His Discriminative model research includes themes of Riemannian manifold, Measure, Similarity, Algorithm and Vectorization.
Real image, Depth map and Robustness is closely connected to Iterative reconstruction in his research, which is encompassed under the umbrella topic of Face.
His Artificial neural network research incorporates themes from Feature extraction, Ranking and Image retrieval.
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