What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
His primary scientific interests are in Artificial intelligence, Pattern recognition, Computer vision, Image and Machine learning.
Many of his studies involve connections with topics such as Surface and Artificial intelligence.
His work on Convolutional neural network as part of general Pattern recognition research is frequently linked to Domain adaptation, Nonlinear system and Property, thereby connecting diverse disciplines of science.
His study in the field of Segmentation and Iterative reconstruction is also linked to topics like ENCODE and Contrast.
His studies deal with areas such as Matching, Graphical model, Unary operation and Belief propagation as well as Image.
His Ranking study in the realm of Machine learning connects with subjects such as Process.
His most cited work include:
- Unsupervised Domain Adaptation by Domain Invariant Projection (308 citations)
- Unsupervised Domain Adaptation by Domain Invariant Projection (308 citations)
- Discrete-Continuous Depth Estimation from a Single Image (267 citations)
What are the main themes of his work throughout his whole career to date?
Mathieu Salzmann focuses on Artificial intelligence, Computer vision, Pattern recognition, Machine learning and Segmentation.
His study in Image, Deep learning, Pose, Monocular and Representation is done as part of Artificial intelligence.
Mathieu Salzmann has researched Computer vision in several fields, including Surface, Polygon mesh and Robustness.
The various areas that Mathieu Salzmann examines in his Pattern recognition study include Object, Cognitive neuroscience of visual object recognition and Leverage.
His Machine learning research incorporates elements of Training set, Inference and Benchmark.
He interconnects Artificial neural network, Object detection and Algorithm in the investigation of issues within Segmentation.
He most often published in these fields:
- Artificial intelligence (82.75%)
- Computer vision (30.99%)
- Pattern recognition (30.28%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (82.75%)
- Pattern recognition (30.28%)
- Algorithm (15.14%)
In recent papers he was focusing on the following fields of study:
Mathieu Salzmann spends much of his time researching Artificial intelligence, Pattern recognition, Algorithm, Computer vision and Deep learning.
Artificial intelligence and Machine learning are commonly linked in his work.
His research in the fields of Convolutional neural network and Unsupervised learning overlaps with other disciplines such as ENCODE.
His Algorithm research is multidisciplinary, incorporating perspectives in Parametric surface, Polygon mesh, Surface reconstruction, Curvature and Image stitching.
The concepts of his Deep learning study are interwoven with issues in Computation, Least squares and Motion capture.
His study in Image is interdisciplinary in nature, drawing from both Flow, Active learning and Feature extraction.
Between 2019 and 2021, his most popular works were:
- Evaluating The Search Phase of Neural Architecture Search (131 citations)
- Single-Stage 6D Object Pose Estimation (16 citations)
- A Stochastic Conditioning Scheme for Diverse Human Motion Prediction (12 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Mathieu Salzmann mostly deals with Artificial intelligence, Pattern recognition, Algorithm, Deep learning and Machine learning.
Mathieu Salzmann combines subjects such as Sequence, Key and Computer vision with his study of Artificial intelligence.
His Pattern recognition study incorporates themes from Object and Image processing, Image.
His study explores the link between Algorithm and topics such as Leverage that cross with problems in Point cloud, Shape reconstruction, Surface reconstruction, Metric tensor and Differentiable function.
His Deep learning study integrates concerns from other disciplines, such as Visualization, Unsupervised learning, Motion capture and Blossom algorithm.
The Machine learning study combines topics in areas such as Simple random sample, Heuristics, Similarity and Search algorithm.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
