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.
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.
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.
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.
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Unsupervised Domain Adaptation by Domain Invariant Projection
Mahsa Baktashmotlagh;Mahsa Baktashmotlagh;Mehrtash T. Harandi;Mehrtash T. Harandi;Brian C. Lovell;Mathieu Salzmann;Mathieu Salzmann.
international conference on computer vision (2013)
Discrete-Continuous Depth Estimation from a Single Image
Miaomiao Liu;Mathieu Salzmann;Xuming He.
computer vision and pattern recognition (2014)
Beyond Sharing Weights for Deep Domain Adaptation
Artem Rozantsev;Mathieu Salzmann;Pascal Fua.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2019)
Context-Aware Crowd Counting
Weizhe Liu;Mathieu Salzmann;Pascal Fua.
computer vision and pattern recognition (2019)
Kernel Methods on the Riemannian Manifold of Symmetric Positive Definite Matrices
Sadeep Jayasumana;Richard Hartley;Mathieu Salzmann;Hongdong Li.
computer vision and pattern recognition (2013)
Learning the Number of Neurons in Deep Networks
Jose M. Alvarez;Mathieu Salzmann.
neural information processing systems (2016)
Deep Subspace Clustering Networks
Pan Ji;Tong Zhang;Hongdong Li;Mathieu Salzmann.
neural information processing systems (2017)
Learning to Find Good Correspondences
Kwang Moo Yi;Eduard Trulls;Yuki Ono;Vincent Lepetit.
computer vision and pattern recognition (2018)
Evaluating The Search Phase of Neural Architecture Search
Kaicheng Yu;Christian Sciuto;Martin Jaggi;Claudiu Musat.
international conference on learning representations (2020)
Structured Prediction of 3D Human Pose with Deep Neural Networks
Bugra Tekin;Isinsu Katircioglu;Mathieu Salzmann;Vincent Lepetit.
british machine vision conference (2016)
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