Deep metric learning with angular loss
The modern image search system requires semantic understanding of image, and a key yet
under-addressed problem is to learn a good metric for measuring the similarity between …
under-addressed problem is to learn a good metric for measuring the similarity between …
Optical flow modeling and computation: A survey
D Fortun, P Bouthemy, C Kervrann - Computer Vision and Image …, 2015 - Elsevier
Optical flow estimation is one of the oldest and still most active research domains in
computer vision. In 35 years, many methodological concepts have been introduced and …
computer vision. In 35 years, many methodological concepts have been introduced and …
Markov random field modeling, inference & learning in computer vision & image understanding: A survey
In this paper, we present a comprehensive survey of Markov Random Fields (MRFs) in
computer vision and image understanding, with respect to the modeling, the inference and …
computer vision and image understanding, with respect to the modeling, the inference and …
A comparative study of modern inference techniques for structured discrete energy minimization problems
Szeliski et al. published an influential study in 2006 on energy minimization methods for
Markov random fields. This study provided valuable insights in choosing the best …
Markov random fields. This study provided valuable insights in choosing the best …
A comparative study of modern inference techniques for discrete energy minimization problems
Seven years ago, Szeliski et al. published an influential study on energy minimization
methods for Markov random fields (MRF). This study provided valuable insights in choosing …
methods for Markov random fields (MRF). This study provided valuable insights in choosing …
The atlas for the aspiring network scientist
M Coscia - arXiv preprint arXiv:2101.00863, 2021 - arxiv.org
Network science is the field dedicated to the investigation and analysis of complex systems
via their representations as networks. We normally model such networks as graphs: sets of …
via their representations as networks. We normally model such networks as graphs: sets of …
Higher order energies for image segmentation
A novel energy minimization method for general higher order binary energy functions is
proposed in this paper. We first relax a discrete higher order function to a continuous one …
proposed in this paper. We first relax a discrete higher order function to a continuous one …
Associative hierarchical random fields
This paper makes two contributions: the first is the proposal of a new model—The
associative hierarchical random field (AHRF), and a novel algorithm for its optimization; the …
associative hierarchical random field (AHRF), and a novel algorithm for its optimization; the …
Quadratic reformulations of nonlinear binary optimization problems
Very large nonlinear unconstrained binary optimization problems arise in a broad array of
applications. Several exact or heuristic techniques have proved quite successful for solving …
applications. Several exact or heuristic techniques have proved quite successful for solving …
Quadratization in discrete optimization and quantum mechanics
N Dattani - arXiv preprint arXiv:1901.04405, 2019 - arxiv.org
A book about turning high-degree optimization problems into quadratic optimization
problems that maintain the same global minimum (ground state). This book explores …
problems that maintain the same global minimum (ground state). This book explores …