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David Mohlin

dblp:267/9504 · DBLP profile ↗
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2ranked-venue papers
2as first author
1since 2021 · last 2021
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 2 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
3D vision · 70% Probabilistic and Bayesian machine learning · 23% Learning paradigms · 7%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision › pose estimation
orientation estimation		 0.412020
Probabilistic Orientation Estimation with Matrix Fisher Distributions · NeurIPS 2020
Computer vision › 3D vision
pose estimation		 0.412020
Probabilistic Orientation Estimation with Matrix Fisher Distributions · NeurIPS 2020
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › regression
probabilistic regression		 0.412020
Probabilistic Orientation Estimation with Matrix Fisher Distributions · NeurIPS 2020
Computer vision › 3D vision › pose estimation
rotation estimation		 0.412020
Probabilistic Orientation Estimation with Matrix Fisher Distributions · NeurIPS 2020
Machine learning › Learning paradigms › supervised learning
neural network regression		 0.112020
Probabilistic Orientation Estimation with Matrix Fisher Distributions · NeurIPS 2020

Methods — techniques the papers use, named apart from their topics

negative log-likelihood loss · 0.4matrix fisher distribution · 0.4
YearPublicationVenuePosition
2021 Probabilistic Regression with Huber Distributions
David Mohlin, Gérald Bianchi, Josephine Sullivan
 BMVC1
2020 Probabilistic Orientation Estimation with Matrix Fisher Distributions
abstract
This paper focuses on estimating probability distributions over the set of 3D ro- tations (SO(3)) using deep neural networks. Learning to regress models to the set of rotations is inherently difficult due to differences in topology between R^N and SO(3). We overcome this issue by using a neural network to out- put the parameters for a matrix Fisher distribution since these parameters are homeomorphic to R^9 . By using a negative log likelihood loss for this distri- bution we get a loss which is convex with respect to the network outputs. By optimizing this loss we improve state-of-the-art on several challenging applica- ble datasets, namely Pascal3D+, ModelNet10-SO(3). Our code is available at https://github.com/Davmo049/Publicproborientationestimationwith_matrix fisherdistributions
David Mohlin, Josephine Sullivan, Gérald Bianchi
 NeurIPS1