Hyperbolic Deep Learning in Computer Vision: A Survey
- URL: http://arxiv.org/abs/2305.06611v1
- Date: Thu, 11 May 2023 07:14:23 GMT
- Title: Hyperbolic Deep Learning in Computer Vision: A Survey
- Authors: Pascal Mettes, Mina Ghadimi Atigh, Martin Keller-Ressel, Jeffrey Gu,
Serena Yeung
- Abstract summary: hyperbolic space has gained rapid traction for learning in computer vision.
We provide a categorization and in-depth overview of current literature on hyperbolic learning for computer vision.
We outline how hyperbolic learning is performed in all themes and discuss the main research problems that benefit from current advances in hyperbolic learning for computer vision.
- Score: 20.811974050049365
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep representation learning is a ubiquitous part of modern computer vision.
While Euclidean space has been the de facto standard manifold for learning
visual representations, hyperbolic space has recently gained rapid traction for
learning in computer vision. Specifically, hyperbolic learning has shown a
strong potential to embed hierarchical structures, learn from limited samples,
quantify uncertainty, add robustness, limit error severity, and more. In this
paper, we provide a categorization and in-depth overview of current literature
on hyperbolic learning for computer vision. We research both supervised and
unsupervised literature and identify three main research themes in each
direction. We outline how hyperbolic learning is performed in all themes and
discuss the main research problems that benefit from current advances in
hyperbolic learning for computer vision. Moreover, we provide a high-level
intuition behind hyperbolic geometry and outline open research questions to
further advance research in this direction.
Related papers
- Integration and Performance Analysis of Artificial Intelligence and
Computer Vision Based on Deep Learning Algorithms [5.734290974917728]
This paper focuses on the analysis of the application effectiveness of the integration of deep learning and computer vision technologies.
Deep learning achieves a historic breakthrough by constructing hierarchical neural networks, enabling end-to-end feature learning and semantic understanding of images.
The successful experiences in the field of computer vision provide strong support for training deep learning algorithms.
arXiv Detail & Related papers (2023-12-20T09:37:06Z) - Deep Learning to See: Towards New Foundations of Computer Vision [88.69805848302266]
This book criticizes the supposed scientific progress in the field of computer vision.
It proposes the investigation of vision within the framework of information-based laws of nature.
arXiv Detail & Related papers (2022-06-30T15:20:36Z) - Neural Fields in Visual Computing and Beyond [54.950885364735804]
Recent advances in machine learning have created increasing interest in solving visual computing problems using coordinate-based neural networks.
neural fields have seen successful application in the synthesis of 3D shapes and image, animation of human bodies, 3D reconstruction, and pose estimation.
This report provides context, mathematical grounding, and an extensive review of literature on neural fields.
arXiv Detail & Related papers (2021-11-22T18:57:51Z) - Deep Reinforcement Learning in Computer Vision: A Comprehensive Survey [29.309914600633032]
Deep reinforcement learning augments the reinforcement learning framework and utilizes the powerful representation of deep neural networks.
Recent works have demonstrated the remarkable successes of deep reinforcement learning in various domains including finance, medicine, healthcare, video games, robotics, and computer vision.
arXiv Detail & Related papers (2021-08-25T23:01:48Z) - Threat of Adversarial Attacks on Deep Learning in Computer Vision:
Survey II [86.51135909513047]
Deep Learning is vulnerable to adversarial attacks that can manipulate its predictions.
This article reviews the contributions made by the computer vision community in adversarial attacks on deep learning.
It provides definitions of technical terminologies for non-experts in this domain.
arXiv Detail & Related papers (2021-08-01T08:54:47Z) - Tensor Methods in Computer Vision and Deep Learning [120.3881619902096]
tensors, or multidimensional arrays, are data structures that can naturally represent visual data of multiple dimensions.
With the advent of the deep learning paradigm shift in computer vision, tensors have become even more fundamental.
This article provides an in-depth and practical review of tensors and tensor methods in the context of representation learning and deep learning.
arXiv Detail & Related papers (2021-07-07T18:42:45Z) - Deep Learning for Embodied Vision Navigation: A Survey [108.13766213265069]
"Embodied visual navigation" problem requires an agent to navigate in a 3D environment mainly rely on its first-person observation.
This paper attempts to establish an outline of the current works in the field of embodied visual navigation by providing a comprehensive literature survey.
arXiv Detail & Related papers (2021-07-07T12:09:04Z) - Hyperbolic Deep Neural Networks: A Survey [31.04110049167551]
We refer to the model as hyperbolic deep neural network in this paper.
To stimulate future research, this paper presents acoherent and comprehensive review of the literature around the neural components in the construction of hyperbolic deep neuralnetworks.
arXiv Detail & Related papers (2021-01-12T15:55:16Z) - Learning Depth With Very Sparse Supervision [57.911425589947314]
This paper explores the idea that perception gets coupled to 3D properties of the world via interaction with the environment.
We train a specialized global-local network architecture with what would be available to a robot interacting with the environment.
Experiments on several datasets show that, when ground truth is available even for just one of the image pixels, the proposed network can learn monocular dense depth estimation up to 22.5% more accurately than state-of-the-art approaches.
arXiv Detail & Related papers (2020-03-02T10:44:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.