DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific
Visualization
- URL: http://arxiv.org/abs/2204.06504v1
- Date: Wed, 13 Apr 2022 16:42:32 GMT
- Title: DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific
Visualization
- Authors: Chaoli Wang and Jun Han
- Abstract summary: We survey related deep learning (DL) works in SciVis, specifically in the direction of DL4SciVis.
We classify and discuss these works along six dimensions: domain setting, research task, learning type, network architecture, loss function, and evaluation metric.
This state-of-the-art survey guides SciVis researchers in gaining an overview of this emerging topic and points out future directions to grow this research.
- Score: 15.080469766866907
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Since 2016, we have witnessed the tremendous growth of artificial
intelligence+visualization (AI+VIS) research. However, existing survey papers
on AI+VIS focus on visual analytics and information visualization, not
scientific visualization (SciVis). In this paper, we survey related deep
learning (DL) works in SciVis, specifically in the direction of DL4SciVis:
designing DL solutions for solving SciVis problems. To stay focused, we
primarily consider works that handle scalar and vector field data but exclude
mesh data. We classify and discuss these works along six dimensions: domain
setting, research task, learning type, network architecture, loss function, and
evaluation metric. The paper concludes with a discussion of the remaining gaps
to fill along the discussed dimensions and the grand challenges we need to
tackle as a community. This state-of-the-art survey guides SciVis researchers
in gaining an overview of this emerging topic and points out future directions
to grow this research.
Related papers
- SPIQA: A Dataset for Multimodal Question Answering on Scientific Papers [43.18330795060871]
SPIQA is a dataset specifically designed to interpret complex figures and tables within the context of scientific research articles.
We employ automatic and manual curation to create the dataset.
SPIQA comprises 270K questions divided into training, validation, and three different evaluation splits.
arXiv Detail & Related papers (2024-07-12T16:37:59Z) - Evaluating ChatGPT-4 Vision on Brazil's National Undergraduate Computer Science Exam [0.0]
This study investigates the performance of ChatGPT-4 Vision, OpenAI's most advanced visual model.
By presenting the model with the exam's open and multiple-choice questions in their original image format, we were able to evaluate the model's reasoning and self-reflecting capabilities.
ChatGPT-4 Vision significantly outperformed the average exam participant, positioning itself within the top 10 best score percentile.
arXiv Detail & Related papers (2024-06-14T02:42:30Z) - MASSW: A New Dataset and Benchmark Tasks for AI-Assisted Scientific Workflows [58.56005277371235]
We introduce MASSW, a comprehensive text dataset on Multi-Aspect Summarization of ScientificAspects.
MASSW includes more than 152,000 peer-reviewed publications from 17 leading computer science conferences spanning the past 50 years.
We demonstrate the utility of MASSW through multiple novel machine-learning tasks that can be benchmarked using this new dataset.
arXiv Detail & Related papers (2024-06-10T15:19:09Z) - A Comprehensive Survey on Underwater Image Enhancement Based on Deep Learning [51.7818820745221]
Underwater image enhancement (UIE) presents a significant challenge within computer vision research.
Despite the development of numerous UIE algorithms, a thorough and systematic review is still absent.
arXiv Detail & Related papers (2024-05-30T04:46:40Z) - Milestones in Autonomous Driving and Intelligent Vehicles: Survey of
Surveys [71.28049144033773]
We propose a Survey of Surveys (SoS) for total technologies of autonomous driving (AD) and intelligent vehicles (IVs)
This article is the first SoS with milestones in AD and IVs, which constitutes our complete research work together with two other technical surveys.
arXiv Detail & Related papers (2023-03-30T08:31:22Z) - The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations [0.0]
Machine learning (ML) models are nowadays used in complex applications in various domains, such as medicine, bioinformatics, and other sciences.
Due to their black box nature, however, it may sometimes be hard to understand and trust the results they provide.
This has increased the demand for reliable visualization tools related to enhancing trust in ML models.
We present a State-of-the-Art Report (STAR) on enhancing trust in ML models with the use of interactive visualization.
arXiv Detail & Related papers (2022-12-22T14:29:43Z) - Attention is All They Need: Exploring the Media Archaeology of the Computer Vision Research Paper [4.968848569103028]
We study changes in computer vision over the past decade, as the deep learning revolution has driven unprecedented growth in the discipline.
Our analysis focuses on the research attention economy: how research paper elements contribute towards advertising, measuring and disseminating an increasingly commodified contribution''
arXiv Detail & Related papers (2022-09-22T17:42:44Z) - Deep Depth Completion: A Survey [26.09557446012222]
We provide a comprehensive literature review that helps readers better grasp the research trends and clearly understand the current advances.
We investigate the related studies from the design aspects of network architectures, loss functions, benchmark datasets, and learning strategies.
We present a quantitative comparison of model performance on two widely used benchmark datasets, including an indoor and an outdoor dataset.
arXiv Detail & Related papers (2022-05-11T08:24:00Z) - Fine-Grained Image Analysis with Deep Learning: A Survey [146.22351342315233]
Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition.
This paper attempts to re-define and broaden the field of FGIA by consolidating two fundamental fine-grained research areas -- fine-grained image recognition and fine-grained image retrieval.
arXiv Detail & Related papers (2021-11-11T09:43:56Z) - 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) - From Symbols to Embeddings: A Tale of Two Representations in
Computational Social Science [77.5409807529667]
The study of Computational Social Science (CSS) is data-driven and significantly benefits from the availability of online user-generated contents and social networks.
To explore the answer, we give a thorough review of data representations in CSS for both text and network.
We present the applications of the above representations based on the investigation of more than 400 research articles from 6 top venues involved with CSS.
arXiv Detail & Related papers (2021-06-27T11:04:44Z)
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.