Data for Paper "Scalable Semantic 3D Mapping of Coral Reefs with Deep Learning"

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This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...

Deep learning has revolutionized the field of computer vision, a success largely attributable to the growing size of models, datasets, and computational power. Simultaneously, a critical pain point arises as several computer vision applications are deploye ...

Deep learning has emerged as a promising avenue for automatic mapping, demonstrating high efficacy in land cover categorization through various semantic segmentation models. Nonetheless, the practical deployment of these models encounters important challen ...

Recent advancements in deep learning have revolutionized 3D computer vision, enabling the extraction of intricate 3D information from 2D images and video sequences. This thesis explores the application of deep learning in three crucial challenges of 3D com ...

Monitoring forests, in particular their response to climate and land use change, requires studying long time scales. While efficient deep learning methods have been developed to process short time series of satellite imagery, leveraging long time series of ...

The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...

Video DeepFakes are fake media created with Deep Learning (DL) that manipulate a person’s expression or identity. Most current DeepFake detection methods analyze each frame independently, ignoring inconsistencies and unnatural movements between frames. Som ...

In this thesis we explore the applications of projective geometry, a mathematical theory of the relation between 3D scenes and their 2D images, in modern learning-based computer vision systems. This is an interesting research question which contradicts the ...

We propose a novel system leveraging deep learning-based methods to predict urban traffic accidents and estimate their severity. The major challenge is the data imbalance problem in traffic accident prediction. The problem is caused by numerous zero values ...

The successes of deep learning for semantic segmentation can in be, in part, attributed to its scale: a notion that encapsulates the largeness of these computational architectures and the labeled datasets they are trained on. These resource requirements hi ...

Test time augmentation has been shown to be an effective approach to combat domain shifts in deep learning. Despite their promising performance levels, the interpretability of the underlying used models is however low. Saliency maps have been widely used i ...

Discovering new materials is essential but challenging, time-consuming, and expensive. In many cases, simulations can be useful for estimating material properties. For many of the most interesting properties, however, simulations are infeasible because of ...

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really gener ...

Motivation: Host to intricate networks of marine species, coral reefs are among the most biologically diverse ecosystems on Earth. Over the past few decades, major degradations of coral reefs have been observed worldwide, which is largely attributed to the ...

We address the problem of segmenting anomalies and unusual obstacles in road scenes for the purpose of self-driving safety. The objects in question are not present in the common training sets as it is not feasible to collect and annotate examples for every ...

Artificial intelligence, particularly the subfield of machine learning, has seen a paradigm shift towards data-driven models that learn from and adapt to data. This has resulted in unprecedented advancements in various domains such as natural language proc ...

This paper introduces TACOSS a text-image alignment approach that allows explainable land cover semantic segmentation by directly integrating semantic concepts encoded from texts. TACOSS combines convolutional neural networks for visual feature extraction ...

Natural language processing and other artificial intelligence fields have witnessed impressive progress over the past decade. Although some of this progress is due to algorithmic advances in deep learning, the majority has arguably been enabled by scaling ...

In this thesis, we explore techniques for addressing the communication bottleneck in data-parallel distributed training of deep learning models. We investigate algorithms that either reduce the size of the messages that are exchanged between workers, or th ...

Recent successes in deep learning for vision and natural language processing are attributed to larger models but come with energy consumption and scalability issues. Current training of digital deep-learning models primarily relies on backpropagation that ...

Data for Paper "Scalable Semantic 3D Mapping of Coral Reefs with Deep Learning"

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Partial discharge localization in power transformer tanks using machine learning methods

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

Land Cover Mapping From Multiple Complementary Experts Under Heavy Class Imbalance

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

Towards Trustworthy Deep Learning for Image Reconstruction

Efficient Temporally-Aware DeepFake Detection using H.264 Motion Vectors

Incorporating Projective Geometry into Deep Learning

From Prediction to Prevention: Leveraging Deep Learning in Traffic Accident Prediction Systems

Confidence Matters: Applications to Semantic Segmentation

SAGTTA: SALIENCY GUIDED TEST TIME AUGMENTATION FOR MEDICAL IMAGE SEGMENTATION ACROSS VENDOR DOMAIN SHIFT

Toward data-driven materials design: From atoms to pilot plants

Why is the winner the best?

The Reef Environment Centralized InFormation System (RECIFS) : An integrated geo‐environmental database for coral reef research and conservation

Detecting Anomalies and Obstacles in Road Scenes

Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations

Text as a Richer Source of Supervision in Semantic Segmentation Tasks

Text Representation Learning for Low Cost Natural Language Understanding

Communication-efficient distributed training of machine learning models

Backpropagation-free training of deep physical neural networks