Zobrazeno 1 - 10
of 64
pro vyhledávání: '"Lobry, Sylvain"'
Large language models (LLMs) are being used in data science code generation tasks, but they often struggle with complex sequential tasks, leading to logical errors. Their application to geospatial data processing is particularly challenging due to di
Externí odkaz:
http://arxiv.org/abs/2410.18792
Publikováno v:
15th European Conference on Synthetic Aperture Radar, April 23 26, 2024, Munich, Germany
Remote sensing visual question answering (RSVQA) has been involved in several research in recent years, leading to an increase in new methods. RSVQA automatically extracts information from satellite images, so far only optical, and a question to auto
Externí odkaz:
http://arxiv.org/abs/2408.15642
Autor:
Tosato, Lucrezia, Boussaid, Hichem, Weissgerber, Flora, Kurtz, Camille, Wendling, Laurent, Lobry, Sylvain
Visual Question Answering for Remote Sensing (RSVQA) is a task that aims at answering natural language questions about the content of a remote sensing image. The visual features extraction is therefore an essential step in a VQA pipeline. By incorpor
Externí odkaz:
http://arxiv.org/abs/2407.08669
We wish to define the limits of a classical classification model based on deep learning when applied to abstract images, which do not represent visually identifiable objects.QR codes (Quick Response codes) fall into this category of abstract images:
Externí odkaz:
http://arxiv.org/abs/2307.10677
Publikováno v:
In International Journal of Applied Earth Observation and Geoinformation November 2024 134
Visual question answering (VQA) has recently been introduced to remote sensing to make information extraction from overhead imagery more accessible to everyone. VQA considers a question (in natural language, therefore easy to formulate) about an imag
Externí odkaz:
http://arxiv.org/abs/2109.11848
Publikováno v:
ACCV 2020
Convolutional neural networks (CNN) are known to learn an image representation that captures concepts relevant to the task, but do so in an implicit way that hampers model interpretability. However, one could argue that such a representation is hidde
Externí odkaz:
http://arxiv.org/abs/2009.08720
This paper introduces the task of visual question answering for remote sensing data (RSVQA). Remote sensing images contain a wealth of information which can be useful for a wide range of tasks including land cover classification, object counting or d
Externí odkaz:
http://arxiv.org/abs/2003.07333
A main issue preventing the use of Convolutional Neural Networks (CNN) in end user applications is the low level of transparency in the decision process. Previous work on CNN interpretability has mostly focused either on localizing the regions of the
Externí odkaz:
http://arxiv.org/abs/1909.08442
We present an Active Learning (AL) strategy for re-using a deep Convolutional Neural Network (CNN)-based object detector on a new dataset. This is of particular interest for wildlife conservation: given a set of images acquired with an Unmanned Aeria
Externí odkaz:
http://arxiv.org/abs/1907.07319