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pro vyhledávání: '"Liu Chenyang"'
Remote sensing image change captioning (RSICC) aims to articulate the changes in objects of interest within bi-temporal remote sensing images using natural language. Given the limitations of current RSICC methods in expressing general features across
Externí odkaz:
http://arxiv.org/abs/2407.14032
Recently, the Mamba architecture based on state space models has demonstrated remarkable performance in a series of natural language processing tasks and has been rapidly applied to remote sensing change detection (CD) tasks. However, most methods en
Externí odkaz:
http://arxiv.org/abs/2406.04207
Multi-view segmentation in Remote Sensing (RS) seeks to segment images from diverse perspectives within a scene. Recent methods leverage 3D information extracted from an Implicit Neural Field (INF), bolstering result consistency across multiple views
Externí odkaz:
http://arxiv.org/abs/2405.14171
The recent advancement of generative foundational models has ushered in a new era of image generation in the realm of natural images, revolutionizing art design, entertainment, environment simulation, and beyond. Despite producing high-quality sample
Externí odkaz:
http://arxiv.org/abs/2405.13570
Remote Sensing Image Change Captioning (RSICC) aims to describe surface changes between multi-temporal remote sensing images in language, including the changed object categories, locations, and dynamics of changing objects (e.g., added or disappeared
Externí odkaz:
http://arxiv.org/abs/2404.18895
Remote sensing image classification forms the foundation of various understanding tasks, serving a crucial function in remote sensing image interpretation. The recent advancements of Convolutional Neural Networks (CNNs) and Transformers have markedly
Externí odkaz:
http://arxiv.org/abs/2403.19654
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing 2024
Monitoring changes in the Earth's surface is crucial for understanding natural processes and human impacts, necessitating precise and comprehensive interpretation methodologies. Remote sensing satellite imagery offers a unique perspective for monitor
Externí odkaz:
http://arxiv.org/abs/2403.19646
The existing methods for Remote Sensing Image Change Captioning (RSICC) perform well in simple scenes but exhibit poorer performance in complex scenes. This limitation is primarily attributed to the model's constrained visual ability to distinguish a
Externí odkaz:
http://arxiv.org/abs/2312.15311
Publikováno v:
Anti-Corrosion Methods and Materials, 2024, Vol. 71, Issue 6, pp. 809-819.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/ACMM-07-2024-3050
Recently, utilizing deep neural networks to build the opendomain dialogue models has become a hot topic. However, the responses generated by these models suffer from many problems such as responses not being contextualized and tend to generate generi
Externí odkaz:
http://arxiv.org/abs/2309.02823