Search Results for author:
Found 8 papers, 3 papers with code
ChatEarthNet: A Global-Scale Image-Text Dataset Empowering Vision-Language Geo-Foundation Models
In this context, we introduce a global-scale, high-quality image-text dataset for remote sensing, providing natural language descriptions for Sentinel-2 data to facilitate the understanding of satellite imagery for common users.
RRSIS: Referring Remote Sensing Image Segmentation
Localizing desired objects from remote sensing images is of great use in practical applications.
Overcoming Language Bias in Remote Sensing Visual Question Answering via Adversarial Training
Based on the adversarial branch, we introduce two regularizers to constrain the training process against language bias.
Vision-Language Models in Remote Sensing: Current Progress and Future Trends
Existing AI-related research in remote sensing primarily focuses on visual understanding tasks while neglecting the semantic understanding of the objects and their relationships.
Multilingual Augmentation for Robust Visual Question Answering in Remote Sensing Images
With the proposed augmented dataset, we are able to obtain more questions in addition to the original ones with the same meaning.
From Easy to Hard: Learning Language-guided Curriculum for Visual Question Answering on Remote Sensing Data
To be more specific, a language-guided SPCL method with a soft weighting strategy is explored in this work.
Change Detection Meets Visual Question Answering
In order to provide every user with flexible access to change information and help them better understand land-cover changes, we introduce a novel task: change detection-based visual question answering (CDVQA) on multi-temporal aerial images.
GETNET: A General End-to-end Two-dimensional CNN Framework for Hyperspectral Image Change Detection
In order to better handle high dimension problem and explore abundance information, this paper presents a General End-to-end Two-dimensional CNN (GETNET) framework for hyperspectral image change detection (HSI-CD).
