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Found 11 papers, 5 papers with code
Change-Agent: Towards Interactive Comprehensive Remote Sensing Change Interpretation and Analysis
The Change-Agent integrates a multi-level change interpretation (MCI) model as the eyes and a large language model (LLM) as the brain.
RSMamba: Remote Sensing Image Classification with State Space Model
Remote sensing image classification forms the foundation of various understanding tasks, serving a crucial function in remote sensing image interpretation.
Learning to detect cloud and snow in remote sensing images from noisy labels
This paper is the first to consider the impact of label noise on the detection of clouds and snow in remote sensing images.
Pixel-Level Change Detection Pseudo-Label Learning for Remote Sensing Change Captioning
The existing methods for Remote Sensing Image Change Captioning (RSICC) perform well in simple scenes but exhibit poorer performance in complex scenes.
Promoting Open-domain Dialogue Generation through Learning Pattern Information between Contexts and Responses
In this paper, we first build an open-domain dialogue model based on the pre-trained language model (i. e., GPT-2).
RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model
We also propose several ongoing derivatives for instance segmentation tasks, drawing on recent advancements within the SAM community, and compare their performance with RSPrompter.
Img2Vec: A Teacher of High Token-Diversity Helps Masked AutoEncoders
To study which type of deep features is appropriate for MIM as a learning target, we propose a simple MIM framework with serials of well-trained self-supervised models to convert an Image to a feature Vector as the learning target of MIM, where the feature extractor is also known as a teacher model.
Implicit Ray-Transformers for Multi-view Remote Sensing Image Segmentation
In the first stage, we optimize a neural field to encode the color and 3D structure of the remote sensing scene based on multi-view images.
Progressive Scale-aware Network for Remote sensing Image Change Captioning
To sufficiently utilize the extracted multi-scale features for captioning, we propose a scale-aware reinforcement (SR) module and combine it with the Transformer decoding layer to progressively utilize the features from different PDP layers.
Coarse-Super-Resolution-Fine Network (CoSF-Net): A Unified End-to-End Neural Network for 4D-MRI with Simultaneous Motion Estimation and Super-Resolution
Four-dimensional magnetic resonance imaging (4D-MRI) is an emerging technique for tumor motion management in image-guided radiation therapy (IGRT).
Semi-synthesis: A fast way to produce effective datasets for stereo matching
Though synthetic dataset is proposed to fill the gaps of large data demand, the fine-tuning on real dataset is still needed due to the domain variances between synthetic data and real data.
