A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends
Deep supervised learning algorithms typically require a large volume of labeled data to
achieve satisfactory performance. However, the process of collecting and labeling such data …
achieve satisfactory performance. However, the process of collecting and labeling such data …
Vision-language models in remote sensing: Current progress and future trends
The remarkable achievements of ChatGPT and Generative Pre-trained Transformer 4 (GPT-
4) have sparked a wave of interest and research in the field of large language models …
4) have sparked a wave of interest and research in the field of large language models …
Large selective kernel network for remote sensing object detection
Recent research on remote sensing object detection has largely focused on improving the
representation of oriented bounding boxes but has overlooked the unique prior knowledge …
representation of oriented bounding boxes but has overlooked the unique prior knowledge …
Rtmdet: An empirical study of designing real-time object detectors
In this paper, we aim to design an efficient real-time object detector that exceeds the YOLO
series and is easily extensible for many object recognition tasks such as instance …
series and is easily extensible for many object recognition tasks such as instance …
SpectralGPT: Spectral foundation model
The foundation model has recently garnered significant attention due to its potential to
revolutionize the field of visual representation learning in a self-supervised manner. While …
revolutionize the field of visual representation learning in a self-supervised manner. While …
Adaptive rotated convolution for rotated object detection
Rotated object detection aims to identify and locate objects in images with arbitrary
orientation. In this scenario, the oriented directions of objects vary considerably across …
orientation. In this scenario, the oriented directions of objects vary considerably across …
Geochat: Grounded large vision-language model for remote sensing
Abstract Recent advancements in Large Vision-Language Models (VLMs) have shown great
promise in natural image domains allowing users to hold a dialogue about given visual …
promise in natural image domains allowing users to hold a dialogue about given visual …
Samrs: Scaling-up remote sensing segmentation dataset with segment anything model
The success of the Segment Anything Model (SAM) demonstrates the significance of data-
centric machine learning. However, due to the difficulties and high costs associated with …
centric machine learning. However, due to the difficulties and high costs associated with …
Skysense: A multi-modal remote sensing foundation model towards universal interpretation for earth observation imagery
Abstract Prior studies on Remote Sensing Foundation Model (RSFM) reveal immense
potential towards a generic model for Earth Observation. Nevertheless these works primarily …
potential towards a generic model for Earth Observation. Nevertheless these works primarily …
Consistency-and dependence-guided knowledge distillation for object detection in remote sensing images
As one of the challenging tasks in the remote sensing (RS), object detection has been
successfully applied in many fields. Convolution neural network (CNN) has recently …
successfully applied in many fields. Convolution neural network (CNN) has recently …