Mm-llms: Recent advances in multimodal large language models
In the past year, MultiModal Large Language Models (MM-LLMs) have undergone
substantial advancements, augmenting off-the-shelf LLMs to support MM inputs or outputs …
substantial advancements, augmenting off-the-shelf LLMs to support MM inputs or outputs …
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 …
Heterogeneous forgetting compensation for class-incremental learning
Class-incremental learning (CIL) has achieved remarkable successes in learning new
classes consecutively while overcoming catastrophic forgetting on old categories. However …
classes consecutively while overcoming catastrophic forgetting on old categories. However …
Sam-assisted remote sensing imagery semantic segmentation with object and boundary constraints
Semantic segmentation of remote sensing imagery plays a pivotal role in extracting precise
information for diverse downstream applications. Recent development of the segment …
information for diverse downstream applications. Recent development of the segment …
Self correspondence distillation for end-to-end weakly-supervised semantic segmentation
Efficiently training accurate deep models for weakly supervised semantic segmentation
(WSSS) with image-level labels is challenging and important. Recently, end-to-end WSSS …
(WSSS) with image-level labels is challenging and important. Recently, end-to-end WSSS …
[HTML][HTML] Aerialformer: Multi-resolution transformer for aerial image segmentation
When performing remote sensing image segmentation, practitioners often encounter various
challenges, such as a strong imbalance in the foreground–background, the presence of tiny …
challenges, such as a strong imbalance in the foreground–background, the presence of tiny …
Deep Learning-Based Semantic Segmentation of Remote Sensing Images: A Survey
L Huang, B Jiang, S Lv, Y Liu… - IEEE Journal of Selected …, 2023 - ieeexplore.ieee.org
Semantic segmentation of remote sensing images (SSRSIs), which aims to assign a
category to each pixel in remote sensing images, plays a vital role in a broad range of …
category to each pixel in remote sensing images, plays a vital role in a broad range of …
Remote sensing semantic segmentation via boundary supervision-aided multiscale channelwise cross attention network
High spatial resolution (HSR) remote sensing (RS) images inevitably pose the challenge of
multiscale transformation, as small objects, such as cars and helicopters (HCs), may occupy …
multiscale transformation, as small objects, such as cars and helicopters (HCs), may occupy …
Transcending pixels: boosting saliency detection via scene understanding from aerial imagery
Existing remote sensing image salient object detection (RSI-SOD) methods widely perform
object-level semantic understanding with pixel-level supervision, but ignore the image-level …
object-level semantic understanding with pixel-level supervision, but ignore the image-level …
[HTML][HTML] DDPM-SegFormer: Highly refined feature land use and land cover segmentation with a fused denoising diffusion probabilistic model and transformer
The semantic segmentation of land use and land cover (LULC) is a crucial and widely
employed remote sensing task. Conventional convolutional neural networks and vision …
employed remote sensing task. Conventional convolutional neural networks and vision …