Synthetic aperture radar (SAR) image change detection is a critical task...
The prominent progress in generative models has significantly improved t...
Hyperspectral unmixing is a critical yet challenging task in hyperspectr...
Traditionally, numerical models have been deployed in oceanography studi...
Removing the noise and improving the visual quality of hyperspectral ima...
Considering the ill-posed nature, contrastive regularization has been
de...
The joint hyperspectral image (HSI) and LiDAR data classification aims t...
Forecasts by the European Centre for Medium-Range Weather Forecasts (ECM...
Photometric stereo recovers the surface normals of an object from multip...
Monocular depth estimation is an important task that can be applied to m...
Heterogeneous graph convolutional networks have gained great popularity ...
Recently, change detection methods for synthetic aperture radar (SAR) im...
Despite the demonstrated editing capacity in the latent space of a pretr...
Trajectory-User Linking (TUL), which links trajectories to users who gen...
Transferable adversarial attack has drawn increasing attention due to th...
One fundamental problem in temporal graph analysis is to count the
occur...
Benefited from the rapid and sustainable development of synthetic apertu...
Synthetic aperture radar (SAR) image change detection is a vital yet
cha...
In recent years, hyperspectral image (HSI) classification based on gener...
Traditional synthetic aperture radar image change detection methods base...
Sea subsurface temperature, an essential component of aquatic wildlife,
...
Although Convolution Neural Networks (CNNs) has made substantial progres...
Ocean fronts can cause the accumulation of nutrients and affect the
prop...
The goal of photometric stereo is to measure the precise surface normal ...
Textures contain a wealth of image information and are widely used in va...
Recently, distillation approaches are suggested to extract general knowl...
Clustering is one of the fundamental tasks in computer vision and patter...
Network-structured data becomes ubiquitous in daily life and is growing ...
Interactive single-image segmentation is ubiquitous in the scientific an...
Change detection from synthetic aperture radar (SAR) imagery is a critic...
Knowledge distillation has been widely used to produce portable and effi...
An efficient linear self-attention fusion model is proposed in this pape...
Convolutional neural networks (CNN) have made great progress for synthet...
Existing remote sensing change detection methods are heavily affected by...
Knowledge distillation is a popular paradigm for learning portable neura...
Underwater object detection technique is of great significance for vario...
In recent years, single modality based gait recognition has been extensi...
It is a challenging task to identify a person based on her/his gait patt...
In recent years, deep learning based object detection methods have achie...
We present to recover the complete 3D facial geometry from a single dept...
We propose to learn a cascade of globally-optimized modular boosted fern...
Underwater image enhancement, as a pre-processing step to improve the
ac...
Underwater image enhancement is such an important vision task due to its...
Generative adversarial networks (GANs) are widely used in image generati...
In recent years, deep learning based methods have achieved promising
per...
Current semantic segmentation models only exploit first-order statistics...
Low-shot learning indicates the ability to recognize unseen objects base...
Learning to recognize novel visual categories from a few examples is a
c...
High storage and computational costs obstruct deep neural networks to be...
Obtaining dense 3D reconstrution with low computational cost is one of t...