arXiv AI
Jun 6
ATT-CR: Adaptive Triangular Transformer for Cloud Removal
arXiv:2606. 05999v1 Announce Type: cross Abstract: Cloud removal aims to accurately reconstruct the ground objects obscured by clouds in remote sensing images.
ATT-CR: Adaptive Triangular Transformer for Cloud Removal
Cloud removal aims to accurately reconstruct the ground objects obscured by clouds in remote sensing images. Existing Transformer-based methods utilizing self-attention have shown impressive results by effectively modeling long-range dependencies in cloudy images.
Related stories
Hugging Face Trending Papers
Aug 4
SGFormer: Structure-Guided Transformer for Robust Local Feature Matching
Local feature matching is a fundamental component of photogrammetry, enabling accurate image correspondence critical for tasks such as 3D reconstruction, stereo mapping, and visual localization. While recent detector-free matching methods, like LoFTR, have advanced the field, the global features obtained by leveraging the global-range modeling capacity of the unconstrained attention mechanism compromise the model's attention to the salient structures in certain scenarios.
arXiv Machine Learning
Jun 5
Exact Linear Attention
arXiv:2605. 18848v3 Announce Type: replace Abstract: This paper introduces Exact Linear Attention (ELA), a mechanism that achieves linear computational complexity for Transformer attention by exploiting the exact decomposition property of kernel functions, thereby eliminating approximation error.
Hugging Face Trending Papers
Jul 17
BCG-Former: Toward Pareto-Efficient Hyperspectral Image Classification via Band-Contextual Gating
Hyperspectral image (HSI) classification systems are increasingly deployed on platforms with strict computational budgets, such as UAVs and small spaceborne sensors. In these settings, accuracy alone is not enough; the model must also run within tight latency and memory constraints.
arXiv AI
Jun 2
LALE: Lightweight-Transformer Architecture for Land-Cover Estimation
arXiv:2606. 02092v1 Announce Type: cross Abstract: Semantic segmentation of remote sensing imagery requires models that capture both global context and local detail under tight computational budgets.
arXiv Machine Learning
Jul 21
Distributed solar generation forecasting using attention-based deep neural networks for cloud movement prediction
arXiv:2411. 10921v2 Announce Type: replace Abstract: Accurate forecasts of distributed solar generation are necessary to maintain grid stability amid the increased uptake of distributed solar photovoltaic (PV) systems.
arXiv Machine Learning
Jul 7
SAF3R: Dynamic Sparse Attention for Feed-Forward 3D Reconstruction Transformers
arXiv:2607. 03612v1 Announce Type: cross Abstract: Feed-forward 3D reconstruction (F3R) transformers have recently achieved remarkable success.
Hugging Face Trending Papers
Aug 3
Global-Scale Self-Supervised Spatiotemporal Learning for NDVI Time-Series Reconstruction
Accurate and efficient reconstruction of cloud-contaminated and noise-corrupted NDVI time series remains a challenge in remote sensing. Deep learning provides a promising solution for modeling complex spatiotemporal dependencies; however, its application is often limited by the difficulty of obtaining paired clear-sky and degraded NDVI data for identical spatiotemporal locations.
Hugging Face Trending Papers
Jun 1
LALE: Lightweight-Transformer Architecture for Land-Cover Estimation
Semantic segmentation of remote sensing imagery requires models that capture both global context and local detail under tight computational budgets. Prior work typically optimizes for one of these axes: attention for global context, convolution for local detail, or compactness for efficiency.
Hugging Face Trending Papers
Aug 6
Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement
Restoring high-fidelity remote sensing imagery from extreme low-light degradation is indispensable for reliable Earth observation and downstream machine vision. However, under severe noise and illumination corruption, existing methods suffer from attention drift, erroneously aggregating features across distinct physical boundaries and causing severe structural blurring and color distortion.
Hugging Face Trending Papers
Jun 23
Flood Mapping from RGB imagery using a Vision Foundation Model
Timely, high-resolution maps of flood extent around settlements are essential for emergency response and damage assessment. We consider airborne RGB imagery for flood mapping as it can be collected rapidly at low cost.
arXiv Machine Learning
Jun 16
Spatial Priors via Space Filling Curves for Small and Limited Data Vision Transformers
arXiv:2606. 14757v1 Announce Type: cross Abstract: Though Vision Transformers (ViTs) have become the dominant backbone in many computer vision tasks, due to permutation equivariance, their attention mechanism lacks explicit spatial inductive biases.