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Found 19 papers, 9 papers with code
Prototype-oriented Unsupervised Change Detection for Disaster Management
Climate change has led to an increased frequency of natural disasters such as floods and cyclones.
Hausdorff Distance Matching with Adaptive Query Denoising for Rotated Detection Transformer
The Detection Transformer (DETR) has emerged as a pivotal role in object detection tasks, setting new performance benchmarks due to its end-to-end design and scalability.
On Pitfalls of $\textit{RemOve-And-Retrain}$: Data Processing Inequality Perspective
Approaches for appraising feature importance approximations, alternatively referred to as attribution methods, have been established across an extensive array of contexts.
A Billion-scale Foundation Model for Remote Sensing Images
Recently, research in the remote sensing field has focused primarily on the pretraining method and the size of the dataset, with limited emphasis on the number of model parameters.
Ranked #1 on
Object Detection In Aerial Images
on DIOR-R
Implicit Stacked Autoregressive Model for Video Prediction
Future frame prediction has been approached through two primary methods: autoregressive and non-autoregressive.
Ranked #1 on
Weather Forecasting
on SEVIR
Self-Pair: Synthesizing Changes from Single Source for Object Change Detection in Remote Sensing Imagery
For change detection in remote sensing, constructing a training dataset for deep learning models is difficult due to the requirements of bi-temporal supervision.
Semi-Implicit Hybrid Gradient Methods with Application to Adversarial Robustness
Adversarial examples, crafted by adding imperceptible perturbations to natural inputs, can easily fool deep neural networks (DNNs).
Contrastive Multiview Coding with Electro-optics for SAR Semantic Segmentation
In the training of deep learning models, how the model parameters are initialized greatly affects the model performance, sample efficiency, and convergence speed.
Training Domain-invariant Object Detector Faster with Feature Replay and Slow Learner
In deep learning-based object detection on remote sensing domain, nuisance factors, which affect observed variables while not affecting predictor variables, often matters because they cause domain changes.
On the Power of Deep but Naive Partial Label Learning
We also address the question of how and why such a naive model works well with deep neural networks.
Revisiting Classical Bagging with Modern Transfer Learning for On-the-fly Disaster Damage Detector
In this paper, we revisit the classical bootstrap aggregating approach in the context of modern transfer learning for data-efficient disaster damage detection.
Deep Closed-Form Subspace Clustering
We propose Deep Closed-Form Subspace Clustering (DCFSC), a new embarrassingly simple model for subspace clustering with learning non-linear mapping.
NL-LinkNet: Toward Lighter but More Accurate Road Extraction with Non-Local Operations
Road extraction from very high resolution satellite (VHR) images is one of the most important topics in the field of remote sensing.
Bridging Adversarial Robustness and Gradient Interpretability
Adversarial training is a training scheme designed to counter adversarial attacks by augmenting the training dataset with adversarial examples.
Why are Saliency Maps Noisy? Cause of and Solution to Noisy Saliency Maps
Saliency Map, the gradient of the score function with respect to the input, is the most basic technique for interpreting deep neural network decisions.
Rectified Gradient: Layer-wise Thresholding for Sharp and Coherent Attribution Maps
In this paper, we identify the cause of noisy saliency maps.
Domain Adaptive Generation of Aircraft on Satellite Imagery via Simulated and Unsupervised Learning
Object detection and classification for aircraft are the most important tasks in the satellite image analysis.
Noise-adding Methods of Saliency Map as Series of Higher Order Partial Derivative
SmoothGrad and VarGrad are techniques that enhance the empirical quality of standard saliency maps by adding noise to input.
