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Found 16 papers, 4 papers with code
TimeHF: Billion-Scale Time Series Models Guided by Human Feedback
Time series neural networks perform exceptionally well in real-world applications but encounter challenges such as limited scalability, poor generalization, and suboptimal zero-shot performance.
sTransformer: A Modular Approach for Extracting Inter-Sequential and Temporal Information for Time-Series Forecasting
We compare our model with linear models and existing forecasting models on long-term time-series forecasting, achieving new state-of-the-art results.
Semi-Supervised SAR ATR Framework with Transductive Auxiliary Segmentation
The recognition performance of 94. 18\% can be achieved under 20 training samples in each class with simultaneous accurate segmentation results.
Crucial Feature Capture and Discrimination for Limited Training Data SAR ATR
Based on the initial recognition results, the feature capture module automatically searches and locks the crucial image regions for correct recognition, which we named as the golden key of image.
An Entropy-Awareness Meta-Learning Method for SAR Open-Set ATR
In this letter, we propose an entropy-awareness meta-learning method that improves the exclusiveness of feature distribution of known classes which means our method is effective for not only classifying the seen classes but also encountering the unseen other classes.
SAR Ship Target Recognition via Selective Feature Discrimination and Multifeature Center Classifier
However, the characteristics of SAR ship images, large inner-class variance, and small interclass difference lead to the whole features containing useless partial features and a single feature center for each class in the classifier failing with large inner-class variance.
SAR Ship Target Recognition Via Multi-Scale Feature Attention and Adaptive-Weighed Classifier
Therefore, we proposed a SAR ship recognition method via multi-scale feature attention and adaptive-weighted classifier to enhance features in each scale, and adaptively choose the effective feature scale for accurate recognition.
SAR ATR Method with Limited Training Data via an Embedded Feature Augmenter and Dynamic Hierarchical-Feature Refiner
The designed augmenter increases the amount of information available for supervised training and improves the separability of the extracted features.
Causal SAR ATR with Limited Data via Dual Invariance
As a result, it has a detrimental causal effect damaging the efficacy of feature $X$ extracted from SAR images, leading to weak generalization of SAR ATR with limited data.
Unveiling Causalities in SAR ATR: A Causal Interventional Approach for Limited Data
Then, a feature discrimination approach with hybrid similarity measurement is introduced to measure and mitigate the structural and vector angle impacts of varying imaging conditions on the extracted features from SAR images.
A deep deformable residual learning network for SAR images segmentation
In this paper, we proposed a deep deformable residual learning network for target segmentation that attempts to preserve the precise contour of the target.
When Deep Learning Meets Multi-Task Learning in SAR ATR: Simultaneous Target Recognition and Segmentation
In this paper, we propose a new multi-task learning approach for SAR ATR, which could obtain the accurate category and precise shape of the targets simultaneously.
SAR Target Image Generation Method Using Azimuth-Controllable Generative Adversarial Network
The similarity discriminator can differentiate the generated SAR target images from the real SAR images to ensure the accuracy of the generated, while the azimuth predictor measures the difference of azimuth between the generated and the desired to ensure the azimuth controllability of the generated.
Global in Local: A Convolutional Transformer for SAR ATR FSL
Convolutional neural networks (CNNs) have dominated the synthetic aperture radar (SAR) automatic target recognition (ATR) for years.
SAR ATR under Limited Training Data Via MobileNetV3
In recent years, deep learning has been widely used to solve the bottleneck problem of synthetic aperture radar (SAR) automatic target recognition (ATR).
NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results
This challenge includes three tracks.
