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Found 12 papers, 1 papers with code
FSCIL-SEI: Few-Shot Class-Incremental Learning Approach for Specific Emitter Identification
—Specific emitter identification (SEI) is a non-password authentication method that adds an extra layer of security to wireless devices.
Few-Shot Medical Image Segmentation with Large Kernel Attention
To achieve this, we introduce a plug-and-play attention module that dynamically enhances both query and support features, thereby improving the representativeness of the extracted features.
SuDA: Support-based Domain Adaptation for Sim2Real Motion Capture with Flexible Sensors
Thanks to the high-linearity of flexible sensors, we address this challenge by proposing a novel Sim2Real Mocap solution based on domain adaptation, eliminating the need for labeled data yet achieving comparable accuracy to supervised learning.
Multi-Task Learning for Fatigue Detection and Face Recognition of Drivers via Tree-Style Space-Channel Attention Fusion Network
These systems typically need to handle multiple tasks simultaneously, such as detecting fatigue driving and recognizing the driver's identity.
Support-Query Prototype Fusion Network for Few-shot Medical Image Segmentation
Currently, most studies employ a prototype-based approach, in which prototypical networks are used to construct prototypes from the support set, guiding the processing of the query set to obtain the final results.
Unveiling Nonlinear Dynamics in Catastrophe Bond Pricing: A Machine Learning Perspective
This paper explores the implications of using machine learning models in the pricing of catastrophe (CAT) bonds.
HB-net: Holistic bursting cell cluster integrated network for occluded multi-objects recognition
Although in high-noise settings, standard CNNs exhibit slightly greater robustness when compared to HB-net models, the models that combine the HB framework and EA mechanism achieve a comparable level of accuracy and resilience to ResNet50, despite having only three convolutional layers and approximately $1/30$ of the parameters.
Distance and Hop-wise Structures Encoding Enhanced Graph Attention Networks
Numerous works have proven that existing neighbor-averaging Graph Neural Networks cannot efficiently catch structure features, and many works show that injecting structure, distance, position or spatial features can significantly improve performance of GNNs, however, injecting overall structure and distance into GNNs is an intuitive but remaining untouched idea.
Multi-layered Network Exploration via Random Walks: From Offline Optimization to Online Learning
For the online learning setting, neither the network structure nor the node weights are known initially.
Semantic Borrowing for Generalized Zero-Shot Learning
Generalized zero-shot learning (GZSL) is one of the most realistic but challenging problems due to the partiality of the classifier to supervised classes, especially under the class-inductive instance-inductive (CIII) training setting, where testing data are not available.
Pandemic risk management: resources contingency planning and allocation
Repeated history of pandemics, such as SARS, H1N1, Ebola, Zika, and COVID-19, has shown that pandemic risk is inevitable.
Community Exploration: From Offline Optimization to Online Learning
We introduce the community exploration problem that has many real-world applications such as online advertising.
