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Many-Class Few-Shot Learning on Multi-Granularity Class Hierarchy
We study many-class few-shot (MCFS) problem in both supervised learning and meta-learning settings. Compared to the well-studied many-class many-shot an...
文献获取Few-shot learning based on hierarchical classification via multi-granularity relation networks
Few-shot learning is one of the significant areas of machine learning, which aims to recognize novel visual classes from few labeled examples. Many exis...
Many-Class Few-Shot Learning on Multi-Granularity Class Hierarchy
We study many-class few-shot (MCFS) problem in both supervised learning and meta-learning settings. Compared to the well-studied many-class many-shot an...
Hierarchical few-shot learning based on top-down correction mechanism with stop strategy
Few-shot learning has become an important branch of machine learning, which aims to give correct prediction information to unknown samples. Many few-sho...
Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classification
Background and objective: Employing deep learning models in critical domains such as medical imaging poses challenges associated with the limited availa...
WhisPAr: Transferring pre-trained audio models to fine-grained classification via Prompt and Adapter
However, current pre-trained models still have the problems of coarse knowledge granularity and poor zero/few-shot performance and cannot be well applie...
Multi-granularity awareness via cross fusion for few-shot learning
Many existing few-shot learning methods based on a single-granularity structure have achieved promising results, but they insufficiently exploit the lat...
Anomaly Detection, Prognostics, and Diagnostics: Machine Learning for the Hadron Calorimeter at the CMS Experiment
Machine Learning (ML) tools have gained immense popularity due to the proliferation of sensor data for monitoring, prognostic, and diagnostic applicatio...
ECGWiz: a unified and generalizable AI model for comprehensive ECG diagnosis
BACKGROUND. Deep learning has demonstrated diagnostic performance in electrocardiogram (ECG) analysis comparable to that of clinicians. However, existin...
Bridging Generative and Discriminative Models for Unified Visual Perception with Diffusion Priors
The remarkable prowess of diffusion models in image generation has spurred efforts to extend their application beyond generative tasks. However, a persistent challenge exists in lacking a unified approach to apply diffusion models to ...
