ArticleJournal of imaging2022
Easy-Ensemble Augmented-Shot-Y-Shaped Learning: State-of-the-Art Few-Shot Classification with Simple Components.
Article in Journal of imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Multi-stage fusion of local and global features for few-shot image classification.Scientific reports · 2025Article
- Development and validation of a real-time risk prediction model for acute kidney injury in hospitalized pediatric patients.World journal of pediatrics : WJP · 2025Article
- Reproducing the Few-Shot Learning Capabilities of the Visual Ventral Pathway Using Vision Transformers and Neural Fields.Brain sciences · 2025Article
- A single fast Hebbian-like process enabling one-shot class addition in deep neural networks without backbone modification.Frontiers in neuroscience · 2024Article
- A Foreground Prototype-Based One-Shot Segmentation of Brain Tumors.Diagnostics (Basel, Switzerland) · 2023Article
- Few-Shot Learning Geometric Ensemble for Multi-label Classification of Chest X-Rays.Data augmentation, labelling, and imperfections : second MICCAI workshop, DALI 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings. DALI (Workshop) (2nd : 2022 : Singapore) · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Few-shot classification aims at leveraging knowledge learned in a deep learning model, in order to obtain good classification performance on new problems, where only a few labeled samples per class are available. Recent years have seen a fair number of works in the field, each one introducing their own methodology. A frequent problem, though, is the use of suboptimally trained models as a first building block, leading to doubts about whether proposed approaches bring gains if applied to more sophisticated pretrained models. In this work, we propose a simple way to train such models, with the aim of reaching top performance on multiple standardized benchmarks in the field. This methodology offers a new baseline on which to propose (and fairly compare) new techniques or adapt existing ones.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.