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Found 3 papers, 2 papers with code
Meta-tuning Loss Functions and Data Augmentation for Few-shot Object Detection
Few-shot object detection, the problem of modelling novel object detection categories with few training instances, is an emerging topic in the area of few-shot learning and object detection.
Semantics-driven Attentive Few-shot Learning over Clean and Noisy Samples
Over the last couple of years few-shot learning (FSL) has attracted great attention towards minimizing the dependency on labeled training examples.
Closed-form Sample Probing for Learning Generative Models in Zero-shot Learning
In our approach, at each generative model update step, we fit a task-specific closed-form ZSL model from generated samples, and measure its loss on novel samples all within the compute graph, a procedure that we refer to as sample probing.
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