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Berkan Demirel

2 accepted papers

2023

Meta-Tuning Loss Functions and Data Augmentation for Few-Shot Object Detection

CVPR 2023poster

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. Contemporary techniques can be divided into two groups: fine-tuning based and meta-learning based approaches.…

Cited by 26SourcePDFScholar
2017

Attributes2Classname: A Discriminative Model for Attribute-Based Unsupervised Zero-Shot Learning

ICCV 2017poster

We propose a novel approach for unsupervised zero-shot learning (ZSL) of classes based on their names. Most existing unsupervised ZSL methods aim to learn a model for directly comparing image features and class names. However, this proves to be a difficult task due to dominance of non-visual semanti…

Cited by 84PDFcodeScholar