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Orhun Buğra Baran

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
2022

Closed-form Sample Probing for Learning Generative Models in Zero-shot Learning

ICLR 2022poster

Generative model based approaches have led to significant advances in zero-shot learning (ZSL) over the past few years. These approaches typically aim to learn a conditional generator that synthesizes training samples of classes conditioned on class definitions. The final zero-shot learning model is…