4 citations · 5 across the 3 of their papers we have counts for
4 papers
Policy-based Tuning of Autoregressive Image Models with Instance- and Distribution-Level Rewards
Orhun Bugra Baran, Melih Kandemir, Ramazan Gokberk Cinbis
Autoregressive (AR) models are highly effective for image generation, yet their standard maximum-likelihood estimation training lacks direct optimization for sample quality and div…
Meta-tuning Loss Functions and Data Augmentation for Few-shot Object Detection
Berkan Demirel, Orhun Buğra Baran, Ramazan Gokberk Cinbis
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…
Semantics-driven Attentive Few-shot Learning over Clean and Noisy Samples
Orhun Buğra Baran, Ramazan Gökberk Cinbiş
Over the last couple of years few-shot learning (FSL) has attracted great attention towards minimizing the dependency on labeled training examples. An inherent difficulty in FSL is…
MaskSplit: Self-supervised Meta-learning for Few-shot Semantic Segmentation
Mustafa Sercan Amac, Ahmet Sencan, Orhun Bugra Baran +2
Just like other few-shot learning problems, few-shot segmentation aims to minimize the need for manual annotation, which is particularly costly in segmentation tasks. Even though t…