Ramazan Gökberk Cinbiş

E-mail

gcinbis@metu.edu.tr

Department

Department of Computer Engineering

ORCID

0000-0003-0962-7101

Scopus Author ID

19337067900

Web of Science Researcher ID

AAQ-6929-2020

Shadow-aware terrain classification: advancing hyperspectral image sensing through generative adversarial networks and correlated sample synthesis Peker, Ali Gokalp; Yuksel, Seniha Esen; Cinbiş, Ramazan Gökberk; Cetin, Yasemin Yardimci (2024-07-01) In recent years, the utilization of hyperspectral sensors for remote sensing has marked a profound advancement due to the success of machine learning techniques. Nevertheless, difficulties still exist, especially in locati...
SAR2ET: End-to-end SAR-driven Multisource ET Imagery Estimation Over Croplands Cetin, Samet; Ulker, Berk; Erten, Esra; Cinbiş, Ramazan Gökberk (2024-01-01) Evapotranspiration (ET) is a crucial parameter in agriculture as it plays a vital role in managing water resources, monitoring droughts, and optimizing crop yields across different ecosystems. Given its significance in cro...
HybridAugment++: Unified Frequency Spectra Perturbations for Model Robustness Yücel, Mehmet Kerim; Cinbiş, Ramazan Gökberk; DUYGULU ŞAHİN, PINAR (2023-10-06) Convolutional Neural Networks (CNN) are known to exhibit poor generalization performance under distribution shifts. Their generalization have been studied extensively, and one line of work approaches the problem from a fre...
Meta-tuning Loss Functions and Data Augmentation for Few-shot Object Detection Demirel, Berkan; Baran, Orhun Buğra; Cinbiş, Ramazan Gökberk (2023-06-21) 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 ob- ject detection. Contemporary techniques can b...
Attentive Sequential Auto-Encoding Towards Unsupervised Object-centric Scene Modeling ÇETİN, YARKIN DENİZ; Cinbiş, Ramazan Gökberk (2022-12-01)
Semantics-driven attentive few-shot learning over clean and noisy samples Baran, Orhun Buğra; Cinbiş, Ramazan Gökberk (2022-11-01) Over the last couple of years, few-shot learning (FSL) has attracted significant attention towards minimiz-ing the dependency on labeled training examples. An inherent difficulty in FSL is handling ambiguities resulting fr...
Caption generation on scenes with seen and unseen object categories Demirel, Berkan; Cinbiş, Ramazan Gökberk (2022-08-01) Image caption generation is one of the most challenging problems at the intersection of vision and language domains. In this work, we propose a realistic captioning task where the input scenes may incorporate visual object...
Caption Generation on Scenes with Seen and Unseen Object Categories Demirel, Berkan; Cinbiş, Ramazan Gökberk (2022-06-01)
Closed-form sample probing for learning generative models in Zero-shot Learning Çetin, Samet; Baran, Orhun Buğra; Cinbiş, Ramazan Gökberk (2022-04-25) 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 c...
How robust are discriminatively trained zero-shot learning models? Yucel, Mehmet Kerim; Cinbiş, Ramazan Gökberk; DUYGULU ŞAHİN, PINAR (2022-3-01) Data shift robustness has been primarily investigated from a fully supervised perspective, and robustness of zero shot learning (ZSL) models have been largely neglected. In this paper, we present novel analyses on the robu...

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