no code implementations • CVPR 2023 • 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 detection.
no code implementations • 13 Aug 2021 • Berkan Demirel, Ramazan Gokberk Cinbis
For this problem, we propose a detection-driven approach that consists of a single-stage generalized zero-shot detection model to recognize and localize instances of both seen and unseen classes, and a template-based captioning model that transforms detections into sentences.
no code implementations • 31 Jul 2019 • Berkan Demirel, Ramazan Gokberk Cinbis, Nazli Ikizler-Cinbis
Image caption generation is a long standing and challenging problem at the intersection of computer vision and natural language processing.
no code implementations • 22 May 2019 • Berkan Demirel, Omer Ozdil, Yunus Emre Esin, Safak Ozturk
In this paper, we propose an unified hyperspectral image classification method which takes three-dimensional hyperspectral data cube as an input and produces a classification map.
no code implementations • 16 May 2019 • Berkan Demirel, Ramazan Gokberk Cinbis, Nazli Ikizler-Cinbis
In this work, we propose a zero-shot learning method to effectively model knowledge transfer between classes via jointly learning visually consistent word vectors and label embedding model in an end-to-end manner.
2 code implementations • 16 May 2018 • Berkan Demirel, Ramazan Gokberk Cinbis, Nazli Ikizler-Cinbis
Object detection is considered as one of the most challenging problems in computer vision, since it requires correct prediction of both classes and locations of objects in images.
Ranked #7 on Zero-Shot Object Detection on PASCAL VOC'07
1 code implementation • ICCV 2017 • Berkan Demirel, Ramazan Gokberk Cinbis, Nazli Ikizler-Cinbis
We propose a novel approach for unsupervised zero-shot learning (ZSL) of classes based on their names.