18 citations · 23 across the 5 of their papers we have counts for
7 papers
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…
Caption Generation on Scenes with Seen and Unseen Object Categories
Berkan Demirel, Ramazan Gokberk Cinbis
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…
Image Captioning with Unseen Objects
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. A number of recently proposed approaches…
Segmentation-Aware Hyperspectral Image Classification
Berkan Demirel, Omer Ozdil, Yunus Emre Esin +1
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.…
Learning Visually Consistent Label Embeddings for Zero-Shot Learning
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 embeddi…
Zero-Shot Object Detection by Hybrid Region Embedding
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.…
Attributes2Classname: A discriminative model for attribute-based unsupervised zero-shot learning
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. Most existing unsupervised ZSL methods aim to learn a model for directly comp…