5 papers · 1 filter
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…
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.…