59 citations · 70 across the 4 of their papers we have counts for
9 papers
Towards Zero-shot Sign Language Recognition
Yunus Can Bilge, Ramazan Gokberk Cinbis, Nazli Ikizler-Cinbis
This paper tackles the problem of zero-shot sign language recognition (ZSSLR), where the goal is to leverage models learned over the seen sign classes to recognize the instances of…
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…
Red Carpet to Fight Club: Partially-supervised Domain Transfer for Face Recognition in Violent Videos
Yunus Can Bilge, Mehmet Kerim Yucel, Ramazan Gokberk Cinbis +2
In many real-world problems, there is typically a large discrepancy between the characteristics of data used in training versus deployment. A prime example is the analysis of aggre…
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…
Zero-Shot Sign Language Recognition: Can Textual Data Uncover Sign Languages?
Yunus Can Bilge, Nazli Ikizler-Cinbis, Ramazan Gokberk Cinbis
We introduce the problem of zero-shot sign language recognition (ZSSLR), where the goal is to leverage models learned over the seen sign class examples to recognize the instances o…
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…