1 code implementation • 7 Oct 2023 • Samet Hicsonmez, Nermin Samet, Fidan Samet, Oguz Bakir, Emre Akbas, Pinar Duygulu
Current state-of-the-art video-to-video translation models rely on having a video sequence or a single style image to stylize an input video.
1 code implementation • ICCV 2023 • Mehmet Kerim Yucel, Ramazan Gokberk Cinbis, Pinar Duygulu
First, inspired by these observations, we propose a simple yet effective data augmentation method HybridAugment that reduces the reliance of CNNs on high-frequency components, and thus improves their robustness while keeping their clean accuracy high.
1 code implementation • 20 Jan 2023 • Samet Hicsonmez, Nermin Samet, Emre Akbas, Pinar Duygulu
We leverage semantic image segmentation from a general-purpose panoptic segmentation network to generate an additional adversarial loss function.
1 code implementation • 26 Jan 2022 • Mehmet Kerim Yucel, Ramazan Gokberk Cinbis, Pinar Duygulu
In this paper, we present novel analyses on the robustness of discriminative ZSL to image corruptions.
1 code implementation • 11 Feb 2021 • Samet Hicsonmez, Nermin Samet, Emre Akbas, Pinar Duygulu
Our loss function can be integrated to any baseline GAN model.
no code implementations • 16 Sep 2020 • Yunus Can Bilge, Mehmet Kerim Yucel, Ramazan Gokberk Cinbis, Nazli Ikizler-Cinbis, Pinar Duygulu
To mimic such scenarios, we formulate a realistic domain-transfer problem, where the goal is to transfer the recognition model trained on clean posed images to the target domain of violent videos, where training videos are available only for a subset of subjects.
2 code implementations • 17 Aug 2020 • Mehmet Kerim Yucel, Ramazan Gokberk Cinbis, Pinar Duygulu
In constrast, Zero-shot Learning (ZSL) and Generalized Zero-shot Learning (GZSL) tasks inherently lack supervision across all classes.
4 code implementations • 13 Feb 2020 • Samet Hicsonmez, Nermin Samet, Emre Akbas, Pinar Duygulu
To address this problem, we propose a new framework for the quantitative evaluation of image-to-illustration models, where both content and style are taken into account using separate classifiers.
no code implementations • 19 May 2018 • Mehmet Kerim Yucel, Yunus Can Bilge, Oguzhan Oguz, Nazli Ikizler-Cinbis, Pinar Duygulu, Ramazan Gokberk Cinbis
With the introduction of large-scale datasets and deep learning models capable of learning complex representations, impressive advances have emerged in face detection and recognition tasks.
no code implementations • 10 Apr 2017 • Samet Hicsonmez, Nermin Samet, Fadime Sener, Pinar Duygulu
The style was noticeable in other characters of the same illustrator in different books as well.
no code implementations • 10 Jul 2014 • Eren Golge, Pinar Duygulu
We attack the problem of learning face models for public faces from weakly-labelled images collected from web through querying a name.
no code implementations • 9 Jul 2014 • Alican Bozkurt, Pinar Duygulu, A. Enis Cetin
Recognizing fonts has become an important task in document analysis, due to the increasing number of available digital documents in different fonts and emphases.
no code implementations • 3 Jan 2014 • Ahmet Iscen, Eren Golge, Ilker Sarac, Pinar Duygulu
We introduce ConceptVision, a method that aims for high accuracy in categorizing large number of scenes, while keeping the model relatively simpler and efficient for scalability.
no code implementations • 3 Jan 2014 • Ahmet Iscen, Anil Armagan, Pinar Duygulu
Unusual events are important as being possible indicators of undesired consequences.
no code implementations • 16 Dec 2013 • Eren Golge, Pinar Duygulu
The proposed method outperforms the state-of-the-art studies on the task of learning low-level concepts, and it is competitive in learning higher level concepts as well.