Search Results for author: Sinem Aslan

Found 11 papers, 2 papers with code

Transductive Visual Verb Sense Disambiguation

1 code implementation20 Dec 2020 Sebastiano Vascon, Sinem Aslan, Gianluca Bigaglia, Lorenzo Giudice, Marcello Pelillo

Verb Sense Disambiguation is a well-known task in NLP, the aim is to find the correct sense of a verb in a sentence.

EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review

no code implementations28 Sep 2020 Sana Yasin, Syed Asad Hussain, Sinem Aslan, Imran Raza, Muhammad Muzammel, Alice Othmani

Mental disorders represent critical public health challenges as they are leading contributors to the global burden of disease and intensely influence social and financial welfare of individuals.

EEG

CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation

1 code implementation17 Jan 2020 A. Emre Kavur, N. Sinem Gezer, Mustafa Barış, Sinem Aslan, Pierre-Henri Conze, Vladimir Groza, Duc Duy Pham, Soumick Chatterjee, Philipp Ernst, Savaş Özkan, Bora Baydar, Dmitry Lachinov, Shuo Han, Josef Pauli, Fabian Isensee, Matthias Perkonigg, Rachana Sathish, Ronnie Rajan, Debdoot Sheet, Gurbandurdy Dovletov, Oliver Speck, Andreas Nürnberger, Klaus H. Maier-Hein, Gözde BOZDAĞI AKAR, Gözde Ünal, Oğuz Dicle, M. Alper Selver

The analysis shows that the performance of DL models for single modality (CT / MR) can show reliable volumetric analysis performance (DICE: 0. 98 $\pm$ 0. 00 / 0. 95 $\pm$ 0. 01) but the best MSSD performance remain limited (21. 89 $\pm$ 13. 94 / 20. 85 $\pm$ 10. 63 mm).

Weakly Supervised Semantic Segmentation Using Constrained Dominant Sets

no code implementations20 Sep 2019 Sinem Aslan, Marcello Pelillo

The availability of large-scale data sets is an essential pre-requisite for deep learning based semantic segmentation schemes.

Weakly-Supervised Semantic Segmentation

Unsupervised Domain Adaptation using Graph Transduction Games

no code implementations6 May 2019 Sebastiano Vascon, Sinem Aslan, Alessandro Torcinovich, Twan van Laarhoven, Elena Marchiori, Marcello Pelillo

Unsupervised domain adaptation (UDA) amounts to assigning class labels to the unlabeled instances of a dataset from a target domain, using labeled instances of a dataset from a related source domain.

Object Recognition Unsupervised Domain Adaptation

Unobtrusive and Multimodal Approach for Behavioral Engagement Detection of Students

no code implementations16 Jan 2019 Nese Alyuz, Eda Okur, Utku Genc, Sinem Aslan, Cagri Tanriover, Asli Arslan Esme

We propose a multimodal approach for detection of students' behavioral engagement states (i. e., On-Task vs. Off-Task), based on three unobtrusive modalities: Appearance, Context-Performance, and Mouse.

Detecting Behavioral Engagement of Students in the Wild Based on Contextual and Visual Data

no code implementations15 Jan 2019 Eda Okur, Nese Alyuz, Sinem Aslan, Utku Genc, Cagri Tanriover, Asli Arslan Esme

To investigate the detection of students' behavioral engagement (On-Task vs. Off-Task), we propose a two-phase approach in this study.

The Importance of Socio-Cultural Differences for Annotating and Detecting the Affective States of Students

no code implementations12 Jan 2019 Eda Okur, Sinem Aslan, Nese Alyuz, Asli Arslan Esme, Ryan S. Baker

One open question in annotating affective data for affect detection is whether the labelers (i. e., human experts) need to be socio-culturally similar to the students being labeled, as this impacts the cost feasibility of obtaining the labels.

Compressively Sensed Image Recognition

no code implementations15 Oct 2018 Aysen Degerli, Sinem Aslan, Mehmet Yamac, Bulent Sankur, Moncef Gabbouj

Recent literature works show that compressive image classification is possible in CS domain without reconstruction of the signal.

Compressive Sensing General Classification +1

Ancient Coin Classification Using Graph Transduction Games

no code implementations2 Oct 2018 Sinem Aslan, Sebastiano Vascon, Marcello Pelillo

Experiments are conducted on the only publicly available dataset which is composed of 180 images of 60 types of Roman coins.

Classification General Classification +1

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