308 citations · 856 across the 20 of their papers we have counts for
21 papers
ODE-Based Transformer Decoders for Iterative Sign Language Translation
Tuğçe Kızıltepe, Hacer Yalim Keles
Sign language translation has achieved strong results with Transformer architectures, yet recent improvements largely rely on scaling model capacity at the cost of increased comput…
Gloss-Free Representation Learning for Cross-Dataset Sign Spotting
Oğuz Akif Tüfekcioğlu, Ezgi Ekin, Mustafa Kaan Çevik +1
Sign-language research for resource-constrained languages is often limited by the cost of dense linguistic labels such as glosses, temporal boundaries, and sign order. Broadcast ne…
AV-SLP: Alignment-Aware Variational Modeling for Disentangled Sign Language Production
Sümeyye Meryem Taşyürek, Enis Mücahid İskender, Hacer Yalim Keles
Building upon recent structural disentanglement frameworks for sign language production, we propose AV-SLP, an alignment-aware variational framework that learns articulator-w…
CLoE: Curriculum Learning on Endoscopic Images for Robust MES Classification
Zeynep Ozdemir, Hacer Yalim Keles, Omer Ozgur Tanriover
Estimating disease severity from endoscopic images is essential in assessing ulcerative colitis, where the Mayo Endoscopic Subscore (MES) is widely used to grade inflammation. Howe…
SLRTP2025 Sign Language Production Challenge: Methodology, Results, and Future Work
Harry Walsh, Ed Fish, Ozge Mercanoglu Sincan +19
Sign Language Production (SLP) is the task of generating sign language video from spoken language inputs. The field has seen a range of innovations over the last few years, with th…
Disentangle and Regularize: Sign Language Production with Articulator-Based Disentanglement and Channel-Aware Regularization
Sumeyye Meryem Tasyurek, Tugce Kiziltepe, Hacer Yalim Keles
In this work, we propose DARSLP, a simple gloss-free, transformer-based sign language production (SLP) framework that directly maps spoken-language text to sign pose sequences. We…
SkelCap: Automated Generation of Descriptive Text from Skeleton Keypoint Sequences
Ali Emre Keskin, Hacer Yalim Keles
Numerous sign language datasets exist, yet they typically cover only a limited selection of the thousands of signs used globally. Moreover, creating diverse sign language datasets…
E-TSL: A Continuous Educational Turkish Sign Language Dataset with Baseline Methods
Şükrü Öztürk, Hacer Yalim Keles
This study introduces the continuous Educational Turkish Sign Language (E-TSL) dataset, collected from online Turkish language lessons for 5th, 6th, and 8th grades. The dataset com…
Meta-Transfer Derm-Diagnosis: Exploring Few-Shot Learning and Transfer Learning for Skin Disease Classification in Long-Tail Distribution
Zeynep Özdemir, Hacer Yalim Keles, Ömer Özgür Tanrıöver
Building accurate models for rare skin diseases remains challenging due to the lack of sufficient labeled data and the inherently long-tailed distribution of available samples. The…
Adversarial Sparse Teacher: Defense Against Distillation-Based Model Stealing Attacks Using Adversarial Examples
Eda Yilmaz, Hacer Yalim Keles
We introduce Adversarial Sparse Teacher (AST), a robust defense method against distillation-based model stealing attacks. Our approach trains a teacher model using adversarial exam…
SVInvNet: A Densely Connected Encoder-Decoder Architecture for Seismic Velocity Inversion
Mojtaba Najafi Khatounabad, Hacer Yalim Keles, Selma Kadioglu
This study presents a deep learning-based approach to seismic velocity inversion problem, focusing on both noisy and noiseless training datasets of varying sizes. Our Seismic Veloc…
Using Motion History Images with 3D Convolutional Networks in Isolated Sign Language Recognition
Ozge Mercanoglu Sincan, Hacer Yalim Keles
Sign language recognition using computational models is a challenging problem that requires simultaneous spatio-temporal modeling of the multiple sources, i.e. faces, hands, body,…
Towards disease-aware image editing of chest X-rays
Aakash Saboo, Sai Niranjan Ramachandran, Kai Dierkes +1
Disease-aware image editing by means of generative adversarial networks (GANs) constitutes a promising avenue for advancing the use of AI in the healthcare sector. Here, we present…
ChaLearn LAP Large Scale Signer Independent Isolated Sign Language Recognition Challenge: Design, Results and Future Research
Ozge Mercanoglu Sincan, Julio C. S. Jacques Junior, Sergio Escalera +1
The performances of Sign Language Recognition (SLR) systems have improved considerably in recent years. However, several open challenges still need to be solved to allow SLR to be…
A Hierarchical Approach to Remote Sensing Scene Classification
Ozlem Sen, Hacer Yalim Keles
Remote sensing scene classification deals with the problem of classifying land use/cover of a region from images. To predict the development and socioeconomic structures of cities,…
Iterative Facial Image Inpainting Based on an Encoder-Generator Architecture
Yahya Dogan, Hacer Yalim Keles
Facial image inpainting is a challenging problem as it requires generating new pixels that include semantic information for masked key components in a face, e.g., eyes and nose. Re…
AUTSL: A Large Scale Multi-modal Turkish Sign Language Dataset and Baseline Methods
Ozge Mercanoglu Sincan, Hacer Yalim Keles
Sign language recognition is a challenging problem where signs are identified by simultaneous local and global articulations of multiple sources, i.e. hand shape and orientation, h…
Evaluation Of Hidden Markov Models Using Deep CNN Features In Isolated Sign Recognition
Anil Osman Tur, Hacer Yalim Keles
Isolated sign recognition from video streams is a challenging problem due to the multi-modal nature of the signs, where both local and global hand features and face gestures needs…
Semi-supervised Image Attribute Editing using Generative Adversarial Networks
Yahya Dogan, Hacer Yalim Keles
Image attribute editing is a challenging problem that has been recently studied by many researchers using generative networks. The challenge is in the manipulation of selected attr…
Learning Multi-scale Features for Foreground Segmentation
Long Ang Lim, Hacer Yalim Keles
Foreground segmentation algorithms aim segmenting moving objects from the background in a robust way under various challenging scenarios. Encoder-decoder type deep neural networks…
Foreground Segmentation Using a Triplet Convolutional Neural Network for Multiscale Feature Encoding
Long Ang Lim, Hacer Yalim Keles
A common approach for moving objects segmentation in a scene is to perform a background subtraction. Several methods have been proposed in this domain. However, they lack the abili…