Emre Akbaş

E-mail

eakbas@metu.edu.tr

Department

Department of Computer Engineering

ORCID

0000-0002-3760-6722

Scopus Author ID

15123935000

Web of Science Researcher ID

R-4173-2017

Representation recycling for streaming video analysis Ertenli, Can Ufuk; Cinbiş, Ramazan Gökberk; Akbaş, Emre (2026-11-01) We present StreamDEQ, a method that aims to infer frame-wise representations on videos with minimal per-frame computation. Conventional deep networks perform feature extraction from scratch at each frame in the absence of ...
Intrinsic dimensionality as a model-free measure of class imbalance Eser, Çağrı; Baltaci, Zeynep Sonat; Akbaş, Emre; KALKAN, SİNAN (2026-04-14) Imbalance in classification tasks is commonly quantified by the cardinalities of examples across classes. This, however, disregards the presence of redundant examples and inherent differences in the learning difficulties o...
Q-Former Autoencoder: A Modern Framework for Medical Anomaly Detection Dalmonte, Francesco; Bayar, Emirhan; Akbaş, Emre; Georgescu, Mariana-Iuliana (2026-01-01) Anomaly detection in medical images is an important yet challenging task due to the diversity of possible anomalies and the practical impossibility of collecting comprehensively annotated data sets. In this work, we tackle...
Transformers in Small Object Detection: A Benchmark and Survey of State-of-the-Art Miri Rekavandi, Aref; Rashidi, Shima; Boussaid, Farid; Hoefs, Stephen; Akbaş, Emre; Bennamoun, Mohammed (2025-09-10) Transformers have rapidly gained popularity in computer vision, especially in the field of object detection. Upon examining the outcomes of state-of-the-art object detection methods, we noticed that transformers consistent...
WAIT: Feature warping for animation to illustration video translation using GANs Hicsonmez, Samet; Samet, Nermin; Samet, Fidan; Bakir, Oguz; Akbaş, Emre; DUYGULU ŞAHİN, PINAR (2025-07-07) In this paper, we explore a new domain for video-to-video translation. Motivated by the availability of animation movies that are adopted from illustrated books for children, we aim to stylize these videos with the style o...
Colorectal cancer tumor grade segmentation: A new dataset and baseline results Arslan, Duygu; Sehlaver, Sina; Guder, Erce; Temena, Mehmet Arda; Bahcekapili, Alper; Ozdemir, Umut; Turkay, Duriye Ozer; Guner, Gunes; Guresci, Servet; SÖKMENSÜER, CENK; Akbaş, Emre; Acar, Ahmet (2025-02-28) Routine pathology assessment for the tumor grading is currently performed under the microscope by experienced pathologists which might be prone to interpersonal variability and requiring years of experience. Over the past ...
DeepKin: Predicting Relatedness From Low-Coverage Genomes and Palaeogenomes With Convolutional Neural Networks Güler, Murat; Yılmaz, Ardan; Katırcıoğlu, Büşra; Kantar, Sarp; Ünver, Tara Ekin; Vural, Kıvılcım Başak; ALTINIŞIK, NEFİZE EZGİ; Akbaş, Emre; Somel, Mehmet (2025-01-01) DeepKin is a novel tool designed to predict relatedness from genomic data using convolutional neural networks (CNNs). Traditional methods for estimating relatedness often struggle when genomic data is limited, as with pala...
Bucketed Ranking-Based Losses for Efficient Training of Object Detectors Yavuz, Feyza; Cam, Baris Can; Doğan, Adnan Harun; Oksuz, Kemal; Akbaş, Emre; KALKAN, SİNAN (2025-01-01) Ranking-based loss functions, such as Average Precision Loss and Rank&Sort Loss, outperform widely used score-based losses in object detection. These loss functions better align with the evaluation criteria, have fewer hyp...
COLORECTAL CANCER TUMOR GRADE SEGMENTATION IN DIGITAL HISTOPATHOLOGY IMAGES: FROM GIGA TO MINI CHALLENGE Bahcekapili, Alper; Arslan, Duygu; Ozdemir, Umut; Ozkirli, Berkay; Akbaş, Emre; Acar, Ahmet; Akar, Gözde; He, Bingdou; Xu, Shuoyu; Çağlar, Ümit Mert; Temizel, Alptekin; Picaud, Guillaume; Chaumont, Marc; Subsol, Gerard; Teot, Luc; Alsharekh, Fahad; Alghannam, Shahad; Mao, Hexiang; Zhang, Wenhua (2025-01-01) Colorectal cancer (CRC) is the third most diagnosed cancer and the second leading cause of cancer-related death worldwide. Accurate histopathological grading of CRC is essential for prognosis and treatment planning but rem...
AIDCON: An Aerial Image Dataset and Benchmark for Construction Machinery Ersöz, Ahmet Bahaddin; Pekcan, Onur; Akbaş, Emre (2024-09-01) Applying deep learning algorithms in the construction industry holds tremendous potential for enhancing site management, safety, and efficiency. The development of such algorithms necessitates a comprehensive and diverse i...

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