Reyhan Kevser Keser

Reyhan Kevser Keser
Istanbul Technical University · Department of Informatics

PhD Student

About

7
Publications
1,699
Reads
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8
Citations

Publications

Publications (7)
Preprint
Full-text available
Despite recent advances, object detection in aerial images is still a challenging task. Specific problems in aerial images makes the detection problem harder, such as small objects, densely packed objects, objects in different sizes and with different orientations. To address small object detection problem, we propose a two-stage object detection f...
Article
Full-text available
Despite recent advances, object detection in aerial images is still a challenging task. Specific problems in aerial images makes the detection problem harder, such as small objects, densely packed objects, objects in different sizes and with different orientations. To address small object detection problem, we propose a two-stage object detection f...
Preprint
Full-text available
We propose a novel knowledge distillation methodology for compressing deep neural networks. One of the most efficient methods for knowledge distillation is hint distillation, where the student model is injected with information (hints) from several different layers of the teacher model. Although the selection of hint points can drastically alter th...
Preprint
Full-text available
Graph autoencoders are very efficient at embedding graph-based complex data sets. However, most of the autoencoders have shallow depths and their efficiency tends to decrease with the increase of layer depth. In this paper, we study the effect of adding residual connections to shallow and deep graph variational and vanilla autoencoders. We show tha...
Conference Paper
Full-text available
Graphs are usually represented by high dimensional data. Hence, graph embedding is an essential task, which aims to represent a graph in a lower dimension while protecting the original graph’s properties. In this paper, we propose a novel graph embedding method called Residual Variational Graph Autoencoder (RVGAE), which boosts variational graph au...
Conference Paper
Object recognition can be performed with high accuracy thanks to the robust feature descriptors defining the significant areas in images. However, these features suffer from high dimensional structure, in other words "curse of dimensionality" for further processes. Autoencoders (AE) are proposed in this study to solve the dimensionality reduction p...
Article
Full-text available
Vehicle logo recognition has become an important part of object recognition in recent years because of its usage in surveillance applications. In order to achieve a higher recognition rates, several methods are proposed, such as Scale Invariant Feature Transform (SIFT), convolutional neural networks, bag-of-words and their variations. A fast logo r...

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Projects

Projects (2)
Project
Activity recognition, object detection, classification etc. in partly decompressed H.265 bitstream. Features such as tree structure, motion information, prediction units etc. are fed into novel deep learning models instead of using raw pixel data to perform desired task on videos. This approach yields extremely efficient video understanding methods.
Archived project
Dimensionality Reduction