no code implementations • 16 Dec 2023 • Ameen Ali, Hakan Cevikalp, Lior Wolf
Here, we propose a different approach that is based on a stratification of the graph nodes.
no code implementations • 22 Dec 2022 • Hakan Cevikalp, Hasan Saribas
This paper introduces a novel classification loss that maximizes the margin in both the Euclidean and angular spaces at the same time.
no code implementations • 24 Feb 2021 • Hakan Cevikalp, Bedirhan Uzun, Okan Köpüklü, Gurkan Ozturk
In this paper, we propose a new deep neural network classifier that simultaneously maximizes the inter-class separation and minimizes the intra-class variation by using the polyhedral conic classification function.
no code implementations • 18 Nov 2020 • Hasan Saribas, Hakan Cevikalp, Okan Köpüklü, Bedirhan Uzun
Although motion provides distinctive and complementary information especially for fast moving objects, most of the recent tracking architectures primarily focus on the objects' appearance information.
1 code implementation • 30 Sep 2020 • Okan Köpüklü, Stefan Hörmann, Fabian Herzog, Hakan Cevikalp, Gerhard Rigoll
Convolutional Neural Networks with 3D kernels (3D-CNNs) currently achieve state-of-the-art results in video recognition tasks due to their supremacy in extracting spatiotemporal features within video frames.
no code implementations • ICCV 2019 • Hakan Cevikalp, Golara Ghorban Dordinejad
Majority of the image set based face recognition methods use a generatively learned model for each person that is learned independently by ignoring the other persons in the gallery set.
no code implementations • CVPR 2017 • Hakan Cevikalp, Bill Triggs
Our experiments show that they significantly outperform both linear SVMs and existing one-class discriminants on a wide range of object detection, open set recognition and conventional closed-set classification tasks.