3 citations · 4 across the 3 of their papers we have counts for
6 papers
Reducing Label Noise in Anchor-Free Object Detection
Nermin Samet, Samet Hicsonmez, Emre Akbas
Current anchor-free object detectors label all the features that spatially fall inside a predefined central region of a ground-truth box as positive. This approach causes label noi…
HoughNet: Integrating near and long-range evidence for bottom-up object detection
Nermin Samet, Samet Hicsonmez, Emre Akbas
This paper presents HoughNet, a one-stage, anchor-free, voting-based, bottom-up object detection method. Inspired by the Generalized Hough Transform, HoughNet determines the presen…
GANILLA: Generative Adversarial Networks for Image to Illustration Translation
Samet Hicsonmez, Nermin Samet, Emre Akbas +1
In this paper, we explore illustrations in children's books as a new domain in unpaired image-to-image translation. We show that although the current state-of-the-art image-to-imag…
Generating Positive Bounding Boxes for Balanced Training of Object Detectors
Kemal Oksuz, Baris Can Cam, Emre Akbas +1
Two-stage deep object detectors generate a set of regions-of-interest (RoI) in the first stage, then, in the second stage, identify objects among the proposed RoIs that sufficientl…
Imbalance Problems in Object Detection: A Review
Kemal Oksuz, Baris Can Cam, Sinan Kalkan +1
In this paper, we present a comprehensive review of the imbalance problems in object detection. To analyze the problems in a systematic manner, we introduce a problem-based taxonom…
Self-Supervised Learning of 3D Human Pose using Multi-view Geometry
Muhammed Kocabas, Salih Karagoz, Emre Akbas
Training accurate 3D human pose estimators requires large amount of 3D ground-truth data which is costly to collect. Various weakly or self supervised pose estimation methods have…