2 citations · 2 across the 5 of their papers we have counts for
5 papers
MatchED: Crisp Edge Detection Using End-to-End, Matching-based Supervision
Bedrettin Cetinkaya, Sinan Kalkan, Emre Akbas
Generating crisp, i.e., one-pixel-wide, edge maps remains one of the fundamental challenges in edge detection, affecting both traditional and learning-based methods. To obtain cris…
RSPose: Ranking Based Losses for Human Pose Estimation
Muhammed Can Keles, Bedrettin Cetinkaya, Sinan Kalkan +1
While heatmap-based human pose estimation methods have shown strong performance, they suffer from three main problems: (P1) "Commonly used Mean Squared Error (MSE)" Loss may not al…
RankED: Addressing Imbalance and Uncertainty in Edge Detection Using Ranking-based Losses
Bedrettin Cetinkaya, Sinan Kalkan, Emre Akbas
Detecting edges in images suffers from the problems of (P1) heavy imbalance between positive and negative classes as well as (P2) label uncertainty owing to disagreement between di…
A New Multi-Level Hazy Image and Video Dataset for Benchmark of Dehazing Methods
Bedrettin Cetinkaya, Yucel Cimtay, Fatma Nazli Gunay +1
The changing level of haze is one of the main factors which affects the success of the proposed dehazing methods. However, there is a lack of controlled multi-level hazy dataset in…
Does depth estimation help object detection?
Bedrettin Cetinkaya, Sinan Kalkan, Emre Akbas
Ground-truth depth, when combined with color data, helps improve object detection accuracy over baseline models that only use color. However, estimated depth does not always yield…