2 citations · 3 across the 9 of their papers we have counts for
9 papers
Gaze4HRI: Zero-shot Benchmarking Gaze Estimation Neural-Networks for Human-Robot Interaction
Berk Sezer, Ali Görkem Küçük, Erol Şahin +1
While zero-shot appearance-based 3D gaze estimation offers significant cost-efficiency by directly mapping RGB images to gaze vectors, its reliability in Human-Robot Interaction (H…
Investigating Bias and Fairness in Appearance-based Gaze Estimation
Burak Akgül, Erol Şahin, Sinan Kalkan
While appearance-based gaze estimation has achieved significant improvements in accuracy and domain adaptation, the fairness of these systems across different demographic groups re…
LuMon: A Comprehensive Benchmark and Development Suite with Novel Datasets for Lunar Monocular Depth Estimation
Aytaç Sekmen, Fatih Emre Gunes, Furkan Horoz +9
Monocular Depth Estimation (MDE) is crucial for autonomous lunar rover navigation using electro-optical cameras. However, deploying terrestrial MDE networks to the Moon brings a se…
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…
Intrinsic Dimensionality as a Model-Free Measure of Class Imbalance
Çağrı Eser, Zeynep Sonat Baltacı, Emre Akbaş +1
Imbalance in classification tasks is commonly quantified by the cardinalities of examples across classes. This, however, disregards the presence of redundant examples and inherent…
Bucketed Ranking-based Losses for Efficient Training of Object Detectors
Feyza Yavuz, Baris Can Cam, Adnan Harun Dogan +3
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 wi…
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…
Class Uncertainty: A Measure to Mitigate Class Imbalance
Z. S. Baltaci, K. Oksuz, S. Kuzucu +5
Class-wise characteristics of training examples affect the performance of deep classifiers. A well-studied example is when the number of training examples of classes follows a long…