2 citations · 3 across the 8 of their papers we have counts for
7 papers · 1 filter
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…
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…