27 citations · 41 across the 9 of their papers we have counts for
10 papers
TaCarla: A comprehensive benchmarking dataset for end-to-end autonomous driving
Tugrul Gorgulu, Atakan Dag, M. Esat Kalfaoglu +4
Collecting a high-quality dataset is a critical task that demands meticulous attention to detail, as overlooking certain aspects can render the entire dataset unusable. Autonomous…
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…
Generalized Mask-aware IoU for Anchor Assignment for Real-time Instance Segmentation
Barış Can Çam, Kemal Öksüz, Fehmi Kahraman +3
This paper introduces Generalized Mask-aware Intersection-over-Union (GmaIoU) as a new measure for positive-negative assignment of anchor boxes during training of instance segmenta…
Mask-aware IoU for Anchor Assignment in Real-time Instance Segmentation
Kemal Oksuz, Baris Can Cam, Fehmi Kahraman +3
This paper presents Mask-aware Intersection-over-Union (maIoU) for assigning anchor boxes as positives and negatives during training of instance segmentation methods. Unlike conven…
Rank & Sort Loss for Object Detection and Instance Segmentation
Kemal Oksuz, Baris Can Cam, Emre Akbas +1
We propose Rank & Sort (RS) Loss, a ranking-based loss function to train deep object detection and instance segmentation methods (i.e. visual detectors). RS Loss supervises the cla…
One Metric to Measure them All: Localisation Recall Precision (LRP) for Evaluating Visual Detection Tasks
Kemal Oksuz, Baris Can Cam, Sinan Kalkan +1
Despite being widely used as a performance measure for visual detection tasks, Average Precision (AP) is limited in (i) reflecting localisation quality, (ii) interpretability and (…
A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection
Kemal Oksuz, Baris Can Cam, Emre Akbas +1
We propose average Localisation-Recall-Precision (aLRP), a unified, bounded, balanced and ranking-based loss function for both classification and localisation tasks in object detec…
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…
Localization Recall Precision (LRP): A New Performance Metric for Object Detection
Kemal Oksuz, Baris Can Cam, Emre Akbas +1
Average precision (AP), the area under the recall-precision (RP) curve, is the standard performance measure for object detection. Despite its wide acceptance, it has a number of sh…