1 code implementation • 19 Oct 2021 • Kemal Oksuz, Baris Can Cam, Fehmi Kahraman, Zeynep Sonat Baltaci, Sinan Kalkan, Emre Akbas
We present the effectiveness of maIoU on a state-of-the-art (SOTA) assigner, ATSS, by replacing IoU operation by our maIoU and training YOLACT, a SOTA real-time instance segmentation method.
Ranked #9 on Real-time Instance Segmentation on MSCOCO
3 code implementations • ICCV 2021 • Kemal Oksuz, Baris Can Cam, Emre Akbas, Sinan Kalkan
RS Loss supervises the classifier, a sub-network of these methods, to rank each positive above all negatives as well as to sort positives among themselves with respect to (wrt.)
2 code implementations • 21 Nov 2020 • Kemal Oksuz, Baris Can Cam, Sinan Kalkan, Emre Akbas
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 (iii) robustness to the design choices regarding its computation, and its applicability to outputs without confidence scores.
3 code implementations • NeurIPS 2020 • Kemal Oksuz, Baris Can Cam, Emre Akbas, Sinan Kalkan
We propose average Localisation-Recall-Precision (aLRP), a unified, bounded, balanced and ranking-based loss function for both classification and localisation tasks in object detection.
Ranked #86 on Object Detection on COCO test-dev
1 code implementation • 21 Sep 2019 • Kemal Oksuz, Baris Can Cam, Emre Akbas, Sinan Kalkan
Using our generator as an analysis tool, we show that (i) IoU imbalance has an adverse effect on performance, (ii) hard positive example mining improves the performance only for certain input IoU distributions, and (iii) the imbalance among the foreground classes has an adverse effect on performance and that it can be alleviated at the batch level.
Ranked #194 on Object Detection on COCO minival
1 code implementation • 31 Aug 2019 • Kemal Oksuz, Baris Can Cam, Sinan Kalkan, Emre Akbas
In this paper, we present a comprehensive review of the imbalance problems in object detection.
3 code implementations • ECCV 2018 • Kemal Oksuz, Baris Can Cam, Emre Akbas, Sinan Kalkan
Moreover, we present LRP results of a simple online video object detector which uses a SOTA still image object detector and show that the class-specific optimized thresholds increase the accuracy against the common approach of using a general threshold for all classes.