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
1 code implementation • 3 Jan 2023 • Fehmi Kahraman, Kemal Oksuz, Sinan Kalkan, Emre Akbas
(ii) Motivated by our observations, e. g., that NMS-free detectors can also benefit from correlation, we propose Correlation Loss, a novel plug-in loss function that improves the performance of various object detectors by directly optimizing correlation coefficients: E. g., Correlation Loss on Sparse R-CNN, an NMS-free method, yields 1. 6 AP gain on COCO and 1. 8 AP gain on Cityscapes dataset.
no code implementations • 28 Dec 2023 • Barış Can Çam, Kemal Öksüz, Fehmi Kahraman, Zeynep Sonat Baltaci, Sinan Kalkan, Emre Akbaş
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 segmentation methods.