Search Results for author: Baris Can Cam

Found 7 papers, 7 papers with code

Mask-aware IoU for Anchor Assignment in Real-time Instance Segmentation

1 code implementation19 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.

Real-time Instance Segmentation Semantic Segmentation

Rank & Sort Loss for Object Detection and Instance Segmentation

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.)

Instance Segmentation Object Detection +1

One Metric to Measure them All: Localisation Recall Precision (LRP) for Evaluating Visual Detection Tasks

1 code implementation21 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.

Instance Segmentation Keypoint Detection +3

A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection

2 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.

Classification General Classification +1

Generating Positive Bounding Boxes for Balanced Training of Object Detectors

1 code implementation21 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.

Object Detection

Imbalance Problems in Object Detection: A Review

1 code implementation31 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.

Object Detection

Localization Recall Precision (LRP): A New Performance Metric for 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.

Object Detection

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