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Found 7 papers, 7 papers with code
Mask-aware IoU for Anchor Assignment in Real-time Instance Segmentation
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
Rank & Sort Loss for Object Detection and Instance Segmentation
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.)
One Metric to Measure them All: Localisation Recall Precision (LRP) for Evaluating Visual Detection Tasks
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.
A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection
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 #75 on
Object Detection
on COCO test-dev
Generating Positive Bounding Boxes for Balanced Training of Object Detectors
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
Imbalance Problems in Object Detection: A Review
In this paper, we present a comprehensive review of the imbalance problems in object detection.
Localization Recall Precision (LRP): A New Performance Metric for Object Detection
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.
