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Found 3 papers, 3 papers with code
Focaler-IoU: More Focused Intersection over Union Loss
Existing researchs improve regression performance by utilizing the geometric relationship between bounding boxes, while ignoring the impact of difficult and easy sample distribution on bounding box regression.
Shape-IoU: More Accurate Metric considering Bounding Box Shape and Scale
As an important component of the detector localization branch, bounding box regression loss plays a significant role in object detection tasks.
Inner-IoU: More Effective Intersection over Union Loss with Auxiliary Bounding Box
Based on the above, we first analyzed the BBR model and concluded that distinguishing different regression samples and using different scales of auxiliary bounding boxes to calculate losses can effectively accelerate the bounding box regression process.
Ranked #1 on
Object Detection
on AI-TOD
(mAP50 metric)
