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Research map / Detection and segmentation / CNN features

Black-Box Explanation of Object Detectors via Saliency Maps

CVPR 2021 · Detection and segmentation › CNN features · approach: Region-based and query-based detectors · 143 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use black-box randomized masking weighted by detection similarity, including localization and category, instead of classification saliency baselinesYOLOv3 explanations · MS-COCO 2017 valPointing game (bbox): 0.7304 -> 0.9656Helped
Use black-box randomized masking weighted by detection similarity, including localization and category, instead of classification saliency baselinesYOLOv3 explanations · MS-COCO 2017 valPointing game (mask): 0.5195 -> 0.8458Helped
Use black-box randomized masking weighted by detection similarity, including localization and category, instead of classification saliency baselinesYOLOv3 explanations · MS-COCO 2017 valDeletion: 0.0464 -> 0.0440Helped
Use black-box randomized masking weighted by detection similarity, including localization and category, instead of classification saliency baselinesYOLOv3 explanations · MS-COCO 2017 valInsertion: 0.4561 -> 0.5622Helped
Show D-RISE explanations from the more accurate detector rather than the less accurate detectorYOLOv3 versus YOLOv3-Tiny · 242 objects on MS-COCO testUser preference for explanation being better or more trustworthy: 27.4% -> 50.2%Helped

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