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This paper examines performance evaluation criteria for basic vision tasksinvolving sets of objects namely, object detection, instance-level segmentationand multi-object tracking. The rankings of algorithms by an existing criterioncan fluctuate with different choices of parameters, e.g. Intersection overUnion (IoU) threshold, making their evaluations unreliable. More importantly,there is no means to verify whether we can trust the evaluations of acriterion. This work suggests a notion of trustworthiness for performancecriteria, which requires (i) robustness to parameters for reliability, (ii)contextual meaningfulness in sanity tests, and (iii) consistency withmathematical requirements such as the metric properties. We observe that theserequirements were overlooked by many widely-used criteria, and explorealternative criteria using metrics for sets of shapes. We also assess all thesecriteria based on the suggested requirements for trustworthiness.

How Trustworthy are Performance Evaluations for Basic Vision Tasks?