Prime Sample Attention in Object Detection

Understand

It is a common paradigm in object detection frameworks to treat all samples equally and target at maximizing the performance on average.

• In this work, we revisit this paradigm through a careful study on how different samples contribute to the overall performance measured in terms of mAP.
• Our study suggests that the samples in each mini-batch are neither independent nor equally important, and therefore a better classifier on average does not necessarily mean higher mAP.
• Motivated by this study, we propose the notion of Prime Samples, those that play a key role in driving the detection performance.