An Analysis of Scale Invariance in Object Detection - SNIP

Understand

An analysis of different techniques for recognizing and detecting objects under extreme scale variation is presented.

• Scale specific and scale invariant design of detectors are compared by training them with different configurations of input data.
• By evaluating the performance of different network architectures for classifying small objects on ImageNet, we show that CNNs are not robust to changes in scale.
• Based on this analysis, we propose to train and test detectors on the same scales of an image-pyramid.