Unsupervised Hard Example Mining from Videos for Improved Object Detection

Understand

Important gains have recently been obtained in object detection by using training objectives that focus on {\em hard negative} examples, i.e., negative examples that are currently rated as positive or ambiguous by the detector.

• These examples can strongly influence parameters when the network is trained to correct them.
• Unfortunately, they are often sparse in the training data, and are expensive to obtain.
• In this work, we show how large numbers of hard negatives can be obtained {\em automatically} by analyzing the output of a trained detector on video sequences.