Before revision:  We show that choosing positive examples separated by large temporal distances and negative examples close in the descriptor space improves the quality of the learned descriptors for the multi-object tracking task.
After revision:  We show that self-labelling challenging triplets--choosing positive examples separated by large temporal distances and negative examples close in the descriptor space improves the quality of the learned descriptors for the multi-object tracking task.