form is adopted, as to be described next.

Consider the training subset of a specific representation layer, which can be further decomposed as $\mathcal{D}^m=\{\mathcal{D}_+^m, \mathcal{D}_-^m \}$. Our bbox proposals can be generated at sliding-window locations that are partitioned into positive examples $\mathcal{D}_+^m$ and negative ones $\mathcal{D}_-^m$. An anchor is centered at the sliding window on each layer associated with aspect ratio of 0.41 (width to height, adopted based on [@DolEtAl:cvpr09]). For positive examples $\mathcal{D}_+^m$ , two kinds of anchors are labeled positive: (i) The anchor that has an IoU overlap higher than 0.5 with any ground-truth bbox; (ii) The anchor that has the highest IoU overlap with a ground-truth bbox. Note in the first case, a single ground-truth bbox may assign positive labels to multiple anchors; The second case is to ensure that at least one positive example exists for any ground-truth bbox. For negative examples $\mathcal{D}_-^m$ , a negative label is assigned if its IoU ratio is lower than 0.3 over all ground-truth boxes. The rest anchors that are neither positive nor negative are simply ignored as having no contribution to the training objective. In addition, to compensate for the imbalance of the positive and negative examples, a small 