At thet-th backward propagation step, we can derive the gradient il(wt)toupdatei-th module inM. The number in the bracket represents the batch size. We see that when the batch size is small (i.e.,32), the gradientvariancesaresimilar. N and K indicate the number of FPN levels and region proposals fed into the detection head. To evaluate this assumption, as shown in Figure 1, we have three observations. As illustrated by the second figure in Figure 1, the gradient misalignment phenomenon between detection head and backbone has been reduced.

This paper aims to tackle the challenging problem of one-shot object detection.

Morerecenttechniques[ 8,66,67]focus on the discovery of image links and individual object locations within much more diverse image collections.

Semi-supervised learning aims to leverage alarge amount of unlabeled data for performance boosting. Existing works primarily focus on image classification. Inthispaper,wedelveintosemi-supervised learning forobject detection, where labeled data are more labor-intensive to collect.

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Neural Information Processing Systems http://nips.cc/