  Aggregating context information from multiple scales has been proved to be
effective for improving accuracy of Single Shot Detectors (SSDs) on object
detection. However, existing multi-scale context fusion techniques are
computationally expensive, which unfavorably diminishes the advantageous speed
of SSD. In this work, we propose a novel network topology, called WeaveNet,
that can efficiently fuse multi-scale information and boost the detection
accuracy with negligible extra cost. The proposed WeaveNet iteratively weaves
context information from adjacent scales together to enable more sophisticated
context reasoning while maintaining fast speed. Built by stacking light-weight
blocks, WeaveNet is easy to train without requiring batch normalization and can
be further accelerated by our proposed architecture simplification.
Experimental results on PASCAL VOC 2007, PASCAL VOC 2012 benchmarks show
signification performance boost brought by WeaveNet. For 320x320 input of batch
size = 8, WeaveNet reaches 79.5% mAP on PASCAL VOC 2007 test in 101 fps with
only 4 fps extra cost, and further improves to 79.7% mAP with more iterations.
