  Scale variance is one of the crucial challenges in multi-scale object
detection. Early approaches address this problem by exploiting the image and
feature pyramid, which raises suboptimal results with computation burden and
constrains from inherent network structures. Pioneering works also propose
multi-scale (i.e., multi-level and multi-branch) feature fusions to remedy the
issue and have achieved encouraging progress. However, existing fusions still
have certain limitations such as feature scale inconsistency, ignorance of
level-wise semantic transformation, and coarse granularity. In this work, we
present a novel module, the Fluff block, to alleviate drawbacks of current
multi-scale fusion methods and facilitate multi-scale object detection.
Specifically, Fluff leverages both multi-level and multi-branch schemes with
dilated convolutions to have rapid, effective and finer-grained feature
fusions. Furthermore, we integrate Fluff to SSD as FluffNet, a powerful
real-time single-stage detector for multi-scale object detection. Empirical
results on MS COCO and PASCAL VOC have demonstrated that FluffNet obtains
remarkable efficiency with state-of-the-art accuracy. Additionally, we indicate
the great generality of the Fluff block by showing how to embed it to other
widely-used detectors as well.
