Learning Spatial Fusion for Single-Shot Object Detection

Understand

Pyramidal feature representation is the common practice to address the challenge of scale variation in object detection.

• However, the inconsistency across different feature scales is a primary limitation for the single-shot detectors based on feature pyramid.
• In this work, we propose a novel and data driven strategy for pyramidal feature fusion, referred to as adaptively spatial feature fusion (ASFF).
• It learns the way to spatially filter conflictive information to suppress the inconsistency, thus improving the scale-invariance of features, and introduces nearly free inference overhead.