Trash to Treasure: Low-Light Object Detection via
  Decomposition-and-Aggregation
Xiaohan Cui Long Ma Tengyu Ma Jinyuan Liu Xin Fan Risheng Liu
Object detection in low-light scenarios has attracted much attention in the
past few years. A mainstream and representative scheme introduces enhancers as
the pre-processing for regular detectors. However, because of the disparity in
task objectives between the enhancer and detector, this paradigm cannot shine
at its best ability. In this work, we try to arouse the potential of enhancer +
detector. Different from existing works, we extend the illumination-based
enhancers (our newly designed or existing) as a scene decomposition module,
whose removed illumination is exploited as the auxiliary in the detector for
extracting detection-friendly features. A semantic aggregation module is
further established for integrating multi-scale scene-related semantic
information in the context space. Actually, our built scheme successfully
transforms the "trash" (i.e., the ignored illumination in the detector) into
the "treasure" for the detector. Plenty of experiments are conducted to reveal
our superiority against other state-of-the-art methods. The code will be public
if it is accepted.