  Image restoration algorithms such as super resolution (SR) are indispensable
pre-processing modules for object detection in low quality images. Most of
these algorithms assume the degradation is fixed and known a priori. However,
in practical, either the real degradation or optimal up-sampling ratio rate is
unknown or differs from assumption, leading to a deteriorating performance for
both the pre-processing module and the consequent high-level task such as
object detection. Here, we propose a novel self-supervised framework to detect
objects in degraded low resolution images. We utilizes the downsampling
degradation as a kind of transformation for self-supervised signals to explore
the equivariant representation against various resolutions and other
degradation conditions. The Auto Encoding Resolution in Self-supervision
(AERIS) framework could further take the advantage of advanced SR architectures
with an arbitrary resolution restoring decoder to reconstruct the original
correspondence from the degraded input image. Both the representation learning
and object detection are optimized jointly in an end-to-end training fashion.
The generic AERIS framework could be implemented on various mainstream object
detection architectures with different backbones. The extensive experiments
show that our methods has achieved superior performance compared with existing
methods when facing variant degradation situations. Code would be released at
https://github.com/cuiziteng/ECCV_AERIS.

Computer Vision and Pattern Recognition