Exploring Resolution and Degradation Clues As Self-Supervised Signal for Low Quality Object Detection
Ziteng Cui, Yingying Zhu, Lin Gu, Guo-Jun Qi, Xiaoxiao Li, Renrui Zhang, Zenghui Zhang, Tatsuya Harada
"Image restoration algorithms such as super resolution (SR) are indispensable pre-processing modules for object detection in low qual-ity images. Most of these algorithms assume the degradation is fixed andknown a priori. However, in pratical, either the real degrdation or optimalup-sampling ratio rate is unknown or differs from assumption, leading toa deteriorating performance for both the pre-processing module and theconsequent high-level task such as object detection. Here, we propose anovel self-supervised framework to detect objects in degraded low res-olution images. We utilizes the downsampling degradation as a kind oftransformation for self-supervised signals to explore the equivariant representation against various resolutions and other degradation conditions.The Auto Encoding Resolution in Self-supervision (AERIS) frameworkcould further take the advantage of advanced SR architectures with anarbitrary resolution restoring decoder to reconstruct the original corre-spondence from the degraded input image. Both the representation learn-ing and object detection are optimized jointly in an end-to-end trainingfashion. The generic AERIS frameworkcould be implemented on variousmainstream object detection architectures from CNN to Transformer.The extensive experiments show that our methods has achieved supe-rior performance compared with existing methods when facing variantdegradation situations.We will release the open source code."