Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object Detection
This addresses a practical problem for computer vision applications where image restoration assumptions fail due to unknown or mismatched degradation.
The paper tackles object detection in low-quality images with unknown degradation by proposing a self-supervised framework that uses downsampling degradation as a transformation signal, achieving superior performance compared to existing methods in variant degradation situations.
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.