It has been shown that perfectly trained networks exhibit drastic reduction in performance when presented with distorted images. Streaming Network (STNet) is a novel architecture capable of robust classification of the distorted images while been trained on undistorted images. The distortion robustness is enabled by means of sparse input and isolated parallel streams with decoupled weights. Recent results prove STNet is robust to 20 types of noise and distortions. STNet exhibits state-of-the-art performance for classification of low light images, while being of much smaller size when other networks. In this paper, we construct STNets by using scaled versions (number of filters in each layer is reduced by factor of n) of popular networks like VGG16, ResNet50 and MobileNetV2 as parallel streams. These new STNets are tested on several datasets. Our results indicate that more efficient (less FLOPS), new STNets exhibit higher or equal accuracy in comparison with original networks. Considering a diversity of datasets and networks used for tests, we conclude that a new type of STNets is an efficient tool for robust classification of distorted images.
Distilling Vision Transformers for Distortion-Robust Representation Learning
Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Greece（希腊雅典国家与卡波迪斯蒂亚纳大学信息与电信系）；National Observatory of Athens, Greece（希腊雅典国家天文台）；Information Management Systems Institute, Athena Research Center, Greece（希腊阿提卡研究中心信息管理系统研究所）
SimFIR: A Simple Framework for Fisheye Image Rectification with Self-supervised Representation Learning
University of Science and Technology of China（中国科学技术大学）；Zhangjiang Laboratory（张江实验室）；The University of Sydney（悉尼大学）；Hefei Comprehensive National Science Center（合肥综合性国家科学中心）
提出 SimFIR 自监督框架，利用 ViT 图像块表示与统一畸变感知预训练任务学习细粒度畸变特征，从而提升鱼眼图像校正性能和真实场景泛化能力。
OSRT: Omnidirectional Image Super-Resolution with Distortion-aware Transformer
ARC, Tencent PCG（腾讯平台与内容事业群ARC实验室）；The University of Tokyo（东京大学）；Platform Technologies, Tencent Online Video（腾讯在线视频平台技术部）；SIAT, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shanghai AI Lab（上海人工智能实验室）
ML-CLIPSim: Multi-Layer CLIP Similarity for Machine-Oriented Image Quality
Simon Fraser University（西蒙弗雷泽大学）；University of British Columbia（不列颠哥伦比亚大学）；Eastern Institute of Technology（东部技术学院）；Xi’an Jiaotong University（西安交通大学）；Google Inc（谷歌公司）
