Adaptive network combination for single-image reflection removal:a domain generalization perspective  

作　　者：Ming LIU Jianan PAN Zifei YAN Wangmeng ZUO Lei ZHANG 

机构地区：[1]School of Computer Science and Technology,Harbin Institute of Technology,Harbin 150001,China [2]Department of Computing,The Hong Kong Polytechnic University,Hong Kong 999077,China

出　　处：《Frontiers of Computer Science》2025年第1期171-173,共3页计算机科学前沿(英文版)

基　　金：supported in part by the National Natural Science Foundation of China(Grant No.U19A2073).

摘　　要：1 Introduction Recently,multiple synthetic and real-world datasets have been built to facilitate the training of deep single-image reflection removal(SIRR)models.Meanwhile,diverse testing sets are also provided with different types of reflections and scenarios.However,the non-negligible domain gaps between training and testing sets make it difficult to learn deep models generalizing well to testing images.The diversity of reflections and scenes further makes it a mission impossible to learn a single model being effective for all testing sets and real-world reflections.In this paper,we tackle these issues by learning SIRR models from a domain generalization perspective.

关 键 词：testing GENERALIZATION WHILE 

分 类 号：H31[语言文字—英语]