Boosting Binary Object Change Detection via Unpaired Image Prototypes Contrast

Binary object change detection aims to monitor the evolution of the object of interest in a fixed region. Constructing a relevant dataset for deep learning models is strenuous. In the existing datasets, there is usually an imbalance between changed and unchanged samples, as well as a restricted diversity within the changed samples. Aiming at that, some methods use unpaired images used for object segmentation to generate pseudo-bitemporal images for change detection. However, due to the existence of the domain gap between different data sources, the model obtained by these methods cannot well generalize to the real bitemporal images. Inspired by them but to avoid the domain difference, we explore how to directly use the unpaired images within a real change detection dataset to complement changed samples. In detail, a concise metric-based framework is designed, which consists of two branches, a projector and a predictor. The framework obtains the change map (CM) by computing the distance between the bitemporal embedding outputted by the projector. Meanwhile, instructed by an indirect semantic supervision module (ISSM) specially designed, the predictor can generate the semantic confidence map distinguishing the pixels in an image into two categories. Based on the output of the framework, an unpaired image prototype contrast module (UIPCM) is proposed. It enriches the diversity of the change samples for training by combining the prototypes in unpaired images at the feature level, leading to alleviating the imbalance between changed and unchanged samples. Besides, a dual-margin contrastive loss (DMCL) is adopted during training. It can reduce the constraint on the consistency of bitemporal embedding in unchanged regions. The benefits and the superiority of the proposed method are demonstrated on two well-recognized datasets. The code is available at https://github.com/ptdoge/UIPC.