Floor plan recognition requires accurate segmentation and classification of entrance doors, outer contours (walls and windows) and inner contours (various room types) , despite strong spatial dependencies and large stylistic differences between different datasets. To overcome these challenges, we propose FloorPlanFormer, a multi-task learning network divided into three phases: the first phase introduces a Swin Transformer backbone with a pixel decoder to extract fine-grained pixel-level semantics; the second phase employs prompt encoder and mask decoder, and a novel Global Contextual Attention Module (GCAM) is designed to generate clear, high-quality outer contour masks; the third stage uses mask transformer decoder to recognize targets and designs a Masked Feature Refinement Module (MFRM) to accurately delineate the inner contour by modeling the relationship between the local inner and outer contours. Finally, we constructed FloorPlan8K, a dataset containing 8200 images and 77434 instances, on which our model was trained and evaluated, and the results greatly outperformed the state-of-the-art general segmentation methods and specialized methods.

Software defect prediction helps quality assurance teams find defects in software, thereby enhancing the reliability of the systems.In existing code-visualization-based defect prediction methods, challenges arise from mixing code information and the potential omission of critical defect features.To enhance the completeness of code features, this paper proposes a defect prediction model based on code grayscale pixel visualization with a fusion attention mechanism (Gpv2DP).Gpv2DP converts code into grayscale images and reshapes the images to a standard size, effectively alleviating the information loss problem caused by element mixing and image cropping.Furthermore, it constructs a code feature extracting network that simultaneously integrates the channel, spatial and 3D attention.We conduct empirical experiments on ten open-source Java projects from the PROMISE repository.The results show that the F-measure and AUC metrics of Gpv2DP outperform related defect prediction methods.