Semantic segmentation has achieved great progress by effectively fusing features of contextual information. In this article, we propose an end-to-end semantic attention boosting (SAB) framework to adaptively fuse the contextual information iteratively across layers with semantic regularization. Specifically, we first propose a pixelwise semantic attention (SAP) block, with a semantic metric representing the pixelwise category relationship, to aggregate the nonlocal contextual information. In addition, we improve the computation complexity of SAP block from to for images with size . Second, we present a categorywise semantic attention (SAC) block to adaptively balance the nonlocal contextual dependencies and the local consistency with a categorywise weight, overcoming the contextual information confusion caused by the feature imbalance within intra-category. Furthermore, we propose the SAB module to refine the segmentation with SAC and SAP blocks. By applying the SAB module iteratively across layers, our model shrinks the semantic gap and enhances the structure reasoning by fully utilizing the coarse segmentation information. Extensive quantitative evaluations demonstrate that our method significantly improves the segmentation results and achieves superior performance on the PASCAL VOC 2012, Cityscapes, PASCAL Context, and ADE20K datasets.

Cross-domain recommendation (CDR) leverages knowledge from the source domain to make recommendations for the cold-start users in the target domain. On account of fully utilizing information, various relationships among users and items are taken into account, i.e., the interaction relationship between users and their corresponding items; the relationship among users or items; and the indirect relationship between the user and items related to other users. In order to process these relationships, we propose a novel framework named Memory Pool Variational AutoEncoder (MPVAE). The main advantages of the MPVAE model lie in three aspects: (1) it generates the embedding representations that incorporate more information by a memory pool mechanism in the source and target domains; (2) it involves the relationship among users or items efficiently by the similarity measurement, further, the indirect relationship can be explicitly described, which makes full use of information in the source domain; and (3) it leverages the superiority of the probability model from the perspective of the VAE structure, which ensures generation and robustness. Comprehensive experiments on three real datasets show that the proposed model achieves remarkable superiority over several competitive CDR models.

Semantic context has raised concerns in semantic segmentation. In most cases, it is applied to guide feature learning. Instead, this paper applies it to extract the semantic representation, which records the global feature information of each category with a memory tensor. Specifically, we propose a novel semantic representation (SR) module, which consists of semantic embedding (SE) and semantic attention (SA) blocks. The SE block adaptively embeds features into the semantic representation by calculating the memory similarity, and the SA block aggregates the embedded features with semantic attention. The main advantages of the SR module lie in three aspects: i) it enhances the representation ability of semantic context by employing global (cross-image) semantic information; ii) it improves the consistency of intraclass features by aggregating global features of the same categories; and iii) it can be extended to build a semantic representation refinement network (SRRNet) by iteratively applying the SR module across multiple scales, shrinking the semantic gap and enhancing the structural reasoning of the model. Extensive experiments demonstrate that our method significantly improves the segmentation results and achieves superior performance on the PASCAL VOC 2012, Cityscapes, and PASCAL Context datasets.