Salient object segmentation aims at distinguishing various salient objects from backgrounds. Despite the lack of semantic consistency, salient objects often have obvious texture and location characteristics in local area. Based on this priori, we propose a novel Local Context Attention Network (LCANet) to generate locally reinforcement feature maps in a uniform representational architecture. The proposed network introduces an Attentional Correlation Filter (ACF) module to generate explicit local attention by calculating the correlation feature map between coarse prediction and global context. Then it is expanded to a Local Context Block(LCB). Furthermore, an one-stage coarse-to-fine structure is implemented based on LCB to adaptively enhance the local context description ability. Comprehensive experiments are conducted on several salient object segmentation datasets, demonstrating the superior performance of the proposed LCANet against the state-of-the-art methods, especially with 0.883 max F-score and 0.034 MAE on DUTS-TE dataset.

中文翻译：

---

显着对象分割的局部上下文注意

显着对象分割旨在将各种显着对象与背景区分开来。尽管缺乏语义一致性，显着对象在局部区域往往具有明显的纹理和位置特征。基于此先验，我们提出了一种新颖的局部上下文注意网络 (LCANet)，以在统一的表示架构中生成局部强化特征图。所提出的网络引入了注意力相关过滤器（ACF）模块，通过计算粗略预测和全局上下文之间的相关特征图来生成显式局部注意力。然后将其扩展为本地上下文块（LCB）。此外，基于LCB实现了一级从粗到精的结构，以自适应地增强局部上下文描述能力。