论文标题
显着对象细分的本地环境关注
Local Context Attention for Salient Object Segmentation
论文作者
论文摘要
显着对象分割旨在区分各种显着对象和背景。尽管缺乏语义一致性，但显着物体通常在本地具有明显的质感和位置特征。基于此先验，我们提出了一个新颖的本地上下文注意网络（LCANET），以在统一的代表性体系结构中生成本地增强特征图。提出的网络引入了一个注意相关滤波器（ACF）模块，以通过计算粗略预测和全局上下文之间的相关特征图来产生明确的局部关注。然后将其扩展到本地上下文块（LCB）。此外，基于LCB实现了一个阶段的粗到最新结构，以适应性地增强局部上下文描述能力。全面的实验是在几个显着的对象分割数据集上进行的，这表明拟议的lcanet与最先进的方法相比，尤其是最大F-SCORE和0.034 MAE在DUTS-TE数据集上的表现。
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.
