关键词: 广义上下文, 特征聚合, 批维度注意力, 十字条纹注意力, 人体解析

Abstract: Human parsing aims to achieve fine-grained segmentation of human body parts in images. Some human parsing methods enhance part representations by aggregating contextual features, but the scope of such contextual aggregation is often limited. To address this issue, a generalized contextual features aggregation human parsing method was proposed. Guided by prior knowledge of human topological structure, the proposed method not only aggregates contextual features globally within the current image but also extends the aggregation scope to other images. This extended scope was defined as generalized context. For the current image, a Cross-Stripe Attention Module (CSAM) was designed to aggregate global contextual features within the image. This module leverages the part distribution to model the human topological structure prior within the image and uses it as guidance to aggregate contextual features along horizontal and vertical stripes. For other images, a Region-aware Batch Attention Module (RBAM) was designed to aggregate inter-image contextual features at the batch level. Due to the constraints of human topological structure, the positional deviations of similar parts across batch images fall within a certain range. This enables the module to learn the spatial offsets between similar parts across different human images. Based on these offsets, it aggregates features from similar part regions in other images along the batch dimension. Quantitative comparison results show that our method improves the mean Intersection over Union (mIoU) on the LIP dataset by 0.42 percentage points compared to DTML (Dual-Task Mutual Learning). Visualization experiments demonstrate that the proposed method effectively aggregates global features from the current image and part features from other images within the generalized context.

Key words: generalized contextual feature, feature aggregation, batch-dimension attention, cross-stripe attention, human parsing