Exploring the semantic context in scene images is essential for indoor scene
recognition. However, due to the diverse intra-class spatial layouts and the
coexisting inter-class objects, modeling contextual relationships to adapt
various image characteristics is a great challenge. Existing contextual
modeling methods for indoor scene recognition exhibit two limitations: 1)
During training, space-independent information, such as color, may hinder
optimizing the network's capacity to represent the spatial context. 2) These
methods often overlook the differences in coexisting objects across different
scenes, suppressing the performance of scene recognition. To address these
limitations, we propose SpaCoNet, a novel approach that simultaneously models
the Spatial relation and Co-occurrence of objects based on semantic
segmentation. Firstly, the semantic spatial relation module (SSRM) is designed
to explore the spatial relations among objects within a scene. With the help of
semantic segmentation, this module decouples the spatial information from the
image, effectively avoiding the influence of irrelevant features. Secondly,
both spatial context features from SSRM and deep features from RGB feature
extractor are used to distinguish the coexisting object across different
scenes. Finally, utilizing the discriminative features mentioned above, we
employ the self-attention mechanism to explore the long-range co-occurrence
relationships among objects, and further generate a semantic-guided feature
representation for indoor scene recognition. Experimental results on three
publicly available datasets demonstrate the effectiveness and generality of the
proposed method. The code will be made publicly available after the
blind-review process is completed.

本文提出了一种基于语义分割的新方法 SpaCoNet，旨在同时模拟对象的空间关系和共现情况，进而在室内场景识别任务中生成语义引导的特征表示，该方法具有良好的通用性和效果。