室内场景识别中空间关系与对象共现的语义引导建模
Semantic-guided modeling of spatial relation and object co-occurrence for indoor scene recognition
AI总结：
针对室内场景识别中空间布局多样与对象共存差异的挑战，提出SpaCoNet，通过语义分割引导同时建模空间关系与对象共现，并在三个数据集上验证了其有效性与通用性。
摘要：
探索场景图像中的语义上下文对于室内场景识别至关重要。然而，由于类内空间布局的多样性和类间对象的共存性，建模上下文关系以适应各种图像特征是一个巨大的挑战。现有的场景识别上下文建模方法存在两个局限性：1）它们通常仅建模场景内对象之间的一种空间关系（顺序或度量），对多样空间布局的探索有限。2）它们往往忽视不同场景中共存对象的差异，抑制了场景识别性能。为克服这些局限，我们提出SpaCoNet，它同时建模由语义分割引导的对象空间关系和共现。首先，构建语义空间关系模块（SSRM）以建模场景空间特征。借助语义分割，该模块从场景图像中解耦空间信息，并以隐式方式彻底探索对象之间的所有空间关系，从而获得基于语义的空间特征。其次，将SSRM的空间特征和图像特征提取模块的深度特征分配给每个对象，以区分不同场景中的共存对象。最后，利用上述判别性特征，我们设计了一个全局-局部依赖模块来探索对象间的长距离共现，并进一步生成用于室内场景识别的语义引导特征表示。在三个广泛使用的场景数据集上的实验结果表明了所提方法的有效性和通用性。
英文摘要：
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 scene recognition exhibit two limitations: 1) They typically model only one type of spatial relationship (order or metric) among objects within scenes, with limited exploration of diverse spatial layouts. 2) They often overlook the differences in coexisting objects across different scenes, suppressing scene recognition performance. To overcome these limitations, we propose SpaCoNet, which simultaneously models Spatial relation and Co-occurrence of objects guided by semantic segmentation. Firstly, the Semantic Spatial Relation Module (SSRM) is constructed to model scene spatial features. With the help of semantic segmentation, this module decouples spatial information from the scene image and thoroughly explores all spatial relationships among objects in an implicit manner, thereby obtaining semantic-based spatial features. Secondly, both spatial features from the SSRM and deep features from the Image Feature Extraction Module are allocated to each object, so as to distinguish the coexisting object across different scenes. Finally, utilizing the discriminative features above, we design a Global-Local Dependency Module to explore the long-range co-occurrence among objects, and further generate a semantic-guided feature representation for indoor scene recognition. Experimental results on three widely used scene datasets demonstrate the effectiveness and generality of the proposed method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
