Semantic-guided modeling of spatial relation and object co-occurrence for indoor scene recognition
arxiv(2023)
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 kind of spatial relationship among objects within
scenes in an artificially predefined manner, 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 the spatial information from the scene image and thoroughly explores
all spatial relationships among objects in an end-to-end manner. 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.
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