Spatial-Semantic Transformer for Spatial Relation Recognition
Huilin Peng,
Yang Wang,
Hao Ge
Abstract:Spatial relation recognition, which aims to predict a spatial relation predicate, has attracted increasing attention in the computer vision study. During tackling this problem, modeling spatial relation of the subjects and objects is of great importance. We find that only using spatial features leads to poor results in predicting the spatial relation. To overcome these challenges, we propose an effective spatial attention module to enhance spatial features using semantic features. After identifying the importa… Show more
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