  Single-branch object detection methods use shared features for localization
and classification, yet the shared features are not fit for the two different
tasks simultaneously. Multi-branch object detection methods usually use
different features for localization and classification separately, ignoring the
relevance between different tasks. Therefore, we propose multi-semantic
interactive learning (MSIL) to mine the semantic relevance between different
branches and extract multi-semantic enhanced features of objects. MSIL first
performs semantic alignment of regression and classification branches, then
merges the features of different branches by semantic fusion, finally extracts
relevant information by semantic separation and passes it back to the
regression and classification branches respectively. More importantly, MSIL can
be integrated into existing object detection nets as a plug-and-play component.
Experiments on the MS COCO, and Pascal VOC datasets show that the integration
of MSIL with existing algorithms can utilize the relevant information between
semantics of different tasks and achieve better performance.
