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Collaboration by leveraging the shared semantic information plays a crucialrole in overcoming the perception capability limitations of isolated agents.However, existing collaborative perception methods tend to focus solely on thespatial features of semantic information, while neglecting the importance ofthe temporal dimension. Consequently, the potential benefits of collaborationremain underutilized. In this article, we propose Select2Col, a novelcollaborative perception framework that takes into account the{s}patial-t{e}mpora{l} importanc{e} of semanti{c} informa{t}ion. Within theSelect2Col, we develop a collaborator selection method that utilizes alightweight graph neural network (GNN) to estimate the importance of semanticinformation (IoSI) in enhancing perception performance, thereby identifyingcontributive collaborators while excluding those that bring negative impact.Moreover, we present a semantic information fusion algorithm called HPHA(historical prior hybrid attention), which integrates multi-scale attention andshort-term attention modules to capture the IoSI in feature representation fromthe spatial and temporal dimensions respectively, and assigns IoSI-consistentweights for efficient fusion of information from selected collaborators.Extensive experiments on two open datasets demonstrate that our proposedSelect2Col significantly improves the perception performance compared tostate-of-the-art approaches. The code associated with this research is publiclyavailable at https://github.com/huangqzj/Select2Col/.

Select2Col: Leveraging Spatial-Temporal Importance of Semantic Information for Efficient Collaborative Perception