Weakly-supervised　video　object　localization　is　a　challenging　yet　important　task.　The　system　should　spatially　localize　the　object　of　interest　in　videos,　where　only　the　descriptive　sentences　and　their　corresponding　video　segments　are　given　in　the　training　stage.　Recent　efforts　propose　to　apply　image-based　Multiple　Instance　Learning　(MIL)　theory　in　this　video　task,　and　propagate　the　supervision　from　the　video　into　frames　by　applying　different　frame-weighting　strategies.　Despite　their　promising　progress,　the　spatio-temporal　correlation　between　different　object　regions　in　videos　has　been　largely　ignored.　To　fill　the　research　gap,　in　this　work　we　introduce　a　simple　but　effective　feature　expression　and　aggregation　framework,　which　utilizes　the　self-attention　mechanism　to　capture　the　latent　spatio-temporal　correlation　between　multimodal　object　features　and　design　a　multimodal　interaction　module　to　model　the　similarity　between　the　semantic　query　in　sentences　and　the　object　regions　in　videos.　We　conduct　extensive　experimental　evaluation　on　the　YouCookII　and　ActivityNet-Entities　datasets,　which　demonstrates　significant　improvements　over　multiple　competitive　baselines.　©　2021