Recently,　weakly　supervised　temporal　sentence　grounding　in　videos　(TSGV)　has　attracted　extensive　attention　because　it　does　not　require　precise　start-end　time　annotations　during　training,　and　it　can　quickly　retrieve　interesting　segments　according　to　user　needs.　In　weakly　supervised　TSGV,　query　reconstruction　(QR)-based　methods　are　the　current　mainstream,　and　the　quality　of　proposals　determines　their　performance.　QR-based　methods　have　two　problems　in　proposal　quality.　First,　a　multi-modal　global　token　is　usually　mapped　to　proposals　with　limited　duration　diversity,　making　it　difficult　to　capture　relevant　segments　at　varying　durations　in　real　scenarios.　Additionally,　Gaussian　functions　are　typically　used　to　generate　relatively　fixed　weights　for　frames　within　proposals,　which　weigh　the　original　video　features　to　generate　proposal-specific　features.　This　results　in　query-irrelevant　frames　affecting　the　discrimination　of　the　proposal　features.　In　this　study,　we　propose　a　query-aware　multi-scale　proposal　network　(QMN).　Initially,　pre-trained　encoders　are　used　to　extract　video　and　query　features.　Subsequently,　a　multi-scale　proposal　generation　module　is　designed　to　refine　video　features　guided　by　queries　and　diversify　the　duration　of　the　proposal.　This　module　performs　multi-modal　interaction　and　multi-scale　modeling　to　obtain　proposals　of　different　durations.　Furthermore,　to　extract　discriminative　proposal　features　and　enhance　the　modeling　of　proposal　frame　correlation,　a　query-aware　weight　generator　is　constructed　to　learn　frame　weights　to　suppress　query-irrelevant　frame　representations　through　contrastive　learning.　Finally,　the　masked　query　is　reconstructed　using　the　proposal　features　to　select　the　best　proposal.　The　effectiveness　of　the　proposed　QMN　is　verified　through　experiments　on　the　Charades-STA　and　ActivityNet-Captions　datasets.　©　2024　Elsevier　B.V.