Temporal　text　localization　(TTL)　task　refers　to　identify　a　segment　within　a　long　untrimmed　video　that　semantically　matches　a　given　textual　query.　However,　most　methods　require　extensive　manual　annotation　of　temporal　boundaries　for　each　query,　which　restricts　their　scalability　and　practicality　in　real-world　applications.　Moreover,　modeling　temporal　context　information　is　particularly　crucial　for　TTL　task.　In　this　paper,　a　Vision　Token　Rolling　Transformer　for　weakly　supervised　temporal　text　localization　(VTR-former)　is　developed.　VTR-former　does　not　rely　on　predefined　temporal　boundaries　during　training　or　testing.　It　significantly　improves　the　performance　of　the　model　in　temporal　information　capture　and　feature　representation　by　rolling　vision　tokens　and　utilizing　advanced　feature　learning　modules　based　on　the　transformer.　Experiments　on　two　challenging　benchmarks,　including　Charades-STA　and　ActivityNet　Captions,　demonstrate　that　VTR-former　outperforms　the　baseline　network　and　achieves　the　leading　performance.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2025.