Temporal　action　detection　(TAD)　aims　at　locating　the　action　boundaries　and　recognizing　their　categories　among　video　action　clips.　However,　vague　boundary　predictions　suffer　from　quick　switches　of　short　action　moments.　At　the　same　time,　due　to　the　vulnerable　boundaries　cheated　by　similar　video　backgrounds　altering　instead　of　the　motion,　the　model＇s　fine-grained　discrimination　is　highly　reliant　on　the　background.　To　mitigate　these　issues,　we　present　a　novel　dual-guidance　graph-based　architecture,　dubbed　as　dual-guidance　self-augmented　graph　network　(DSGN).　Specifically,　we　first　exploit　an　effective　expansion　approach,　inserting　a　full　zero　gap　sequence　between　the　original　feature　and　the　enhanced　feature　to　eliminate　the　effect　and　increase　the　localization　accuracy　of　short　action　boundaries.　Then,　a　relational　dual-aggregation　method　is　designed　to　integrate　two　levels　of　context　representations,　namely,　the　local　fragment-level　feature　and　the　global　video-level　feature,　into　the　target　proposal-level　feature,　based　on　their　attention　correlations　according　to　the　self-attention　mechanism,　to　augment　the　destination　proposal　clip.　We　demonstrate　that　our　model　outperforms　other　state-of-the-art　methods　experimentally　on　HACS,　ActivityNet　vI.3　and　FineActions.　©　2024　IEEE.