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+presents+a+significant+challenge%252C+akin+to+finding+a+needle+in+a+haystack.+Adapting+existing+short+video+(5-30+seconds)+grounding+methods+to+this+problem+yields+poor+performance.+Since+most+real+life+videos%252C+such+as+those+on+YouTube+and+AR%252FVR%252C+are+lengthy%252C+addressing+this+issue+is+crucial.+Existing+methods+typically+operate+in+two+stages%3a+clip+retrieval+and+grounding.+However%252C+this+disjoint+process+limits+the+retrieval+module%27s+fine-grained+event+understanding%252C+crucial+for+specific+moment+detection.+We+propose+RGNet+which+deeply+integrates+clip+retrieval+and+grounding+into+a+single+network+capable+of+processing+long+videos+into+multiple+granular+levels%252C+e.g.%252C+clips+and+frames.+Its+core+component+is+a+novel+transformer+encoder%252C+RG-Encoder%252C+that+unifies+the+two+stages+through+shared+features+and+mutual+optimization.+The+encoder+incorporates+a+sparse+attention+mechanism+and+an+attention+loss+to+model+both+granularity+jointly.+Moreover%252C+we+introduce+a+contrastive+clip+sampling+technique+to+mimic+the+long+video+paradigm+closely+during+training.+RGNet+surpasses+prior+methods%252C+showcasing+state-of-the-art+performance+on+long+video+temporal+grounding+(LVTG)+datasets+MAD+and+Ego4D.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.f0805a74467cedd1f1144d2c4c23e532%3frik%3dfkKH%252boagylTaxw%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Retrieval+Clips&FORM=IRPRST&ck=2A9596FC56C0A76271AA507E40B3B1C3&selectedIndex=11&itb=0)
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