{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:37:46Z","timestamp":1773801466199,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Dense video captioning jointly localizes and captions salient events in untrimmed videos. Recent methods primarily focus on leveraging additional prior knowledge and advanced multi-task architectures to achieve competitive performance.\nHowever, these pipelines rely on implicit modeling that uses frame-level or fragmented video features, failing to capture the temporal coherence across event sequences and comprehensive semantics within visual contexts. To address this, we propose an explicit temporal-semantic modeling framework called Context-Aware Cross-Modal Interaction (CACMI), which leverages both latent temporal characteristics within videos and linguistic semantics from text corpus. Specifically, our model consists of two core components: Cross-modal Frame Aggregation aggregates relevant frames to extract temporally coherent, event-aligned textual features through cross-modal retrieval; and Context-aware Feature Enhancement utilizes query-guided attention to integrate visual dynamics with pseudo-event semantics. Extensive experiments on the ActivityNet Captions and YouCook2 datasets demonstrate that CACMI achieves the state-of-the-art performance on dense video captioning task.<\/jats:p>","DOI":"10.1609\/aaai.v40i7.37450","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T23:21:44Z","timestamp":1773789704000},"page":"5341-5349","source":"Crossref","is-referenced-by-count":0,"title":["Explicit Temporal-Semantic Modeling for Dense Video Captioning via Context-Aware Cross-Modal Interaction"],"prefix":"10.1609","volume":"40","author":[{"given":"Mingda","family":"Jia","sequence":"first","affiliation":[]},{"given":"Weiliang","family":"Meng","sequence":"additional","affiliation":[]},{"given":"Zenghuang","family":"Fu","sequence":"additional","affiliation":[]},{"given":"Yiheng","family":"Li","sequence":"additional","affiliation":[]},{"given":"Qi","family":"Zeng","sequence":"additional","affiliation":[]},{"given":"Yifan","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Ju","family":"Xin","sequence":"additional","affiliation":[]},{"given":"Rongtao","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Jiguang","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xiaopeng","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/37450\/41412","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/37450\/41412","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T23:21:44Z","timestamp":1773789704000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/37450"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i7.37450","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}