Explicit Temporal-Semantic Modeling for Dense Video Captioning via Context-Aware Cross-Modal Interaction

TL;DR

This paper introduces CACMI, an explicit temporal-semantic modeling framework for dense video captioning that captures temporal coherence and semantics through cross-modal interaction, outperforming previous implicit models.

Contribution

The paper proposes a novel explicit modeling framework, CACMI, that enhances dense video captioning by integrating temporal and semantic information via cross-modal and context-aware mechanisms.

Findings

CACMI achieves state-of-the-art results on ActivityNet Captions.

CACMI outperforms existing methods in dense video captioning metrics.

Extensive experiments validate the effectiveness of explicit temporal-semantic modeling.

Abstract

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. However, 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.