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- W4386598497 abstract "Most of the existing dense video captioning models use a single modality of features for captioning. A video has a wide variety of information like spatial features, temporal features, audio features, and semantic features. In this paper, we propose a dense video captioning model that captures crossmodal attention between different types of features using an audio-visual attention block in the encoder and a hierarchical attention block in the decoder. The audio-visual attention block applies cross-modal attention between the RGB, flow, and audio features. The hierarchical attention block performs two-level attention between the semantic features and the features from the encoder for generating descriptions. The results show that the proposed approach performs better than the state-of-the-art approaches." @default.
- W4386598497 created "2023-09-12" @default.
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- W4386598497 date "2023-10-08" @default.
- W4386598497 modified "2023-09-30" @default.
- W4386598497 title "Multi-Modal Hierarchical Attention-Based Dense Video Captioning" @default.
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- W4386598497 doi "https://doi.org/10.1109/icip49359.2023.10222065" @default.
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