Streaming Dense Video Captioning
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Abstract (arXiv)
An ideal model for dense video captioning -- predicting captions localized temporally in a video -- should be able to handle long input videos, predict rich, detailed textual descriptions, and be able to produce outputs before processing the entire video. Current state-of-the-art models, however, process a fixed number of downsampled frames, and make a single full prediction after seeing the whole video. We propose a streaming dense video captioning model that consists of two novel components: First, we propose a new memory module, based on clustering incoming tokens, which can handle arbitrarily long videos as the memory is of a fixed size. Second, we develop a streaming decoding algorithm that enables our model to make predictions before the entire video has been processed. Our model achieves this streaming ability, and significantly improves the state-of-the-art on three dense video captioning benchmarks: ActivityNet, YouCook2 and ViTT. Our code is released at https://github.com/google-research/scenic.
Key facts. Cited by 82 papers in the map (data through 7 Oct 2026). Builds on 55 of its 67 references that are in the map. First posted on arXiv 1 Apr 2024.
Builds on
55 of 67 references.
References · 55
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- 2023Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioningcited by 263
- 2023PaLI-X: On Scaling up a Multilingual Vision and Language Modelcited by 254
- 2018Long-Term Feature Banks for Detailed Video Understandingcited by 239
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- 2021End-to-End Dense Video Captioning with Parallel Decodingcited by 133
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- 2022UniFormerV2: Spatiotemporal Learning by Arming Image ViTs with Video UniFormercited by 116
- 2020Multi-modal Dense Video Captioningcited by 113
- 2021MAD: A Scalable Dataset for Language Grounding in Videos from Movie Audio Descriptionscited by 107
- 2019The 2019 DAVIS Challenge on VOS: Unsupervised Multi-Object Segmentationcited by 95
- 2020Actor-Context-Actor Relation Network for Spatio-Temporal Action Localizationcited by 91
- 2020Multimodal Pretraining for Dense Video Captioningcited by 79
- 2020A Better Use of Audio-Visual Cues: Dense Video Captioning with Bi-modal Transformercited by 75
- 2021Object-Region Video Transformerscited by 73
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- 2018Bidirectional Attentive Fusion with Context Gating for Dense Video Captioningcited by 68
- 2022Real-time Online Video Detection with Temporal Smoothing Transformerscited by 58
- 2016Online Real-time Multiple Spatiotemporal Action Localisation and Predictioncited by 57
- 2021SCENIC: A JAX Library for Computer Vision Research and Beyondcited by 56
- 2023AutoAD: Movie Description in Contextcited by 50
50 most cited of 55 All 55 (JSON)
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Citing papers per year: 2024: 15; 2025: 44; 2026: 23.
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