Dense Video Captioning
25 papers with code • 4 benchmarks • 7 datasets
Most natural videos contain numerous events. For example, in a video of a “man playing a piano”, the video might also contain “another man dancing” or “a crowd clapping”. The task of dense video captioning involves both detecting and describing events in a video.
Benchmarks
These leaderboards are used to track progress in Dense Video Captioning
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ActivityNet Captions
YouCook2
YouCook
MTL-AQA
ViTT
VidChapters-7M
Vript
Most implemented papers
OmniVid: A Generative Framework for Universal Video Understanding
The core of video understanding tasks, such as recognition, captioning, and tracking, is to automatically detect objects or actions in a video and analyze their temporal evolution.
Streaming Dense Video Captioning
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.
Enhancing Traffic Safety with Parallel Dense Video Captioning for End-to-End Event Analysis
ucf-sst-lab/aicity2024cvprw
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Our solution mainly focuses on the following points: 1) To solve dense video captioning, we leverage the framework of dense video captioning with parallel decoding (PDVC) to model visual-language sequences and generate dense caption by chapters for video.
TrafficVLM: A Controllable Visual Language Model for Traffic Video Captioning
Traffic video description and analysis have received much attention recently due to the growing demand for efficient and reliable urban surveillance systems.
