In the field of video content organization, segmenting long videos into chapters emerges as an important capability, allowing users to quickly identify the information they want. Unfortunately, this topic has hardly received any research attention due to the paucity of publicly available data sets.
To address this challenge, we present VidChapters-7M, a dataset comprising 817,000 videos that have been meticulously segmented into an impressive 7 million chapters. This dataset is automatically assembled by extracting user-annotated chapters from online videos, avoiding the need for labor-intensive manual annotations.
Within the scope of VidChapters-7M, researchers have introduced three distinct tasks. Firstly, there is the task of generating video chapters, which involves the temporal division of a video into segments, accompanied by the generation of a descriptive title for each segment. To further deconstruct this task, two variations are defined: video chapter generation with predefined segment boundaries, where the challenge lies in generating titles for segments with annotated boundaries, and video chapter basing, which requires boundary localization. times of a chapter based on its annotated content. qualification.
A comprehensive evaluation of these tasks was conducted using fundamental foundational approaches and state-of-the-art video language models. The image above shows an illustration of the three tasks defined for VidChapters-7M. Furthermore, pre-training on VidChapters-7M has been shown to produce notable gains on dense video captioning tasks, both in zero shot and adjustment scenarios. This advancement significantly raises the state of the art on benchmark datasets such as YouCook2 and ViTT. Ultimately, experiments have revealed a positive correlation between pre-training dataset size and improved performance in subsequent applications.
VidChapters-7M inherits certain limitations due to its origin from YT-Temporal-180M. These limitations are associated with biases in the distribution of video categories that are present in the source data set. The advancement of video chapter generation models has the potential to facilitate downstream applications, some of which could have negative social impacts, such as video surveillance.
Additionally, models trained on VidChapters-7M may inadvertently reflect biases that exist in videos obtained from platforms such as YouTube. It is necessary to be aware of these considerations when implementing, analyzing or developing these models.
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Janhavi Lande, Graduated in Engineering Physics from IIT Guwahati, Class of 2023. She is an upcoming data scientist and has been working in the world of ml/ai research for the last two years. What fascinates him most is this ever-changing world and its constant demand for humans to keep up. In her hobbies she likes to travel, read and write poems.
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