MSR-VTT Dataset
Adaptation Method
CLIP2Video is a method that combines text and video content, aiming to extend the CLIP (Contrastive Language-Image Pretraining) model to video understanding. Its core idea is to leverage the visual and language embedding capabilities of the CLIP model to generate cross-modal queries that identify content in videos related to text descriptions. The goal of this method is to provide an effective solution for video retrieval, video generation, and video understanding tasks.
The CLIP2Video method works by mapping video frames and text descriptions through the CLIP model into a unified semantic space, thereby enabling efficient video retrieval and content understanding.
Data Description
MSR-VTT (Microsoft Research Video to Text) is a video-to-text dataset released by Microsoft Research, primarily used for the task of video captioning. The dataset contains 10,000 videos from YouTube, with an average duration of about 10 seconds per video, covering a wide range of scenes and activities.
The goal of the dataset is to generate natural language descriptions based on visual content. It is widely used in fields such as video understanding, automatic video annotation, and video summarization.
Dataset Composition and Scale
The dataset contains 10,000 videos, with a total data size of approximately 6.3GB. Each video is associated with at least 20 natural language descriptions for training and testing. The video content includes categories such as daily life, sports activities, entertainment shows, and more.
Number of Videos: 10,000 videos
Data Size: Approximately 6.3GB
Annotations
The annotations in the dataset include the following information:
| KEYS | EXPLAIN |
|---|---|
| video_id | Unique identifier for the video |
| description | Natural language description of the video |
| category_id | Category identifier for the video |
Tasks
The main task of the MSR-VTT dataset is video captioning. Given a video, the model is required to automatically generate a description corresponding to the video's content. This task tests the model's ability to understand video content and convert it into natural language.
Dataset Usage
Download the dataset:
- You can download the dataset from the MSR-VTT official website.
- The dataset includes video files and annotation files. The videos are in .mp4 format, and the annotation files are in .json format.
Preprocessing the dataset:
- Before training the model, you can use common image processing tools to extract frames from the videos.
- It is recommended to use FFmpeg to extract key frames from the videos, or OpenCV to process video data.
References
Xu, Jun, and others. "MSR-VTT: A large video description dataset for bridging video and language." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
Xu, Dejing, et al. "Video question answering via gradually refined attention over appearance and motion." In Proceedings of the 25th ACM International Conference on Multimedia, 2017.