DeVAn: Dense Video Annotation for Video-Language Models

TL;DR

DeVAn is a new dataset with human-annotated video descriptions and summaries for evaluating visual-language models on real-world videos, including captioning, summarization, and retrieval tasks.

Contribution

It introduces DeVAn, a comprehensive dataset with dense annotations for videos, and benchmarks current models on captioning, summarization, and retrieval, emphasizing human-aligned evaluation metrics.

Findings

Model-based metrics align better with human preferences.

Current models show room for improvement in dense video captioning.

DeVAn provides a standardized benchmark for future video-language research.

Abstract

We present a novel human annotated dataset for evaluating the ability for visual-language models to generate both short and long descriptions for real-world video clips, termed DeVAn (Dense Video Annotation). The dataset contains 8.5K YouTube video clips of 20-60 seconds in duration and covers a wide range of topics and interests. Each video clip is independently annotated by 5 human annotators, producing both captions (1 sentence) and summaries (3-10 sentences). Given any video selected from the dataset and its corresponding ASR information, we evaluate visuallanguage models on either caption or summary generation that is grounded in both the visual and auditory content of the video. Additionally, models are also evaluated on caption- and summary-based retrieval tasks, where the summary-based retrieval task requires the identification of a target video given excerpts of a given summary. Given the novel nature of the paragraph-length video summarization task, we compared different existing evaluation metrics and their alignment with human preferences and found that model-based evaluation metrics provide more semantically-oriented and human-aligned evaluation. Finally, we benchmarked a wide range of current video-language models on DeVAn, and we aim for DeVAn to serve as a useful evaluation set in the age of large language models and complex multi-modal tasks. Code is available at https: //github.com/TK-21st/DeVAn.