MAD: A Scalable Dataset for Language Grounding in Videos from Movie Audio Descriptions (2112.00431v2)

Abstract: The recent and increasing interest in video-language research has driven the development of large-scale datasets that enable data-intensive machine learning techniques. In comparison, limited effort has been made at assessing the fitness of these datasets for the video-language grounding task. Recent works have begun to discover significant limitations in these datasets, suggesting that state-of-the-art techniques commonly overfit to hidden dataset biases. In this work, we present MAD (Movie Audio Descriptions), a novel benchmark that departs from the paradigm of augmenting existing video datasets with text annotations and focuses on crawling and aligning available audio descriptions of mainstream movies. MAD contains over 384,000 natural language sentences grounded in over 1,200 hours of videos and exhibits a significant reduction in the currently diagnosed biases for video-language grounding datasets. MAD's collection strategy enables a novel and more challenging version of video-language grounding, where short temporal moments (typically seconds long) must be accurately grounded in diverse long-form videos that can last up to three hours. We have released MAD's data and baselines code at https://github.com/Soldelli/MAD.

• The paper introduces the MAD dataset, a benchmark with 384K+ sentences from 1,200 hours of movie videos, mitigating biases in video-language grounding tasks.
• It leverages natural audio descriptions to link visual content with language, offering robust evaluation for long-form video understanding.
• Baseline evaluations reveal that state-of-the-art models struggle with MAD’s temporal complexity, highlighting the need for improved grounding techniques.

Analyzing Video-Language Grounding Using the MAD Dataset

The paper "MAD: A Scalable Dataset for Language Grounding in Videos from Movie Audio Descriptions" introduces the Movie Audio Descriptions (MAD) dataset as a novel benchmark designed for video-language grounding tasks. This work addresses significant limitations in the existing datasets, such as hidden biases that lead to overfitting, by leveraging movie audio descriptions. It proposes a shift from augmenting pre-existing video datasets with textual annotations to utilizing audio descriptions that inherently describe visual content.

Dataset Composition and Novelty

MAD contains more than 384,000 natural language sentences grounded in over 1,200 hours of mainstream movie videos. This dataset stands out due to its scale, diversity, and the challenges associated with its tasks: grounding sentences in long-form videos. Unlike previous datasets, MAD has been curated to reduce biases despite being automatically generated, aiming to circumvent issues where models over-rely on dataset peculiarities rather than genuine video understanding.

The dataset incorporates extensive vocabulary diversity, with over 60,000 unique words, in comparison to alternative benchmarks. Significantly, each video is approximately 110 minutes long, and the vocabulary is vast, covering a wide range of diverse movie narratives. This vast scope allows the MAD dataset to introduce more complexity and real-world applicability to video-language grounding tasks.

Evaluation and Benchmarks

On the experimental front, the paper provides baseline results using state-of-the-art models like VLG-Net and CLIP. Notably, the results suggest that existing models struggle significantly when applied to MAD’s long-form video tasks. While the CLIP model, pre-trained for text-to-image retrieval, surpasses VLG-Net in some short video setups, the latter performs better on stricter localization metrics. This indicates potential shortcomings in current models’ capabilities to handle non-trivial temporal dynamics in extended video data.

Theoretical Contributions and Future Directions

The paper underscores the importance of mitigating dataset biases, proposing audio descriptions as a means to develop a more reliable evaluation framework for video-language grounding. This proposition presents rich avenues for further research, pushing the boundaries towards cost-effective and scalable dataset assembly methods.

Additionally, the integration of professional audio descriptions, which are inherently crafted to describe essential visual details for visually impaired audiences, facilitates a more nuanced and contextually relevant language grounding approach. The future for improving video-LLMs may hinge on leveraging such real-world data, compelling researchers to investigate methods that effectively utilize these extensive, naturally occurring descriptive datasets.

Implications for AI Development

From a broader perspective, MAD’s contributions suggest a transformative shift in understanding video-language grounding, with broad implications for AI's capabilities in multimedia understanding, content retrieval, and accessibility applications. The dataset's richness and scale highlight a path forward where AI systems can be made more adept with less curated, real-world data, advancing generalization and practical deployment.

In conclusion, MAD offers profound insights and methodological advancements for the community, paving the way for a more in-depth exploration of grounding in video content beyond traditional, constrained benchmarks. The focus on natural, context-dependent language inputs is poised to attract ongoing interest and drive sustained progress in AI's interpretative proficiencies.