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.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022 |
| Publisher | IEEE Computer Society |
| Pages | 5016-5025 |
| Number of pages | 10 |
| ISBN (Electronic) | 9781665469463 |
| DOIs | |
| State | Published - 2022 |
| Event | 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022 - New Orleans, United States Duration: Jun 19 2022 → Jun 24 2022 |
Publication series
| Name | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
|---|---|
| Volume | 2022-June |
| ISSN (Print) | 1063-6919 |
Conference
| Conference | 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022 |
|---|---|
| Country/Territory | United States |
| City | New Orleans |
| Period | 06/19/22 → 06/24/22 |
Bibliographical note
Funding Information:The paper presents a new video grounding benchmark called MAD, which builds on high-quality audio descriptions in movies. MAD alleviates the shortcomings of previous grounding datasets. Our automatic annotation pipeline allowed us to collect the largest grounding dataset to date. The experimental section provides baselines for the task solution and highlights the challenging nature of the long-form grounding task introduced by MAD. Our methodology comes with two main hypotheses and limitations: (i) Noise cannot be avoided but can be dealt with through scale. (ii) Due to copyright constraints, MAD’s videos will not be publicly released. However, we will provide all necessary features for our experiments’ reproducibility and promote future research in this direction. Acknowledgments. This work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research through the Visual Computing Center (VCC) funding.
Publisher Copyright:
© 2022 IEEE.
Keywords
- Datasets and evaluation
- Video analysis and understanding
- Vision + language
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition