arXiv:2411.02851v1 Announce Type: new
Abstract: The goal of Multilingual Visual Answer Localization (MVAL) is to locate a video segment that answers a given multilingual question. Existing methods either focus solely on visual modality or integrate visual and subtitle modalities. However, these methods neglect the audio modality in videos, consequently leading to incomplete input information and poor performance in the MVAL task. In this paper, we propose a unified Audio-Visual-Textual Span Localization (AVTSL) method that incorporates audio modality to augment both visual and textual representations for the MVAL task. Specifically, we integrate features from three modalities and develop three predictors, each tailored to the unique contributions of the fused modalities: an audio-visual predictor, a visual predictor, and a textual predictor. Each predictor generates predictions based on its respective modality. To maintain consistency across the predicted results, we introduce an Audio-Visual-Textual Consistency module. This module utilizes a Dynamic Triangular Loss (DTL) function, allowing each modality’s predictor to dynamically learn from the others. This collaborative learning ensures that the model generates consistent and comprehensive answers. Extensive experiments show that our proposed method outperforms several state-of-the-art (SOTA) methods, which demonstrates the effectiveness of the audio modality.
Expert Commentary: Incorporating Audio Modality for Multilingual Visual Answer Localization
The Multilingual Visual Answer Localization (MVAL) task aims to identify a specific segment of a video that answers a given multilingual question. While previous methods have primarily focused on visual and textual modalities, the role of audio modality in videos has often been neglected. This paper introduces the Audio-Visual-Textual Span Localization (AVTSL) method, which integrates audio modality alongside visual and textual representations to enhance the performance of the MVAL task.
The AVTSL method takes advantage of the multi-disciplinary nature of multimedia information systems, specifically in the context of animations, artificial reality, augmented reality, and virtual realities. By incorporating features from three modalities, the proposed method provides a comprehensive understanding of the video content and improves the accuracy of the localization task.
One of the key contributions of this paper is the development of three predictors, each tailored to a specific modality. The audio-visual predictor utilizes both visual and audio features, the visual predictor focuses solely on visual features, and the textual predictor leverages textual representations. This multi-modal approach allows each predictor to capture the unique contributions of their respective modalities, resulting in more accurate predictions.
To ensure consistency across the predicted results, the AVTSL method introduces an Audio-Visual-Textual Consistency module. This module incorporates a Dynamic Triangular Loss (DTL) function, enabling collaborative learning between the predictors. By dynamically learning from each other, the predictors generate consistent and comprehensive answers. This is particularly important in the MVAL task, where the integration of multiple modalities is essential for accurate localization.
Extensive experiments have been conducted to evaluate the performance of the proposed AVTSL method. The results demonstrate that the inclusion of the audio modality significantly improves the performance compared to several state-of-the-art methods. This highlights the importance of considering audio information in addition to visual and textual data for the MVAL task.
In conclusion, the AVTSL method presented in this paper showcases the potential of incorporating audio modality for enhancing the accuracy of the Multilingual Visual Answer Localization task. By leveraging features from multiple modalities and employing collaborative learning, this method provides more comprehensive and consistent answers. The multi-disciplinary nature of this approach aligns with the wider field of multimedia information systems and its applications in animations, artificial reality, augmented reality, and virtual realities.