arXiv:2505.02096v1 Announce Type: new
Abstract: Audio-Visual Video Parsing (AVVP) task aims to parse the event categories and occurrence times from audio and visual modalities in a given video. Existing methods usually focus on implicitly modeling audio and visual features through weak labels, without mining semantic relationships for different modalities and explicit modeling of event temporal dependencies. This makes it difficult for the model to accurately parse event information for each segment under weak supervision, especially when high similarity between segmental modal features leads to ambiguous event boundaries. Hence, we propose a multimodal optimization framework, TeMTG, that combines text enhancement and multi-hop temporal graph modeling. Specifically, we leverage pre-trained multimodal models to generate modality-specific text embeddings, and fuse them with audio-visual features to enhance the semantic representation of these features. In addition, we introduce a multi-hop temporal graph neural network, which explicitly models the local temporal relationships between segments, capturing the temporal continuity of both short-term and long-range events. Experimental results demonstrate that our proposed method achieves state-of-the-art (SOTA) performance in multiple key indicators in the LLP dataset.
Expert Commentary: The Multidisciplinary Nature of Audio-Visual Video Parsing
In the realm of multimedia information systems, the task of Audio-Visual Video Parsing (AVVP) stands out as a prime example of a multidisciplinary challenge that combines concepts from computer vision, natural language processing, and audio analysis. The goal of AVVP is to extract event categories and occurrence times from both audio and visual modalities in a given video, requiring a deep understanding of how these modalities interact and complement each other.
Relation to Multimedia Technologies
When we look at the proposed multimodal optimization framework, TeMTG, we can see how it leverages pre-trained multimodal models to generate modality-specific text embeddings and fuse them with audio-visual features. This integration of text analysis with audio-visual processing demonstrates the interconnected nature of multimedia technologies, where different disciplines converge to tackle complex problems.
Artificial Reality and Multimedia Integration
As we delve deeper into the concept of AVVP, we can also draw parallels to the fields of Artificial Reality, Augmented Reality, and Virtual Realities. These immersive technologies heavily rely on audio-visual inputs to create realistic and engaging experiences for users. By improving the accuracy of parsing event information from audio and visual modalities, advancements in AVVP can potentially enhance the realism and interactivity of artificial environments.
Potential Future Developments
Looking ahead, the proposed TeMTG framework represents a significant step towards addressing the challenges of weak supervision and ambiguous event boundaries in AVVP. By explicitly modeling temporal relationships between segments through a multi-hop temporal graph neural network, the method showcases the importance of capturing both short-term and long-range events for accurate parsing.
Overall, the interdisciplinary nature of AVVP and its connections to multimedia information systems, animations, artificial reality, and virtual realities highlight the complex yet fascinating landscape of modern multimedia technologies. As researchers continue to push the boundaries of understanding audio-visual interactions, we can expect further innovations that blur the lines between different disciplines and pave the way for more immersive and intelligent multimedia systems.