arXiv:2412.10749v1 Announce Type: new
Abstract: Answering questions related to audio-visual scenes, i.e., the AVQA task, is becoming increasingly popular. A critical challenge is accurately identifying and tracking sounding objects related to the question along the timeline. In this paper, we present a new Patch-level Sounding Object Tracking (PSOT) method. It begins with a Motion-driven Key Patch Tracking (M-KPT) module, which relies on visual motion information to identify salient visual patches with significant movements that are more likely to relate to sounding objects and questions. We measure the patch-wise motion intensity map between neighboring video frames and utilize it to construct and guide a motion-driven graph network. Meanwhile, we design a Sound-driven KPT (S-KPT) module to explicitly track sounding patches. This module also involves a graph network, with the adjacency matrix regularized by the audio-visual correspondence map. The M-KPT and S-KPT modules are performed in parallel for each temporal segment, allowing balanced tracking of salient and sounding objects. Based on the tracked patches, we further propose a Question-driven KPT (Q-KPT) module to retain patches highly relevant to the question, ensuring the model focuses on the most informative clues. The audio-visual-question features are updated during the processing of these modules, which are then aggregated for final answer prediction. Extensive experiments on standard datasets demonstrate the effectiveness of our method, achieving competitive performance even compared to recent large-scale pretraining-based approaches.
Analysis: Patch-level Sounding Object Tracking for AVQA
The AVQA task, which involves answering questions related to audio-visual scenes, has gained popularity in recent years. However, accurately identifying and tracking sounding objects along the timeline has been a critical challenge. In this paper, the authors propose a Patch-level Sounding Object Tracking (PSOT) method to tackle this problem.
The PSOT method consists of three modules: Motion-driven Key Patch Tracking (M-KPT), Sound-driven KPT (S-KPT), and Question-driven KPT (Q-KPT). Each module contributes to the overall goal of accurately tracking and identifying relevant objects for answering questions.
The M-KPT module utilizes visual motion information to identify salient visual patches with significant movements. This helps in determining which patches are more likely to be related to sounding objects and questions. The motion intensity map between neighboring video frames is used to construct and guide a motion-driven graph network. This module aims to balance the tracking of salient objects and sounding objects.
The S-KPT module, on the other hand, explicitly tracks sounding patches by incorporating audio-visual correspondence. It uses a graph network with an adjacency matrix regularized by the audio-visual correspondence map. This module focuses on tracking patches that are specifically related to sound, ensuring that the model captures important audio cues.
Both the M-KPT and S-KPT modules are performed in parallel for each temporal segment, allowing for simultaneous tracking of salient objects and sounding objects. This ensures that relevant information from both visual and audio modalities is captured.
The Q-KPT module plays a crucial role in retaining patches that are highly relevant to the given question. It ensures that the model focuses on the most informative clues for answering the question. By updating the audio-visual-question features during the processing of these modules, the model can aggregate the information for final answer prediction.
The proposed PSOT method is evaluated on standard datasets and demonstrates competitive performance compared to recent large-scale pretraining-based approaches. This highlights the effectiveness of the method in accurately tracking sounding objects for answering audio-visual scene-related questions.
Multi-disciplinary Nature and Relations to Multimedia Information Systems
The PSOT method presented in this paper encompasses various disciplines, making it a multi-disciplinary research work. It combines computer vision techniques, audio processing, and natural language processing to address the challenges in the AVQA task.
In the field of multimedia information systems, the PSOT method contributes to the development of techniques for analyzing and understanding audio-visual content. By effectively tracking and identifying sounding objects, the method enhances the ability to extract meaningful information from audio-visual scenes. This can have applications in content-based retrieval, video summarization, and automated scene understanding.
Relations to Animations, Artificial Reality, Augmented Reality, and Virtual Realities
The PSOT method is directly related to the fields of animations, artificial reality, augmented reality, and virtual realities. By accurately tracking sounding objects in audio-visual scenes, the method can improve the realism and immersion of animated content, virtual reality experiences, and augmented reality applications.
In animations, the PSOT method can aid in generating realistic sound interactions by accurately tracking and synchronizing sounding objects with the animated visuals. This can contribute to the overall quality and believability of animated content.
In artificial reality, such as virtual reality and augmented reality, the PSOT method can enhance the audio-visual experience by ensuring that virtual or augmented objects produce realistic sounds when interacted with. This can create a more immersive and engaging user experience in virtual or augmented environments.
Overall, the PSOT method presented in this paper has implications for a range of disciplines, including multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. Its contribution to accurately tracking sounding objects in audio-visual scenes has the potential to advance research in these fields and improve various applications and experiences related to audio-visual content.