arXiv:2409.06709v1 Announce Type: new
Abstract: Audio-Visual Source Localization (AVSL) aims to localize the source of sound within a video. In this paper, we identify a significant issue in existing benchmarks: the sounding objects are often easily recognized based solely on visual cues, which we refer to as visual bias. Such biases hinder these benchmarks from effectively evaluating AVSL models. To further validate our hypothesis regarding visual biases, we examine two representative AVSL benchmarks, VGG-SS and EpicSounding-Object, where the vision-only models outperform all audiovisual baselines. Our findings suggest that existing AVSL benchmarks need further refinement to facilitate audio-visual learning.
Audio-Visual Source Localization: Challenges and Opportunities
Audio-Visual Source Localization (AVSL) is an emerging field that aims to accurately determine the location of sound sources within a video. This has several applications in multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. AVSL has the potential to enhance the user experience in these domains by providing more immersive and interactive audiovisual content.
In this paper, the authors identify a significant issue in existing AVSL benchmarks – visual bias. They point out that in many benchmarks, sounding objects can be easily recognized based solely on visual cues. This visual bias undermines the evaluation of AVSL models, as they don’t effectively capture the audio-visual learning capabilities. To demonstrate this, the authors analyze two representative AVSL benchmarks, VGG-SS and EpicSounding-Object, where vision-only models outperform all audiovisual baselines.
This research highlights the need for refinement in existing AVSL benchmarks to promote accurate audio-visual learning. It emphasizes the multi-disciplinary nature of AVSL, requiring the integration of computer vision and audio processing techniques. By tackling the issue of visual bias, researchers can develop more robust AVSL models that are capable of accurately localizing sound sources in videos.
In the wider field of multimedia information systems, AVSL has the potential to revolutionize the way we interact with audiovisual content. By accurately localizing sound sources, multimedia systems can provide a more immersive experience by adapting the audio output based on the user’s perspective and position relative to the source. This can greatly enhance virtual reality and augmented reality applications by creating a more realistic and interactive audiovisual environment.
Moreover, AVSL can contribute to the advancement of animations and artificial reality. By accurately localizing sound sources, animators can synchronize audio and visual elements more precisely, resulting in a more immersive and engaging animated experience. In artificial reality applications, AVSL can add another layer of realism by accurately reproducing spatial audio cues, making artificial environments indistinguishable from real ones.
Overall, the identification of visual bias in existing AVSL benchmarks underscores the importance of refining these benchmarks to promote accurate audio-visual learning. This research highlights the interdisciplinary nature of AVSL and its applications in multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. By addressing these challenges, researchers can unlock the full potential of AVSL and revolutionize the way we perceive and interact with audiovisual content.