1. Introduction
Audio-guided image segmentation is a crucial task in multimodal perception, enabling fine-grained object delineation informed by acoustic cues, yet existing approaches often struggle with effectively integrating diverse modalities. Current methods may not fully exploit the synergistic relationship between audio and visual data, leading to suboptimal performance in complex scenes. This work aims to investigate and compare novel architectural designs to overcome these limitations and achieve more robust segmentation. Models used in this article include Transformer-based architectures for feature extraction, Convolutional Neural Networks (CNNs) for visual encoding, and cross-modal attention mechanisms for fusion.
2. Related Work
Prior research in audio-visual understanding has explored various fusion strategies, from early concatenation to complex attention mechanisms, across tasks like event localization and object recognition. Image segmentation, a well-established field, has seen advancements with fully convolutional networks and transformer models, but its audio-guided variant remains a nascent area. Works combining audio and vision often focus on classification or detection, making dedicated segmentation approaches less explored. Our study builds upon these foundations by specifically addressing the architectural choices for efficient multimodal fusion in segmentation.
3. Methodology
We propose two primary architectural paradigms: the 'Layover' approach and the 'Direct Flight' approach. The 'Layover' strategy involves an initial processing of each modality independently, followed by a late fusion stage that leverages cross-attention to combine high-level features. Conversely, the 'Direct Flight' strategy implements an early and continuously integrated fusion mechanism, allowing audio and visual information to interact at multiple hierarchical levels from the outset. Both paradigms utilize a shared backbone for feature extraction and a segmentation head for pixel-wise prediction, differing fundamentally in their inter-modal communication architecture.
4. Experimental Results
Our experimental evaluation on a comprehensive dataset demonstrates significant performance differences between the proposed paradigms. The 'Direct Flight' approach consistently outperforms the 'Layover' strategy across key segmentation metrics, indicating its superior ability to leverage multimodal information. Specifically, it achieves higher mean Intersection over Union (mIoU) and pixel accuracy, alongside improved inference efficiency. These results highlight the importance of deeply integrated multimodal fusion for achieving state-of-the-art audio-guided image segmentation performance. The table below summarizes the key performance metrics across the two proposed architectures.
5. Discussion
The superior performance of the 'Direct Flight' paradigm can be attributed to its ability to enable richer and more consistent interactions between audio and visual features throughout the network, leading to a more holistic understanding of the scene. This early and pervasive fusion avoids the information bottleneck that can arise from processing modalities separately before a late combination. The implications of these findings suggest that deeply intertwined multimodal architectures are crucial for complex perception tasks where modalities are highly complementary. Future work could explore adaptive fusion mechanisms and the scalability of these approaches to even more diverse datasets.