Spatial-Aware Conditioned Fusion for Audio-Visual Navigation
Summary: arXiv:2604.02390v1 Announce Type: cross
Abstract
Audio-visual navigation tasks require agents to locate and navigate toward continuously vocalizing targets using only visual observations and acoustic cues. However, existing methods mainly rely on simple feature concatenation or late fusion, and lack an explicit discrete representation of the target’s relative position, which limits learning efficiency and generalization. We propose Spatial-Aware Conditioned Fusion (SACF). SACF first discretizes the target’s relative direction and distance from audio-visual cues, predicts their distributions, and encodes them as a compact descriptor for policy conditioning and state modeling. Then, SACF uses audio embeddings and spatial descriptors to generate channel-wise scaling and bias to modulate visual features via conditional linear transformation, producing target-oriented fused representations. SACF improves navigation efficiency with lower computational overhead and generalizes well to unheard target sounds.
Introduction
Recent advancements in artificial intelligence have led to significant progress in audio-visual navigation systems. These systems aim to enhance the ability of agents to navigate their environments by utilizing both visual and audio data. Traditional methods, however, face challenges in effectively integrating these two modalities, leading to suboptimal performance in complex navigational tasks.
The Challenges
- Feature Integration: Existing approaches often use simplistic methods such as feature concatenation or late fusion, which do not leverage the full potential of audio-visual information.
- Lack of Discrete Representation: Without a clear representation of the target’s relative position, the models struggle with generalization and learning efficiency.
- Computational Overhead: Many current techniques require significant computational resources, making them less viable for real-time applications.
Introducing Spatial-Aware Conditioned Fusion (SACF)
SACF addresses these challenges by introducing a novel approach to audio-visual navigation. This method breaks down the target’s relative direction and distance into discrete categories, allowing for more efficient processing and representation. Below are the key components of SACF:
- Discretization: SACF begins by discretizing the target’s relative direction and distance based on the audio-visual cues available to the agent.
- Predictive Modeling: It predicts the distributions of these discretized values and encodes them into a compact descriptor, which is crucial for effective policy conditioning and state modeling.
- Channel-wise Modulation: Using the audio embeddings combined with spatial descriptors, SACF generates channel-wise scaling and bias, allowing visual features to be modulated through a conditional linear transformation.
Benefits of SACF
The implementation of SACF results in several advantages that enhance the performance of audio-visual navigation systems:
- Improved Navigation Efficiency: By using a more structured approach to integrate audio and visual information, SACF enhances the agent’s ability to navigate toward targets more effectively.
- Reduced Computational Load: The method’s design minimizes the computational overhead typically associated with audio-visual task processing.
- Generalization to Unheard Sounds: SACF demonstrates strong generalization capabilities, allowing it to perform well even with targets that have not been previously encountered or vocalized.
Conclusion
Spatial-Aware Conditioned Fusion represents a significant advancement in the field of audio-visual navigation. By addressing the limitations of previous methods and introducing a more structured and efficient approach, SACF holds the potential to improve agent performance in complex environments. Future research may explore further optimizations and applications of this innovative framework.