Iterative Residual Cross-Attention Mechanism: An Integrated Approach for Audio-Visual Navigation Tasks

2025-09-29

AI Summary

Traditional audio-visual navigation (AVN) suffers from information redundancy and temporal misalignment due to the decoupled design of multimodal fusion and sequential modeling. To address this, we propose IRCAM-AVN, an end-to-end framework centered on the Iterative Residual Cross-Attention Mechanism (IRCAM). IRCAM unifies cross-modal feature alignment, temporal modeling, and residual information flow within a single module, enhanced by multi-level residual connections to improve training stability and generalization. The model learns joint audio-visual representations via cross-attention and is optimized end-to-end using reinforcement learning. Evaluated on standard AVN benchmarks, IRCAM-AVN significantly outperforms staged approaches, achieving substantial gains in navigation success rate. Moreover, it demonstrates superior robustness to auditory noise and environmental variations, along with enhanced generalization across unseen scenes and acoustic conditions.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Audio-visual navigation represents a significant area of research in which intelligent agents utilize egocentric visual and auditory perceptions to identify audio targets. Conventional navigation methodologies typically adopt a staged modular design, which involves first executing feature fusion, then utilizing Gated Recurrent Unit (GRU) modules for sequence modeling, and finally making decisions through reinforcement learning. While this modular approach has demonstrated effectiveness, it may also lead to redundant information processing and inconsistencies in information transmission between the various modules during the feature fusion and GRU sequence modeling phases. This paper presents IRCAM-AVN (Iterative Residual Cross-Attention Mechanism for Audiovisual Navigation), an end-to-end framework that integrates multimodal information fusion and sequence modeling within a unified IRCAM module, thereby replacing the traditional separate components for fusion and GRU. This innovative mechanism employs a multi-level residual design that concatenates initial multimodal sequences with processed information sequences. This methodological shift progressively optimizes the feature extraction process while reducing model bias and enhancing the model's stability and generalization capabilities. Empirical results indicate that intelligent agents employing the iterative residual cross-attention mechanism exhibit superior navigation performance.

Problem

• Integrates multimodal fusion and sequence modeling in unified framework
• Reduces redundant processing in audio-visual navigation systems
• Enhances agent navigation performance through iterative residual design

Innovation

• Integrated end-to-end framework for multimodal fusion
• Iterative residual cross-attention mechanism design
• Unified module replacing separate fusion and GRU components