# CTAN: Cycle-Temporal Attention Network for Embodied Audio-Visual Navigation

arXiv: https://arxiv.org/abs/2609.17420 · HTML: https://mlsanity.com/paper/2609.17420/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 24 references that are in the map.
- First posted on arXiv 25 Jul 2026.

## Builds on (most cited first, 21 of 21)

- [Matterport3D: Learning from RGB-D Data in Indoor Environments](https://mlsanity.com/paper/1709.06158/) (2017)
- [The Replica Dataset: A Digital Replica of Indoor Spaces](https://mlsanity.com/paper/1906.05797/) (2019)
- [Cycle-Consistency for Robust Visual Question Answering](https://mlsanity.com/paper/1902.05660/) (2019)
- [Look, Listen, and Act: Towards Audio-Visual Embodied Navigation](https://mlsanity.com/paper/1912.11684/) (2019)
- [Semantic Audio-Visual Navigation](https://mlsanity.com/paper/2012.11583/) (2020)
- [Sound Adversarial Audio-Visual Navigation](https://mlsanity.com/paper/2202.10910/) (2022)
- [Catch Me If You Hear Me: Audio-Visual Navigation in Complex Unmapped Environments with Moving Sounds](https://mlsanity.com/paper/2111.14843/) (2021)
- [AVLEN: Audio-Visual-Language Embodied Navigation in 3D Environments](https://mlsanity.com/paper/2210.07940/) (2022)
- [Measuring Acoustics with Collaborative Multiple Agents](https://mlsanity.com/paper/2310.05368/) (2023)
- [Omnidirectional Information Gathering for Knowledge Transfer-based Audio-Visual Navigation](https://mlsanity.com/paper/2308.10306/) (2023)
- [Audio-Guided Dynamic Modality Fusion with Stereo-Aware Attention for Audio-Visual Navigation](https://mlsanity.com/paper/2509.16924/) (2025)
- [CAVEN: An Embodied Conversational Agent for Efficient Audio-Visual Navigation in Noisy Environments](https://mlsanity.com/paper/2306.04047/) (2023)
- [DOPE: Dual Object Perception-Enhancement Network for Vision-and-Language Navigation](https://mlsanity.com/paper/2505.00743/) (2025)
- [Advancing Audio-Visual Navigation Through Multi-Agent Collaboration in 3D Environments](https://mlsanity.com/paper/2509.22698/) (2025)
- [Iterative Residual Cross-Attention Mechanism: An Integrated Approach for Audio-Visual Navigation Tasks](https://mlsanity.com/paper/2509.25652/) (2025)
- [DGFNet: End-to-End Audio-Visual Source Separation Based on Dynamic Gating Fusion](https://mlsanity.com/paper/2504.21366/) (2025)
- [Dynamic Multi-Target Fusion for Efficient Audio-Visual Navigation](https://mlsanity.com/paper/2509.21377/) (2025)
- [BSS-CFFMA: Cross-Domain Feature Fusion and Multi-Attention Speech Enhancement Network based on Self-Supervised Embedding](https://mlsanity.com/paper/2408.06851/) (2024)
- [VNet: A GAN-based Multi-Tier Discriminator Network for Speech Synthesis Vocoders](https://mlsanity.com/paper/2408.06906/) (2024)
- [Modality-Invariant Bidirectional Temporal Representation Distillation Network for Missing Multimodal Sentiment Analysis](https://mlsanity.com/paper/2501.05474/) (2025)
- [FSDENet: A Frequency and Spatial Domains based Detail Enhancement Network for Remote Sensing Semantic Segmentation](https://mlsanity.com/paper/2510.00059/) (2025)

## Abstract (arXiv)

Audio-visual embodied navigation equips robots with the capability to infer the locations of sound sources by integrating visual inputs and acoustic information (e.g., depth observations and binaural audio cues). The core challenge lies in establishing effective semantic interactions across heterogeneous modalities (which exhibit distinct feature distributions). Existing feature fusion strategies, however, often rely on simple multimodal aggregation and therefore fail to capture the underlying geometric and semantic relationships, leading to information degradation in complex environments. To overcome these limitations, this work presents the Cycle-Temporal Attention Network (CTAN), a framework designed for active semantic-enhanced fusion (rather than straightforward multimodal combination). Specifically, the proposed Audio-Visual Reconstruction Cross-Attention (AVRCA) module employs a bidirectional cycle-consistency constraint (between visual and acoustic representations) to reinforce the spatial semantic attributes of both modalities, thereby facilitating more robust cross-modal interaction. Additionally, we design a Temporal Cross-Modal Memory (TCMM) mechanism to dynamically integrate real-time enhanced multimodal features with historical context, reducing performance drops caused by auditory dead zones. Experimental results obtained on the Replica and Matterport3D benchmarks indicate that the proposed approach achieves superior performance over previous audio-visual navigation methods in terms of success rate (SR), success weighted by path length (SPL), and scene navigation accuracy (SNA).