CTAN: Cycle-Temporal Attention Network for Embodied Audio-Visual Navigation
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 fra...
| Authors | Teng Liu, Yinfeng Yu |
|---|---|
| Primary category | cs.MM |
| Published | 25 July 2026 |
| Last updated at source | 25 July 2026 |
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