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The Cycle-Temporal Attention Network (CTAN) is presented, a framework designed for active semantic-enhanced fusion (rather than straightforward multimodal combination) and achieves superior performance over previous audio-visual navigation methods in terms of success rate, success weighted by path length, and scene nav...
The Transformer-based Token Fusion and Dynamic Graph Planning (TDGP) model is proposed, which incorporates high-level perception layers and leverages the Transformer model to fuse multimodal cues for precise local planning.
An LLM-driven hierarchical room-to-object (HRO) framework for zero-shot object-goal navigation, which guides the agent to explore and navigate to the target object in a coarse-to-fine manner, underscoring LLMs'strong potential for zero-shot object-goal navigation.
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