Papers with RxR
Navigation as Attackers Wish? Towards Building Robust Embodied Agents under Federated Learning (2024.naacl-long)
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| Challenge: | Towards Byzantine-robust federated embodied agent learning, we study the attack and defense for the task of vision-and-language navigation (VLN) |
| Approach: | They propose a new method to defend against a navigation-and-language navigation attack using navigation as wish (NAW) the method provides the server with a 'prompt' of the vision-and language alignment variance between benign and malicious clients so they can be distinguished during training. |
| Outcome: | The proposed method outperforms other state-of-the-art defense methods on two VLN datasets. |
Room-Across-Room: Multilingual Vision-and-Language Navigation with Dense Spatiotemporal Grounding (2020.emnlp-main)
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| Challenge: | Room-Across-Room (RxR) is a vision-and-language navigation dataset that addresses gaps in existing ones by addressing known biases in paths and eliciting more references to visible entities. |
| Approach: | They introduce a new Vision-and-Language Navigation (VLN) dataset that addresses biases in paths and elicits more references to visible entities. |
| Outcome: | The proposed model learns from synchronized pose traces by focusing only on portions of the panorama attended to in human demonstrations. |
DELAN: Dual-Level Alignment for Vision-and-Language Navigation by Cross-Modal Contrastive Learning (2024.lrec-main)
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| Challenge: | Existing studies focus on cross-modal attention at the fusion stage, but modality features generated by disparate uni-encoders reside in their own spaces, leading to a decline in the quality of cross-modulation and decision-making. |
| Approach: | They propose a framework to align navigation-related modalities before fusion by cross-modal contrastive learning. |
| Outcome: | The proposed framework integrates with the majority of existing models, resulting in improved navigation performance on various VLN benchmarks, including R2R, R4R, and CVDN. |