Papers with R2R
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. |
LangNav: Language as a Perceptual Representation for Navigation (2024.findings-naacl)
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| Challenge: | Existing approaches to vision-and-language navigation use visual features as the perceptual representation of a visual representation of an agent's egocentric panoramic view. |
| Approach: | They propose to use off-the-shelf vision systems to convert an agent’s egocentric panoramic view into natural language descriptions. |
| Outcome: | The proposed approach improves on the R2R VLN benchmark by using synthetic trajectories from a prompted language model and domain transfer where a policy learned on one simulated environment (ALFRED) is transferred to another (more realistic) environment and combining both vision- and language-based representations. |
Sub-Instruction Aware Vision-and-Language Navigation (2020.emnlp-main)
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| Challenge: | Despite significant advances, few previous works are able to fully utilize the strong correspondence between visual and textual sequences. |
| Approach: | They propose to provide agents with fine-grained annotations during training and provide them with sub-instructions and their corresponding paths. |
| Outcome: | The proposed method improves the performance of four state-of-the-art agents in a room-to-room (R2R) benchmark dataset. |
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. |
LOViS: Learning Orientation and Visual Signals for Vision and Language Navigation (2022.coling-1)
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| Challenge: | Existing Transformer-based VLN agents entangle orientation and vision information, which limits the learning of each information source. |
| Approach: | They propose to design a navigation agent with explicit Orientation and Vision modules . they use a set of pre-training tasks to feed the modules into the model . |
| Outcome: | The proposed model improves on R2R and R4R datasets and achieves state-of-the-art results. |