Transferable Representation Learning in Vision-and-Language Navigation

By Haoshuo Huang, Vihan Jain, Harsh Mehta, Alexander Ku, Gabriel Ilharco, Jason Baldridge, Eugene Ie. 2019

95 citations recorded by the source corpus.

Vision-and-Language Navigation (VLN) tasks such as Room-to-Room (R2R) require machine agents to interpret natural language instructions and learn to act in visually realistic environments to achieve navigation goals. The overall task requires competence in several perception problems: successful agents combine spatio-temporal, vision and language understanding to produce appropriate action sequences. Our approach adapts pre-trained vision and language representations to relevant in-domain tasks making them more effective for VLN. Specifically, the representations are adapted to solve both a cross-modal sequence alignment and sequence coherence task. In the sequence alignment task, the model determines whether an instruction corresponds to a sequence of visual frames. In the sequence coherence task, the model determines whether the perceptual sequences are predictive sequentially in the instruction-conditioned latent space. By transferring the domain-adapted representations, we improve competitive agents in R2R as measured by the success rate weighted by path length (SPL) metric.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Multimodal Learning

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Citations in the corpus

On the Evaluation of Vision-and-Language Navigation Instructions (2021, methodology): “Such data augmentation is so effective that it is nearly ubiquitous in the best performing agents (Wang et al., 2019; Huang et al., 2019; Li et al., 2019).”
Room-Across-Room: Multilingual Vision-and-Language Navigation with Dense Spatiotemporal Grounding (2020, background): “, 2019), pre-training (Wang et al., 2019; Huang et al., 2019; Li et al., 2019), multi-task learning (Wang et al.”
From Seeing to Moving: A Survey on Learning for Visual Indoor Navigation (VIN) (2020, methodology): “The task has been extensively studied in [Wang et al., 2018; Fried et al., 2018; Tan et al., 2019; Huang et al., 2019; Wang et al., 2019; Hong et al., 2021; Chen et al., 2021].”
Room-and-Object Aware Knowledge Reasoning for Remote Embodied Referring Expression (2021, methodology): “Numerous methods [47, 48, 19, 7, 12, 10, 46, 36, 17, 20] have been proposed to address the VLN task.”
Episodic Transformer for Vision-and-Language Navigation (2021, background): “In addition, auxiliary tasks, such as progress estimation [39, 40], backtracking [30], speaker-driven route selection [20], crossmodal matching [29, 68], back translation [60], pretraining on subtasks [74], and text-based pretraining [14,57] are proposed to improve the performance and generalization”
Counterfactual Vision-and-Language Navigation via Adversarial Path Sampling (2019, background): “Vision-and-Language Navigation Navigation in 3D environments based on natural language instruction has recently been investigated by many studies [3, 8, 21, 22, 26, 20, 33, 25, 11, 32, 31, 16].”
Improving Cross-Modal Alignment in Vision Language Navigation via Syntactic Information (2021, background): “…Navigation task with better interactions between natural language instructions and visual scenes (Fried et al., 2018; Wang et al., 2019; Landi et al., 2019; Wang et al., 2020a; Huang et al., 2019a; Hu et al., 2019; Majumdar et al., 2020; Ma et al., 2019a; Qi et al., 2020b; Zhu et al., 2020a,c).”
VALAN: Vision and Language Agent Navigation (2019, methodology): “The VALAN also comes prepackaged with popular methods proposed for navigation on R2R dataset such as pretraining on auxiliary tasks [11].”