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Learning Disentanglement with Decoupled Labels for Vision-Language Navigation

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13696))

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Abstract

Vision-and-Language Navigation (VLN) requires an agent to follow complex natural language instructions and perceive the visual environment for real-world navigation. Intuitively, we find that instruction disentanglement for each viewpoint along the agent’s path is critical for accurate navigation. However, most methods only utilize the whole complex instruction or inaccurate sub-instructions due to the lack of accurate disentanglement as an intermediate supervision stage. To address this problem, we propose a new Disentanglement framework with Decoupled Labels (DDL) for VLN. Firstly, we manually extend the benchmark dataset Room-to-Room with landmark- and action-aware labels in order to provide fine-grained information for each viewpoint. Furthermore, to enhance the generalization ability, we propose a Decoupled Label Speaker module to generate pseudo-labels for augmented data and reinforcement training. To fully use the proposed fine-grained labels, we design a Disentangled Decoding Module to guide discriminative feature extraction and help alignment of multi-modalities. To reveal the generality of our proposed method, we apply it on a LSTM-based model and two recent Transformer-based models. Extensive experiments on two VLN benchmarks (i.e., R2R and R4R) demonstrate the effectiveness of our approach, achieving better performance than previous state-of-the-art methods.

W. Cheng and X. Dong—Equal contribution. Codes and annotations are available at https://github.com/cwhao98/DDL.

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Acknowledgements

This work was supported partly by the Start-up Research Grant (SRG) of University of Macau. We thank the reviewers for their insightful comments.

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Authors and Affiliations

  1. School of Computer Science, Beijing Institute of Technology, Beijing, China

    Wenhao Cheng

  2. Inception Institute of Artificial Intelligence, Abu Dhabi, UAE

    Xingping Dong

  3. Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE

    Salman Khan

  4. SKL-IOTSC, Computer and Information Science, University of Macau, Macau, China

    Jianbing Shen

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  1. Wenhao Cheng
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  2. Xingping Dong
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  4. Jianbing Shen
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Correspondence to Jianbing Shen .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Cheng, W., Dong, X., Khan, S., Shen, J. (2022). Learning Disentanglement with Decoupled Labels for Vision-Language Navigation. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13696. Springer, Cham. https://doi.org/10.1007/978-3-031-20059-5_18

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