{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/vln-petl-parameter-efficient-transfer","title":"VLN-PETL: Parameter-Efficient Transfer Learning for Vision-and-Language Navigation","arxiv_id":"2308.10172","date":"2023-08-20","proceeding":"ICCV 2023 1","authors":["Yanyuan Qiao","Zheng Yu","Qi Wu"],"abstract":"The performance of the Vision-and-Language Navigation~(VLN) tasks has witnessed rapid progress recently thanks to the use of large pre-trained vision-and-language models. However, full fine-tuning the pre-trained model for every downstream VLN task is becoming costly due to the considerable model size. Recent research hotspot of Parameter-Efficient Transfer Learning (PETL) shows great potential in efficiently tuning large pre-trained models for the common CV and NLP tasks, which exploits the most of the representation knowledge implied in the pre-trained model while only tunes a minimal set of parameters. However, simply utilizing existing PETL methods for the more challenging VLN tasks may bring non-trivial degeneration to the performance. Therefore, we present the first study to explore PETL methods for VLN tasks and propose a VLN-specific PETL method named VLN-PETL. Specifically, we design two PETL modules: Historical Interaction Booster (HIB) and Cross-modal Interaction Booster (CIB). Then we combine these two modules with several existing PETL methods as the integrated VLN-PETL. Extensive experimental results on four mainstream VLN tasks (R2R, REVERIE, NDH, RxR) demonstrate the effectiveness of our proposed VLN-PETL, where VLN-PETL achieves comparable or even better performance to full fine-tuning and outperforms other PETL methods with promising margins.","url_abs":"https://arxiv.org/abs/2308.10172v1","url_pdf":"https://arxiv.org/pdf/2308.10172v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"vln-petl-parameter-efficient-transfer","repo_url":"https://github.com/yanyuanqiao/vln-petl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"transfer-learning","task_name":"Transfer Learning"},{"task_slug":"vision-and-language-navigation","task_name":"Vision and Language Navigation"},{"task_slug":"visual-navigation","task_name":"Visual Navigation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/visual-navigation-on-cooperative-vision-and-1","task":"Visual Navigation","dataset":"Cooperative Vision-and-Dialogue Navigation","model":"VLN-PETL","rank_in_archive_order":2,"of":19,"metrics":{"dist_to_end_reduction":"6.13","spl":"0.07"},"uses_additional_data":false},{"leaderboard":"/sota/visual-navigation-on-room-to-room-1","task":"Visual Navigation","dataset":"R2R","model":"VLN-PETL","rank_in_archive_order":6,"of":11,"metrics":{"spl":"0.58"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2308.10172","atlas_url":"https://app.syntology.ai/?focus=2308.10172","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}