Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A Dual Semantic-Aware Recurrent Global-Adaptive Network for Vision-and-Language Navigation
Authors: Liuyi Wang, Zongtao He, Jiagui Tang, Ronghao Dang, Naijia Wang, Chengju Liu, Qijun Chen
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on the R2R and REVERIE datasets demonstrate that our method achieves better performance than existing methods. |
| Researcher Affiliation | Academia | 1Tongji University, Shanghai, China 2Tongji Artificial Intelligence (Suzhou) Research Institute, Suzhou, China EMAIL |
| Pseudocode | No | The paper does not include any explicitly labeled pseudocode or algorithm blocks. Figures present architectural diagrams rather than structured code steps. |
| Open Source Code | Yes | Code is available at https: //github.com/Crystal Sixone/DSRG. |
| Open Datasets | Yes | To validate our proposed method, we conduct extensive experiments on the R2R [Anderson et al., 2018] and REVERIE datasets [Qi et al., 2020b]. |
| Dataset Splits | Yes | For R2R, four standard metrics are for evaluation: the navigation error (NE): the distance between the ground truth and the agent s stop position; the success rate (SR): the ratio of paths that stop within 3m from the target points; the oracle success rate (OSR): SR with the oracle stop policy; and the success rate weighted by the path length (SPL): SR penalized by the path length. For REVERIE, another two metrics are added: remote grounding success rate (RGS): the ratio of objects grounded correctly, and the RGS weighted by the path length (RGSPL). ... Table 1: Comparison with the state-of-the-art methods on the R2R dataset. (Includes "Validation Seen" and "Validation Unseen" columns). |
| Hardware Specification | Yes | In the pre-training stage, we train our DSRG with batch size 24 for 400k iterations using 1 NVIDIA RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions using a "BERT model" and "Vi T-B/16" for feature extraction, but does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow, specific library versions). |
| Experiment Setup | Yes | In the pre-training stage, we train our DSRG with batch size 24 for 400k iterations using 1 NVIDIA RTX 3090 GPU. ... During fine-tuning, the batch size and the learning rate are 4 and 5 × 10−6, respectively. ... The numbers of transformer layers for instructions, visual and semantic features, and local-global cross-modal attention modules are 9, 2 and 4, respectively. |