Tree-Structured Trajectory Encoding for Vision-and-Language Navigation
Authors: Xinzhe Zhou, Yadong Mu
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On the benchmark dataset R2R, our model achieves a surpassing success rate (SR) of 68% on val-unseen and 66% on test. We further conduct extensive ablation studies and analyses to provide more insights for the effectiveness our designs. |
| Researcher Affiliation | Academia | Xinzhe Zhou1, Yadong Mu* 1,2 1 Wangxuan Institute of Computer Technology, Peking University 2 Peng Cheng Laboratory {zhouxinzhe1023, myd}@pku.edu.cn |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. Descriptions of processes are given in paragraph form. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. There are no explicit statements about code release or repository links. |
| Open Datasets | Yes | Tab. 1 shows the results on the R2R (Anderson et al. 2018b) dataset. Besides R2R, We also tested our model on the more challenging Rx R (Ku et al. 2020) dataset. |
| Dataset Splits | Yes | about 10% samples in R2R val-unseen set involve at least one error-and-correction, despite the paths only take 4-6 steps. ... Tab. 1 shows the results on the R2R (Anderson et al. 2018b) dataset. As can be seen, our model achieves the best SR on the two unseen sets, especially test that is used by the online leaderboard, which demonstrates the effectiveness of our designs. The SR on val-seen is a bit lower than VLNHAMT (Chen et al. 2021) |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or detailed computer specifications) used for running its experiments. It discusses training models but without mentioning hardware. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. It mentions general components like 'LSTM' or 'transformer' but not specific software versions. |
| Experiment Setup | Yes | For implementation, we use a first-in-first-out queue of length k to conduct the filtering online. Throughout our experiments, we set k = 5 empirically. Later in experiments, we will show the detailed effect of varying the ks. |