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..
VLN-Video: Utilizing Driving Videos for Outdoor Vision-and-Language Navigation
Authors: Jialu Li, Aishwarya Padmakumar, Gaurav Sukhatme, Mohit Bansal
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results demonstrate that VLN-VIDEO significantly outperforms previous state-of-the-art models by 2.1% in task completion rate, achieving a new state-of-the-art on the Touchdown dataset. |
| Researcher Affiliation | Collaboration | Jialu Li1,2*, Aishwarya Padmakumar2, Gaurav Sukhatme2, Mohit Bansal1 1University of North Carolina, Chapel Hill 2Amazon Alexa AI EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methods in text and provides an overview diagram (Figure 1), but it does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about releasing source code or a direct link to a code repository. |
| Open Datasets | Yes | We evaluate our agent on the Touchdown dataset (Chen et al. 2019). The Manh50 dataset (Zhu et al. 2021) we use during pre-training is extracted from the Street Learn dataset (Mirowski et al. 2019)... The driving videos we utilized during pre-training come from the BDD100K dataset (Chen et al. 2018). |
| Dataset Splits | Yes | Touchdown is set in Manhattan and contains 9,326 instruction-trajectory pairs, with 6,526 examples in the training set, 1,391 examples in the validation set, and 1,409 examples in the test set. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU model, CPU type, memory) used to run the experiments. |
| Software Dependencies | Yes | We utilize a Mask-RCNN model (He et al. 2017) from the Detectron2 (Wu et al. 2019) package pre-trained on the LVIS dataset (Gupta, Dollar, and Girshick 2019) to detect objects in video frames. ...we additionally compare to using pre-trained BERT-base embeddings (Devlin et al. 2018) for fair comparison. |
| Experiment Setup | No | The paper describes the pre-training tasks (Masked Language Modeling, Instruction and Trajectory Matching, and Next Action Prediction) and the fine-tuning process, but it does not provide specific hyperparameter values (e.g., learning rates, batch sizes, number of epochs) or detailed training configurations. |