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..
Augmented Commonsense Knowledge for Remote Object Grounding
Authors: Bahram Mohammadi, Yicong Hong, Yuankai Qi, Qi Wu, Shirui Pan, Javen Qinfeng Shi
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate our proposed model noticeably outperforms the baseline and archives the state-of-the-art on the REVERIE benchmark. |
| Researcher Affiliation | Academia | 1Australian Institute for Machine Learning (AIML), University of Adelaide 2Australian National University 3Macquarie University 4Griffith University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code is available at https://github.com/Bahram Mohammadi/ACK. |
| Open Datasets | Yes | Experimental results demonstrate our proposed model noticeably outperforms the baseline and archives the state-of-the-art on the REVERIE benchmark. The experiments are conducted on the REVERIE dataset and results show that our proposed approach, ACK, outperforms the state-of-the-art methods. |
| Dataset Splits | Yes | Validation Unseen Test Unseen Navigation Grounding (Table 1 header) and The ACK is merely ablated on the validation unseen split of REVERIE. |
| Hardware Specification | Yes | we only fine-tune the proposed model for 20k iterations on a single NVIDIA 3090 GPU. |
| Software Dependencies | No | The paper mentions several software components like AdamW optimizer, ViT-B/16, Faster R-CNN, ConceptNet, and CLIP model, but does not provide specific version numbers for any of them or for underlying frameworks like PyTorch or TensorFlow. |
| Experiment Setup | Yes | We use Adam W optimizer (Loshchilov and Hutter 2018) and the learning rate is 10 5 during the training. The ACK is not incorporated into pre-training tasks of DUET (Chen et al. 2022) and we only fine-tune the proposed model for 20k iterations on a single NVIDIA 3090 GPU. |