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..
BEV-TSR: Text-Scene Retrieval in BEV Space for Autonomous Driving
Authors: Tao Tang, Dafeng Wei, Zhengyu Jia, Tian Gao, Changwei Cai, Chengkai Hou, Peng Jia, Kun Zhan, Haiyang Sun, Fan JingChen, Yixing Zhao, Xiaodan Liang, Xianpeng Lang, Yang Wang
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on the multi-level datasets show that BEV-TSR achieves state-of-the-art performance, e.g., 85.78% and 87.66% top-1 accuracy on scene-to-text and text-to-scene retrieval respectively. |
| Researcher Affiliation | Collaboration | 1Shenzhen Campus of Sun Yat-sen University 2Li Auto Inc. |
| Pseudocode | No | The paper describes the methodology using text and equations but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | To address these limitations, we have further constructed the nu Scenes-Retrieval dataset based on the nu Scenes dataset, and the toolkit codes are attached in the supplement materials and will be public. |
| Open Datasets | Yes | To this end, we establish a multi-level retrieval dataset, nu Scenes-Retrieval, based on the widely adopted nu Scenes dataset. |
| Dataset Splits | No | The paper describes the creation of the nu Scenes-Retrieval dataset, but it does not provide specific training, validation, or test split percentages or sample counts for the experiments. |
| Hardware Specification | No | The implementation details are provided in the supplementary material. |
| Software Dependencies | No | The implementation details are provided in the supplementary material. |
| Experiment Setup | No | The implementation details are provided in the supplementary material. |