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 | Archive PDF | Plain Text | 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. |