Semi-supervised Class-Agnostic Motion Prediction with Pseudo Label Regeneration and BEVMix
Authors: Kewei Wang, Yizheng Wu, Zhiyu Pan, Xingyi Li, Ke Xian, Zhe Wang, Zhiguo Cao, Guosheng Lin
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments conducted on nu Scenes demonstrate that our SSL method can surpass the self-supervised approach by a large margin by utilizing only a tiny fraction of labeled data. Furthermore, our method exhibits comparable performance to weakly and some fully supervised methods. |
| Researcher Affiliation | Collaboration | 1 Key Laboratory of Image Processing and Intelligent Control, Ministry of Education School of Artificial Intelligence and Automation, Huazhong University of Science and Technology 2 S-Lab, Nanyang Technological University 3 Sense Time Research |
| Pseudocode | Yes | Algorithm 1: Pseudo Label Re-generation |
| Open Source Code | Yes | Code will be available at https://github.com/kwwcv/SSMP. |
| Open Datasets | Yes | Following previous work... nu Scenes (Caesar et al. 2019), which contains 850 scenes with annotations. |
| Dataset Splits | Yes | For fair comparisons, we follow Motion Net to use 500 scenes for training, 100 scenes for validation, and 250 scenes for testing. |
| Hardware Specification | Yes | We implement our model in Pytorch (Paszke et al. 2019) with a single A6000 GPU. |
| Software Dependencies | No | The paper mentions "Pytorch" and cites its authors (Paszke et al. 2019), but it does not specify a version number for Pytorch. It also mentions "Adam" as an optimizer, which is an algorithm, not a software dependency with a version. |
| Experiment Setup | Yes | The parameters K, µ, β, γ, θc, θw, and α are set to 5, 1, 10, 0.6, 3, 5, and 0.999, respectively. |