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..
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 | Venue PDF | 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. |