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..
H-MBA: Hierarchical MamBa Adaptation for Multi-Modal Video Understanding in Autonomous Driving
Authors: Siran Chen, Yuxiao Luo, Yue Ma, Yu Qiao, Yali Wang
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct our H-MBA on multi-modal video understanding benchmarks in autonomous driving, including DRAMA (Malla et al. 2023) and BDD-X (Kim et al. 2018). The extensive results show that, our H-MBA achieves the state-of-the-art performance, e.g., it gets 66.9% m Io U on risk localization, with 5.5% improvement compared with the previous SOTA approach (Malla et al. 2023). |
| Researcher Affiliation | Academia | 1 Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China 2 School of Artificial Intelligence, University of Chinese Academy of Science, Beijing, China 3 Shanghai Artificial Intelligence Laboratory, Shanghai, China 4 The Hong Kong University of Science and Technology, Hong Kong, China 5 The Hong Kong Polytechnic University, Hong Kong, China |
| Pseudocode | No | The paper describes the proposed H-MBA framework, C-Mamba, and Q-Mamba modules using textual descriptions and mathematical formulas (equations 1-6), but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about making the source code publicly available, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We conduct our H-MBA on multi-modal video understanding benchmarks in autonomous driving, including DRAMA (Malla et al. 2023) and BDD-X (Kim et al. 2018). |
| Dataset Splits | No | The paper describes the total size of the datasets and frame sampling strategy (L=5 for DRAMA, L=8 for BDD-X) but does not provide specific training, validation, and test dataset split percentages or counts needed for reproduction. |
| Hardware Specification | Yes | All the experiments are done with 4 A6000 GPUs |
| Software Dependencies | No | The paper mentions using Shikra, CLIP Vi T-L/14, and Vicuna-7/13B but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | we train the model for 5 epochs with 2e 5 learning rate in cosine annealing schedule (Loshchilov and Hutter 2016). |