SMamba: Sparse Mamba for Event-based Object Detection
Authors: Nan Yang, Yang Wang, Zhanwen Liu, Meng Li, Yisheng An, Xiangmo Zhao
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Results on three datasets (Gen1, 1Mpx, and e Tram) demonstrate that our model outperforms other methods in both performance and efficiency. The paper includes sections like "Experiments", "Quantitative Results", "Sparsification Visualizations", and "Ablation Studies" which involve data analysis and performance metrics. |
| Researcher Affiliation | Academia | 1School of Information Engineering, Chang an University, China 2School of Civil Engineering, Tsinghua University, China EMAIL, EMAIL. All authors are affiliated with academic institutions (Chang'an University and Tsinghua University). |
| Pseudocode | No | The paper describes the methodology through text and diagrams (Figure 2, Figure 3, Figure 4) but does not contain any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a concrete statement about the release of source code for the described methodology (SMamba), nor does it include a link to a code repository. It mentions using existing tools like YOLOX but does not offer its own implementation code. |
| Open Datasets | Yes | We conduct experiments on two autonomous driving datasets Gen1 (De Tournemire et al. 2020) and 1Mpx (Perot et al. 2020), and one traffic monitoring dataset e Tram (Verma et al. 2024). These datasets are cited with author names and years, indicating they are established and publicly referenced academic datasets. |
| Dataset Splits | No | To guarantee comparison fairness, we follow the dataset preprocessing methods, augmentation techniques, mixed batching strategy, event representation method and evaluation protocols established in RVT (Gehrig and Scaramuzza 2023). The paper defers to another work for evaluation protocols and does not explicitly state the dataset splits (e.g., percentages, sample counts) within its text. |
| Hardware Specification | No | No specific hardware details (e.g., GPU models, CPU types, memory specifications) used for running the experiments are provided in the paper. It only compares FLOPs, parameters, and runtime. |
| Software Dependencies | No | The paper mentions following protocols established in RVT (Gehrig and Scaramuzza 2023) and using YOLOX (Ge et al. 2021) as the detection head, but it does not specify any version numbers for these or other software dependencies. |
| Experiment Setup | No | Implementation Details. To guarantee comparison fairness, we follow the dataset preprocessing methods, augmentation techniques, mixed batching strategy, event representation method and evaluation protocols established in RVT (Gehrig and Scaramuzza 2023). The paper refers to another work (RVT) for its experimental protocols and does not explicitly provide concrete hyperparameter values, training configurations, or system-level settings within its main text. |