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..
EGSST: Event-based Graph Spatiotemporal Sensitive Transformer for Object Detection
Authors: Sheng Wu, Hang Sheng, Hui Feng, Bo Hu
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we introduce the two datasets utilized, the evaluation metrics, and the implementation details of our models. We train the baseline model, EGSST-B, and the extended model, EGSST-E, and compare their performance with other state-of-the-art models applied to both datasets. Detailed ablation studies are then performed to assess the impact of various components of our models. |
| Researcher Affiliation | Academia | Sheng Wu1 Hang Sheng1 Hui Feng1,2 Bo Hu1,2 1 School of Information Science and Technology, Fudan University 2 State Key Laboratory of Integrated Chips and Systems, Fudan University |
| Pseudocode | No | The paper describes its method in detail but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code can be found at: EGSST. |
| Open Datasets | Yes | Two complex event camera datasets from traffic scenarios are employed in the experiments: the Gen1 Automotive Detection Dataset [46] and the 1 Megapixel Automotive Detection Dataset [9]. |
| Dataset Splits | No | The paper mentions using datasets for training and testing, and sets a "training batch size", but it does not explicitly provide details on the training/validation/test splits, such as percentages, sample counts, or references to predefined splits. |
| Hardware Specification | Yes | The models are trained on RTX3090 GPUs using the Lightning framework...we conducte additional tests on the T4 GPU, which has performance comparable to the Titan Xp and RTX 1080Ti. |
| Software Dependencies | Yes | The framework proposed in this study is developed using Python 3.9 and Py Torch 2.0, with graph processing powered by the advanced Py Torch Geometric library [48]. |
| Experiment Setup | Yes | We employ the Adam optimizer [49] coupled with the One Cycle learning rate schedule [50], which includes 100 warm-up iterations followed by cosine decay starting from the maximum learning rate. The training batch size is set at 8, with an initial learning rate of 1e-6. |