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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
ESEG: Event-Based Segmentation Boosted by Explicit Edge-Semantic Guidance
Authors: Yucheng Zhao, Gengyu Lyu, Ke Li, Zihao Wang, Hao Chen, Zhen Yang, Yongjian Deng
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on DSEC and DDD17 datasets demonstrate the efficacy of the ESEG framework and its core designs. |
| Researcher Affiliation | Academia | 1College of Computer Science, Beijing University of Technology 2School of Computer Science and Engineering, Southeast University |
| Pseudocode | No | The paper describes the SELSAM algorithm and D2CAF module with equations and diagrams (e.g., Figure 3, Figure 4) but does not present structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code https://github.com/cheesewawa/ESEG |
| Open Datasets | Yes | Experiments for this paper are conducted on two commonly used datasets for event-based semantic segmentation, DSEC-Semantic (Gehrig et al. 2021) and DDD17 (Binas et al. 2017). |
| Dataset Splits | No | The paper refers to using DSEC-Semantic and DDD17 datasets for training and evaluation but does not specify the exact training, validation, and test splits (e.g., percentages, sample counts, or specific predefined split references) in the main text. It mentions '11-class labels are used for training and evaluation' for DSEC-Semantic, but not the data splits. |
| Hardware Specification | Yes | All experiments are implemented using Pytorch on an RTX 3090. |
| Software Dependencies | No | The paper mentions implementing experiments using Pytorch but does not specify its version or other software dependencies with version numbers. |
| Experiment Setup | Yes | The Adam W optimizer and the Polynomial LR scheduler are used with the initial learning rate as 6e 5. The model was trained for 40 epochs with the batch size as 4. ... The learning rate scheduler is the same as DSEC but the initial learning rate is 1 10 3 for DDD17. |