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..
Efficient Event Stream Super-Resolution with Recursive Multi-Branch Fusion
Authors: Quanmin Liang, Zhilin Huang, Xiawu Zheng, Feidiao Yang, Jun Peng, Kai Huang, Yonghong Tian
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that our approach achieves over 17% and 31% improvement on synthetic and real datasets, accompanied by a 2.3 acceleration. Furthermore, we evaluate our method on two downstream event-driven applications, i.e., object recognition and video reconstruction, achieving remarkable results that outperform existing methods. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Sun Yat-Sen University 2Peng Cheng Laboratory 3Shenzhen International Graduate School, Tsinghua University 4Peking University |
| Pseudocode | No | No clearly labeled 'Pseudocode' or 'Algorithm' block or figure was found in the paper. |
| Open Source Code | Yes | Our code and Supplementary Material are available at https://github.com/Lqm26/RMFNet. |
| Open Datasets | Yes | Event NFS [Duan et al., 2021] is the first dataset to include LR-HR pairs captured through a designed display-camera system... we utilized an event simulator [Lin et al., 2022] to transform the NFS dataset [Kiani Galoogahi et al., 2017] and RGB-DAVIS dataset [Wang et al., 2020b] into event data, resulting in NFS-syn and RGB-syn datasets. |
| Dataset Splits | Yes | To evaluate the model s generalization, we select a subset of NFS-syn data for 2(4, 8) SR training and then validate on both the NFS-syn and RGB-syn datasets. |
| Hardware Specification | Yes | All experiments were conducted on a Tesla V100 GPU. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x) were explicitly stated in the paper. |
| Experiment Setup | Yes | We set T = 9 and use Mean Squared Error (MSE) to calculate the loss: ... For a fair comparison, we maintained training settings consistent with [Weng et al., 2022]. |