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 | Conference PDF | Archive PDF | Plain Text | 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]. |