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..
Event-driven Video Deblurring via Spatio-Temporal Relation-Aware Network
Authors: Chengzhi Cao, Xueyang Fu, Yurui Zhu, Gege Shi, Zheng-Jun Zha
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our STRA significantly outperforms several competing methods, e.g., on the HQF dataset, our network achieves up to 1.3 d B in terms of PSNR over the most advanced method. |
| Researcher Affiliation | Academia | University of Science and Technology of China, China EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper provides network diagrams and mathematical equations but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at https://github.com/Chengzhi-Cao/STRA. |
| Open Datasets | Yes | Our Spatio-Temporal Relation-Aware network (STRA) is trained based on the benchmark Go Pro dataset [Nah et al., 2017], composed of synthetic events, 2,103 pairs of blurring frames and sharp clear ground-truth frames. ... For evaluation in real-world events, we utilize HQF dataset [Stoffregen et al., 2020], including both real-world events and ground-truth frames captured from a DAVIS240C [Brandli et al., 2014] |
| Dataset Splits | No | The paper mentions training on the Go Pro dataset (2,103 pairs) and testing on Go Pro testing datasets (1,111 pairs) and HQF dataset, but does not explicitly describe a validation dataset or its split. |
| Hardware Specification | Yes | Our network is implemented using Pytorch on a single NVIDIA RTX 2080Ti GPU. |
| Software Dependencies | No | The paper states 'implemented using Pytorch' but does not specify a version number for PyTorch or any other software libraries used. |
| Experiment Setup | Yes | In the training process, we randomly cropped the sampled frames with the size of 256 256. For data augmentation, each patch was horizontally flipped with the probability of 0.5. We use a batch size of 8 training pairs and ADAM optimizer [Kingma and Ba, 2017] with parameter β1 = 0.9, β2 = 0.999. The maximum training epoch is set to 200, with the initial learning rate 10 4, then decays by 25% every 50 epochs. |