End-to-End United Video Dehazing and Detection
Authors: Boyi Li, Xiulian Peng, Zhangyang Wang, Jizheng Xu, Dan Feng
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | A thorough study has been conducted over a number of structure options, to identify the best temporal fusion strategy. Experiment Results on Video Dehazing. Table 1: PSNR/SSIM Comparisons of Various Structures. |
| Researcher Affiliation | Collaboration | 1Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology 2Microsoft Research, Beijing, China 3Department of Computer Science and Engineering, Texas A&M University |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a direct link to a code repository for the methodology described. |
| Open Datasets | Yes | We created a synthetic hazy video dataset based on (1), using 18 videos selected from the TUM RGB-D Dataset (Sturm et al. 2012) and We synthesize hazy videos with various haze levels for a subset of ILSVRC2015 VID dataset (Russakovsky et al. 2015) |
| Dataset Splits | No | The paper explicitly states training and testing set sizes and compositions (e.g., 'training set, consisting of 5 videos with 100,000 frames' and 'testing set called Test Set V1, consisting of the rest 13 relatively short video clips with a total of 403 frames'), but it does not specify a distinct validation set or its size/split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with versions like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | When training EVD-Net, the momentum and the decay parameters are set to 0.9 and 0.0001, respectively, with a batch size of 8. We adopt the Mean Square Error (MSE) loss, which has been shown in (Li et al. 2017a; 2017b) that it is well aligned with SSIM and visual quality. we first tune only the fully-connected layers in the high-level detection part of EVD-Net for 90,000 iterations, and then tune the entire concatenated pipeline for another 10,000 iterations. |