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..
Video Inpainting by Jointly Learning Temporal Structure and Spatial Details
Authors: Chuan Wang, Haibin Huang, Xiaoguang Han, Jue Wang5232-5239
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide qualitative and quantitative evaluation on three datasets, demonstrating that our method outperforms previous learning-based video inpainting methods. |
| Researcher Affiliation | Collaboration | 1Megvii (Face++) EMAIL 2Shenzhen Research Inst. of Big Data, The Chinese University of Hong Kong, Shenzhen, China EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper provides a 'Project Page' URL (http://wangchuan.github.io/archive/research/videoinp/) but does not explicitly state that the source code for the methodology is available there, nor does it provide a direct link to a code repository. |
| Open Datasets | Yes | To validate our 3D-2D combined completion network, we tested on three datasets, Face Forensics (R ossler et al. 2018), 300VW (Chrysos et al. 2015) and Caltech (Doll ar et al. 2012). |
| Dataset Splits | Yes | For each dataset, we separate the whole data samples into training and validation sets and control their proportion 5 : 1. |
| Hardware Specification | Yes | The implementation is based on Tensor Flow and the network training is performed on a single NVIDIA Ge Force GTX 1080 Ti. |
| Software Dependencies | No | The paper mentions 'TensorFlow' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | The Comb CN is trained with 100k iterations by an Adam optimizer, whose regression weight and learning rate are set to 0.01 and 0.001, respectively. Each frame is in 128^2 resolution, F, H, W, r are set to 32, 128, 128 and 2 respectively. We randomly generate a hole across all frames in the [0.375l, 0.5l] pixel range. |