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..
Temporal Adaptive Alignment Network for Deep Video Inpainting
Authors: Ruixin Liu, Zhenyu Weng, Yuesheng Zhu, Bairong Li
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Both quantitative and qualitative evaluation results show that our method significantly outperforms existing deep learning based methods. We conduct extensive experiments on Youtube-VOS [Xu et al., 2018] and DAVIS [Perazzi et al., 2016] datasets. |
| Researcher Affiliation | Academia | Ruixin Liu , Zhenyu Weng , Yuesheng Zhu and Bairong Li Communication and Information Security Lab, Shenzhen Graduate School, Peking University |
| Pseudocode | No | The paper does not contain explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | Yes | We conduct extensive experiments on Youtube-VOS [Xu et al., 2018] and DAVIS [Perazzi et al., 2016] datasets. |
| Dataset Splits | Yes | The first is You Tube-VOS [Xu et al., 2018] Dataset... It contains 4,453 You Tube video clips and 94 object categories and is split into 5,471 for training, 474 for validation and 508 for testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU models, CPU types, or memory) used for running experiments. |
| Software Dependencies | No | The paper mentions using 'Adam optimizer' but does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | Yes | We select five reference frames(Xt 4, Xt 2, Xt 1, Xt+2, Xt+4) and resize them into 256 256 as inputs when training the network. To accelerate the training process while reducing over-fitting, we initialize parameters of our neural network by using the initialization method in [He et al., 2015]. Adam optimizer with the initial learning rate to 10 4 is utilized, we decayed the learning rate by 0.1 every 1 million iterations. For our experiments, the loss term weights are adopted from [Liu et al., 2018]. |