Temporal Adaptive Alignment Network for Deep Video Inpainting
Authors: Ruixin Liu, Zhenyu Weng, Yuesheng Zhu, Bairong Li
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | 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]. |