Large Occluded Human Image Completion via Image-Prior Cooperating
Authors: Hengrun Zhao, Yu Zeng, Huchuan Lu, Lijun Wang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experiments, we demonstrate our method s superior performance compared to state-of-the-art methods. ... Experiments Datasets ... Implementation Details ... Ablation Study |
| Researcher Affiliation | Academia | Hengrun Zhao*1, Yu Zeng*2, Huchuan Lu 1, Lijun Wang1, 1Dalian University of Technology 2Johns Hopkins University |
| Pseudocode | No | The paper describes its methods in text and with architectural diagrams (e.g., Fig. 4, Fig. 6), but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/Zhao Hengrun/LOHC. |
| Open Datasets | Yes | As a result, we opted to use the AHP dataset (Zhou et al. 2021) |
| Dataset Splits | Yes | For testing, we used the original dataset s validation set, which amounted to 3400 pieces, while for training, we utilized the original dataset s training set of 53199 images. |
| Hardware Specification | Yes | We use the Pytorch framework for our implementation and train on an Nvidia A100 GPU. |
| Software Dependencies | No | The paper mentions 'Pytorch framework' and 'U-Net' but does not specify their version numbers or any other software dependencies with specific versions. |
| Experiment Setup | Yes | We trained the coarse network with a batch size of 128 and set the learning rate to 1e-4. For the refinement network, we used a smaller batch size of 16 and set the learning rate to 1e-3. The learning rates of all discriminators are set to 1e-6. |