Inharmonious Region Localization by Magnifying Domain Discrepancy
Authors: Jing Liang, Li Niu, Penghao Wu, Fengjun Guo, Teng Long1574-1582
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on image harmonization dataset show the superiority of our designed framework. |
| Researcher Affiliation | Collaboration | Jing Liang1, Li Niu1*, Penghao Wu1, Fengjun Guo2, Teng Long2 1Mo E Key Lab of Artificial Intelligence, Shanghai Jiao Tong University 2INTSIG {leungjing,ustcnewly,wupenghao Craig}@sjtu.edu.cn, {fengjun guo, mike long}@intsig.net |
| Pseudocode | No | The paper describes algorithms (e.g., how i HDRNet works, DDM loss, DI loss, overall framework) but does not present any formal pseudocode blocks or figures explicitly labeled 'Pseudocode' or 'Algorithm'. |
| Open Source Code | No | The paper mentions implementing the method using Pytorch but does not provide any statement or link regarding the public release of its source code. |
| Open Datasets | Yes | We conduct experiments on the image harmonization dataset i Harmony4 (Cong et al. 2020), which provides inharmonious images with their corresponding inharmonious region masks. |
| Dataset Splits | No | Following (Liang, Niu, and Zhang 2021), the training set and test set are tailored to 64255 images and 7237 images respectively. The paper does not explicitly provide details about a validation dataset split (e.g., size or percentage). |
| Hardware Specification | Yes | All experiments are conducted on a workstation with an Intel Xeon 12-core CPU(2.1 GHz), 128GB RAM, and a single Titan RTX GPU. |
| Software Dependencies | Yes | We implement our method using Pytorch (Paszke et al. 2019) with CUDA v10.2 on Ubuntu 18.04 and set the input image size as 256 256. |
| Experiment Setup | Yes | We choose Adam optimizer (Kingma and Ba 2014) with the initial learning rate 0.0001, batch size 8, and momentum parameters β1 = 0.5, β2 = 0.999. The hyper-parameter λddm and λdi in Eqn. (3) are set as 0.01 for DIRL(Liang, Niu, and Zhang 2021) and 0.001 for UNet (Ronneberger, Fischer, and Brox 2015) respectively. |