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..
Interactive Portrait Harmonization
Authors: Jeya Maria Jose Valanarasu, HE Zhang, Jianming Zhang, Yilin Wang, Zhe Lin, Jose Echevarria, Yinglan Ma, Zijun Wei, Kalyan Sunkavalli, Vishal Patel
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on both synthetic and real-world datasets show that the proposed approach is efficient and robust compared to previous harmonization baselines, especially for portraits. |
| Researcher Affiliation | Collaboration | Jeya Maria Jose Valanarasu1 , He Zhang2, Jianming Zhang2, Yilin Wang2, Zhe Lin2, Jose Echevarria2, Yinglan Ma2, Zijun Wei2, Kalyan Sunkavalli2, Vishal M. Patel1 1 Johns Hopkins University, 2 Adobe Research |
| Pseudocode | No | No pseudocode or algorithm block was found in the paper. |
| Open Source Code | Yes | The code can be found here: https://github.com/jeya-maria-jose/Interactive-Portrait-Harmonization |
| Open Datasets | Yes | Publicly available datasets like i Harmony4 Cong et al. (2020) were proposed for background harmonization and do not provide any reference region information. So, we curate a synthetic dataset and also introduce a real-world portrait harmonization dataset for validating. 1) Int Harmony: ... Int Harmony is built on top of MS-COCO dataset Lin et al. (2014). |
| Dataset Splits | No | The number of training images in Int Harmony is 118287 and 959 images are allocated for testing. No explicit mention of a validation split percentage or count was found for any dataset. |
| Hardware Specification | Yes | Our framework is developed in Pytorch Paszke et al. (2019) and the training is done using NVIDIA RTX 8000 GPUs. |
| Software Dependencies | No | The paper mentions 'Pytorch Paszke et al. (2019)' but does not provide a specific version number for it or any other software dependency. |
| Experiment Setup | Yes | We use an Adam optimizer Kingma & Ba (2014) with a learning rate of 10 4, 10 5, 10 6 at each stage respectively. The batch size is set equal to 48. The images are resized to 256 256 while training. |