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..
Eliminating the Cross-Domain Misalignment in Text-guided Image Inpainting
Authors: Muqi Huang, Chaoyue Wang, Yong Luo, Lefei Zhang
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show exceptional performance on leading datasets such as MS-COCO and Open Images, surpassing state-of-the-art text-guided image inpainting methods. |
| Researcher Affiliation | Collaboration | Muqi Huang1 , Chaoyue Wang2 , Yong Luo1 and Lefei Zhang1,3 1Institute of Artificial Intelligence, School of Computer Science, Wuhan University 2JD Explore Academy 3Hubei Luojia Laboratory |
| Pseudocode | No | The paper describes the method and architecture but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is released at: https://github.com/MucciH/ECDM-inpainting. |
| Open Datasets | Yes | We fine-tune our model on the standard MS-COCO dataset [Lin et al., 2014], which comprises over 100k images in the training set. For testing, we utilize 5k imagetext pairs from the MS-COCO validation set. To assess the robustness of our model to diverse data, we further validate its performance on 1.5k images from the Open Images dataset [Kuznetsova et al., 2020]. |
| Dataset Splits | Yes | For testing, we utilize 5k imagetext pairs from the MS-COCO validation set. |
| Hardware Specification | Yes | Each experiment necessitate the utilization of one A100 GPU. |
| Software Dependencies | Yes | We employ our proposed Structure-Aware Inpainting Learning (SAIL) approach for image inpainting under the architecture of Control Net and it is finetuned from Controlnet v1.1 In Paint Version. |
| Experiment Setup | Yes | The learning rate is set at 5e-5, and the batch size is configured to be 4. |