Stabilizing Holistic Semantics in Diffusion Bridge for Image Inpainting
Authors: Jinjia Peng, Mengkai Li, Huibing Wang
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on Places2, Paris Street View and Celeb A-HQ datasets validate the efficacy of the proposed method. |
| Researcher Affiliation | Academia | 1School of Cyber Security and Computer, Hebei University, China 2College of Information Science and Technology, Dalian Maritime University, China EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methodology using mathematical equations and textual explanations, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | Yes | We validate our method and various baselines on three typical datasets, including Paris Street View (PSV) [Doersch et al., 2012], ...; Celeb A-HQ [Karras et al., 2018] ...; Places2 [Zhou et al., 2017] is a collection of more than 1.8 million natural images in multiple scenes. |
| Dataset Splits | Yes | Paris Street View (PSV) [Doersch et al., 2012], which consists of street photos taken in Paris and contains 14,900 training images and 100 validation images; Celeb A-HQ [Karras et al., 2018] is a dataset containing 30,000 high-quality human face images, divided into 28k training images and 2k validation images; |
| Hardware Specification | No | The paper does not provide specific hardware details (such as GPU/CPU models, processor types, or memory amounts) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | where the parameter a is set to 5 for all experiments. ... In Figure 7, we set Α to take values at [0.2T, 0.4T, 0.6T, 0.8T] to evaluate its effect on the final inpainted results. ... we finally choose Α = 0.6T, as the stage point for phased injection of guidance information. |