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..
Zero-shot Image Editing with Reference Imitation
Authors: Xi Chen, Yutong Feng, Mengting Chen, Yiyang Wang, Shilong Zhang, Yu Liu, Yujun Shen, Hengshuang Zhao
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally show the effectiveness of our method under various test cases as well as its superiority over existing alternatives. We do not have theoretical results. |
| Researcher Affiliation | Collaboration | 1The University of Hong Kong 2Alibaba Group 3Ant Group |
| Pseudocode | No | The paper describes processes in text and figures (like Figure 3 showing the training process) but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | According to the regulations of the company, we would release the code and benchmark after internal check. |
| Open Datasets | Yes | We collect 100 k high-resolution videos from open-sourced websites like Pexels [29]. To further expand the diversity of training samples, we use the SAM [20] dataset that contains 10 million images and 1 billion object masks. |
| Dataset Splits | Yes | During training, the sampling portions of the video and SAM data are 70% versus 30% as default. |
| Hardware Specification | Yes | Experiments are conducted with a total batch size of 64 on 8 A100 GPUs. |
| Software Dependencies | No | The paper mentions software components such as stable diffusion-1.5, CLIP, and DINOv2, and Adam optimizer, but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | In this work, all experiments are conducted with the resolution of 512 512... During training, we use the Adam [19] optimizer and set the learning rate as 1e-5... Experiments are conducted with a total batch size of 64... For the masking strategy of the source image, we randomly choose the grid number N N from 3 to 10. We set 75% chances to drop the grid with SIFT-matched features and set 50% chances for other regions. |