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..
Repositioning the Subject within Image
Authors: Yikai Wang, Chenjie Cao, Ke Fan, Qiaole Dong, Yifan Li, Xiangyang Xue, Yanwei Fu
TMLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To assess SEELE s effectiveness in subject repositioning, we assemble a real-world subject repositioning dataset called Re S. Results of SEELE on Re S demonstrate its efficacy. Code and Re S dataset are available at https://yikai-wang.github.io/seele/. |
| Researcher Affiliation | Collaboration | 1Fudan University; 2DAMO Academy, Alibaba Group; 3Hupan Lab |
| Pseudocode | No | The paper describes the SEELE framework and its components using textual descriptions and flow diagrams, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and Re S dataset are available at https://yikai-wang.github.io/seele/. |
| Open Datasets | Yes | Code and Re S dataset are available at https://yikai-wang.github.io/seele/. We release the Re S dataset at https://yikai-wang.github.io/ seele/ to encourage research in subject repositioning. When addressing subject moving and completion, we employ the MSCOCO dataset (Lin et al., 2014), which provides object masks. For image harmonization, the i Harmony4 dataset (Cong et al., 2020) is utilized, offering unharmonized-harmonized image pairs along with subject-to-harmonize masks. |
| Dataset Splits | Yes | MSCOCO comprises 80k training images and 40k testing images, while i Harmony4 includes 65k training images and 7k testing images. |
| Hardware Specification | Yes | Training is conducted on two A6000 GPUs over 9,000 steps |
| Software Dependencies | No | The paper mentions using the Adam W optimizer (Loshchilov & Hutter, 2017) but does not specify version numbers for any software dependencies like programming languages, libraries, or frameworks. |
| Experiment Setup | Yes | For each task, we utilize the Adam W optimizer (Loshchilov & Hutter, 2017) with a learning rate of 8.0e 5, weight decay of 0.01, and a batch size of 32. Training is conducted on two A6000 GPUs over 9,000 steps, selecting the best checkpoints based on the held-out validation set. |