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..
SLiMe: Segment Like Me
Authors: Aliasghar Khani, Saeid Asgari, Aditya Sanghi, Ali Mahdavi Amiri, Ghassan Hamarneh
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through broad experiments, we examined various design factors and showed that SLi Me outperforms existing oneand few-shot segmentation methods. |
| Researcher Affiliation | Collaboration | Aliasghar Khani1,2, Saeid Asgari Taghanaki1,2, Aditya Sanghi1, Ali Mahdavi Amiri2, Ghassan Hamarneh2 1 Autodesk Research 2 School of Computing Science, Simon Fraser University |
| Pseudocode | No | The paper describes the methodology in prose and through diagrams (e.g., Figure 3 and 4), but does not provide any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code of the project is publicly available. |
| Open Datasets | Yes | We use m Io U to compare our approach against three existing methods: Re GAN (Tritrong et al., 2021), Seg DDPM (Baranchuk et al., 2021), and Seg GPT (Wang et al., 2023) on two datasets: PASCALPart (Chen et al., 2014) and Celeb AMask-HQ (Lee et al., 2020). |
| Dataset Splits | Yes | We present the results for both 10-sample and 1-sample settings, utilizing a single validation sample for 10-sample experiments of SLi Me. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU models, CPU types, or memory used for running the experiments. |
| Software Dependencies | Yes | We opted for SD version 2.1 |
| Experiment Setup | Yes | For optimization, we employed the Adam optimizer with a learning rate of 0.1, optimizing our method for 200 epochs with a batch size of 1. Additionally, we used weighted cross-entropy loss, with each class s weight determined as the ratio of the number of whole pixels in the image to the number of pixels belonging to that class within the image. Furthermore, the values for α and β were set to 1 and 0.005, respectively. We set H and W to be 64. For optimizing on PASCAL-Part classes, we applied the following augmentations: Random Horizontal Flip, Gaussian Blur, Random Crop, and Random Rotation. For the car class, we set the random crop ratio range to [0.5, 1], while for the horse class, it was adjusted to [0.8, 1]. Additionally, we applied random rotation within the range of [ 30, 30] degrees. |