SLiMe: Segment Like Me
Authors: Aliasghar Khani, Saeid Asgari, Aditya Sanghi, Ali Mahdavi Amiri, Ghassan Hamarneh
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | 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. |