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..
SLViT: Scale-Wise Language-Guided Vision Transformer for Referring Image Segmentation
Authors: Shuyi Ouyang, Hongyi Wang, Shiao Xie, Ziwei Niu, Ruofeng Tong, Yen-Wei Chen, Lanfen Lin
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have evaluated our method on three benchmark datasets. The experimental results demonstrate that SLVi T surpasses state-of-the-art methods with lower computational cost. |
| Researcher Affiliation | Collaboration | 1Zhejiang University 2Ritsumeikan University 3Zhejiang Lab |
| Pseudocode | No | The paper describes the model architecture and processes using figures and mathematical equations, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is publicly available at: https://github.com/NaturalKnight/SLViT. |
| Open Datasets | Yes | We perform experiments on three widely used benchmark datasets for referring image segmentation, including Ref COCO [Yu et al., 2016], Ref COCO+ [Yu et al., 2016], and G-Ref [Mao et al., 2016; Nagaraja et al., 2016]. |
| Dataset Splits | Yes | Method Language Ref COCO Ref COCO+ G-Ref Model val test A test B val test A test B val(U) test(U) val(G) |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU models, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions using PyTorch and Hugging Face's Transformer library, but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | Following, we use Adam W optimizer with weight decay 0.01. The learning rate is initialed as 3e-5 and scheduled by polynomial learning rate decay with a power of 0.9. All the models are trained for 60 epochs with a batch size of 16. Each reference has 2-3 sentences on average, and we randomly sample one referring expression per object in a epoch. Image size is adjusted to 480 480 without data augmentation. |