BarLeRIa: An Efficient Tuning Framework for Referring Image Segmentation
Authors: Yaoming Wang, Jin Li, XIAOPENG ZHANG, Bowen Shi, Chenglin Li, Wenrui Dai, Hongkai Xiong, Qi Tian
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on RIS benchmarks demonstrate the superiority of Bar Le RIa over prior PET methods with a significant margin, i.e., achieving an average improvement of 5.6%. |
| Researcher Affiliation | Collaboration | 1Shanghai Jiao Tong University 2Huawei Cloud |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at https://github.com/Nastrond Ad/Bar Le RIa. |
| Open Datasets | Yes | Datasets. We employ three challenging referring image segmentation benchmarks in our experiments: Ref COCO (Kazemzadeh et al., 2014), Ref COCO+ (Kazemzadeh et al., 2014), and G-Ref (Yu et al., 2016). Please refer to the appendix A.1 for details. |
| Dataset Splits | Yes | Ref COCO (Kazemzadeh et al., 2014)... The dataset is divided into four subsets: 120,624 samples for training, 10,834 for validation, 5,657 for test A, and 5,095 for test B. |
| Hardware Specification | Yes | We train the model using 2 Tesla V100 GPUs with a batch size of 32. For Vi T-L/14, we train the model using 8 Tesla V100 GPUs with a batch size of 64 and an initial learning rate of 0.0002. |
| Software Dependencies | No | The paper mentions using the Adam optimizer but does not specify software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | We train the whole network in an end-to-end manner for 50 epochs using the Adam optimizer with a learning rate of 0.0001. A learning rate decay is employed at the 35th epoch with a decay factor of 0.1. |