SAMRefiner: Taming Segment Anything Model for Universal Mask Refinement
Authors: Yuqi Lin, Hengjia Li, Wenqi Shao, Zheng Yang, Jun Zhao, Xiaofei He, Ping Luo, Kaipeng Zhang
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our mask framework on a wide range of benchmarks under different settings, demonstrating better accuracy and efficiency. |
| Researcher Affiliation | Collaboration | 1State Key Lab of CAD&CG, College of Computer Science, Zhejiang University 2Shanghai AI Laboratory 3 FABU Inc. 4 The University of Hong Kong |
| Pseudocode | Yes | We present the pseudo-code for the region merging strategy in Algorithm 1, which is an important component of our split-then-merge (STM) pipeline for semantic segmentation. |
| Open Source Code | Yes | Our code is available at SAMRefiner. |
| Open Datasets | Yes | For a comprehensive evaluation of the mask refinement performance of SAMRefiner, we conduct experiments on a wide range of benchmarks, including those designed for mask refinement (DAVIS-585Chen et al. (2022)), instance segmentation (COCOLin et al. (2014)), semantic segmentation (VOCEveringham et al. (2010)) under different settings. |
| Dataset Splits | Yes | To ensure a fair comparison, we maintain the same split of data subsets (e.g., 1%, 5%, 10%) as each baseline method. We assess pseudo labels quality by randomly sampling 5,000 images in the train set (denoted as train5K) that have no intersection with annotated data subsets. |
| Hardware Specification | Yes | The time cost reported in the paper is tested on a single 3090 GPU. |
| Software Dependencies | No | The paper mentions "We implement our method with Py Torch Paszke et al. (2019)." but does not specify a version number for PyTorch or any other software component. |
| Experiment Setup | Yes | The threshold λ and µ used in the box and mask prompt are set to 0.1 and 0.5 respectively. The factors ω, γ for Gaussian distribution are set to 15 and 4 by default. For Io U adaption step, we use SGD optimizer with 0.01 learning rate. The batch size is set to 5 and we only train for 1 epoch. The learning rate is reduced to one-tenth at steps 60 and 100. We use margin ranking loss with the margin as 0.02 and the Lo RA rank is set to 4. |