MAS-SAM: Segment Any Marine Animal with Aggregated Features
Authors: Tianyu Yan, Zifu Wan, Xinhao Deng, Pingping Zhang, Yang Liu, Huchuan Lu
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on four public MAS datasets demonstrate that our MAS-SAM can obtain better results than other typical segmentation methods. The source code is available at https://github.com/Drchip61/MAS-SAM. |
| Researcher Affiliation | Academia | Tianyu Yan1 , Zifu Wan2 , Xinhao Deng1 , Pingping Zhang1 , Yang Liu1 , Huchuan Lu1 1School of Future Technology, School of Artiļ¬cial Intelligence, Dalian University of Technology 2 Robotics Institute, Carnegie Mellon University |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code is available at https://github.com/Drchip61/MAS-SAM. |
| Open Datasets | Yes | In this work, we adopt four public MAS benchmarks to evaluate the model performance. The MAS3K dataset [Li et al., 2020] comprises 3,103 marine images... The RMAS dataset [Fu et al., 2023] consists of 3,014 marine animal images... The UFO120 dataset [Islam et al., 2020] comprises 1,620 underwater images... The RUWI dataset [Drews-Jr et al., 2021] is a real underwater image dataset... |
| Dataset Splits | No | For the MAS3K dataset, it states: 'we use 1,769 images for training and 1,141 images for testing.' Similar splits are provided for other datasets, but no explicit validation set is mentioned for any dataset. |
| Hardware Specification | Yes | Our model is implemented with the Py Torch toolbox and one RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch toolbox' but does not specify a version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | The initial learning rate and weight decay are set to 0.001 and 0.1, respectively. We reduce the learning rate by a factor of 10 at every 20 epochs. The total number of training epochs is set to 50. The mini-batch size is set to 8. The input images are uniformly resized to 512 512 3. |