Omni-Dimensional State Space Model-driven SAM for Pixel-level Anomaly Detection
Authors: Chao Huang, Qianyi Li, Jie Wen, Bob Zhang
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that the proposed ODS-SAM outperforms state-of-the-art models on both industrial and medical image datasets. |
| Researcher Affiliation | Academia | Chao Huang1,2 , Qianyi Li3 , Jie Wen4 , Bob Zhang1 1University of Macau 2Sun Yat-sen University, Shenzhen Campus 3Ningbo University 4Harbin Institute of Technology, Shenzhen |
| Pseudocode | No | The paper describes the method using diagrams and textual explanations, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about the release of source code or a link to a code repository. |
| Open Datasets | Yes | MVTec AD [Bergmann et al., 2020] is adopted to evaluate the proposed method for pixel-level anomaly detection in industrial scenes. The Mo Nu Seg dataset [Kumar et al., 2017] comprises 30 microscopy images from 7 organs in the training set, with annotations for 21,623 individual nuclei. The Gland segmentation (Gla S) challenge [Sirinukunwattana et al., 2017] encompasses 85 images for training and 80 images for testing. Moreover, four Polyp datasets (Kvasir-SEG [Jha et al., 2020], Clinic DB [Bernal et al., 2015], Colon DB [Tajbakhsh et al., 2015], and ETIS [Silva et al., 2014]) are adopted to evaluate the proposed method. |
| Dataset Splits | Yes | The Mo Nu Seg dataset [Kumar et al., 2017] comprises 30 microscopy images from 7 organs in the training set, with annotations for 21,623 individual nuclei. The Gland segmentation (Gla S) challenge [Sirinukunwattana et al., 2017] encompasses 85 images for training and 80 images for testing. The experiment setup is followed [Fan et al., 2020]. |
| Hardware Specification | Yes | The batch size is 10 and trained on an NVIDIA A100 GPU. |
| Software Dependencies | No | The paper mentions using Adam optimizer but does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | During the training, we adopted the Adam with an initial learning rate of 0.001 and a weight decay regularization parameter of 1e-5. The batch size is 10 and trained on an NVIDIA A100 GPU. The maximum number of epochs is 200. SAM pre-trained weights adopted in all experiments is based on Vi T-H. |