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..
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 | 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. |