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..
Cross-Domain Few-Shot Semantic Segmentation via Doubly Matching Transformation
Authors: Jiayi Chen, Rong Quan, Jie Qin
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on four popular datasets show that DMTNet achieves superior performance over state-of-the-art approaches. |
| Researcher Affiliation | Academia | Nanjing University of Aeronautics and Astronautics State Key Laboratory of Integrated Services Networks, Xidian University |
| Pseudocode | No | The paper describes its methods and components in detail but does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/Chen Jiayi68/DMTNet. |
| Open Datasets | Yes | To fairly compare the cross-domain segmentation performance of DMTNet with PATNet [Lei et al., 2022], we choose PASCAL VOC 2012 with SBD augmentation as the source domain, and ISIC2018 [Codella et al., 2019], Chest X-ray [Candemir et al., 2014], Deepglobe [Demir et al., 2018], and FSS1000 [Wei et al., 2019] as the target domains. |
| Dataset Splits | No | The paper details a meta-training and meta-testing setup with support and query sets, but does not explicitly define a separate 'validation' split for hyperparameter tuning in the traditional sense. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used (e.g., GPU models, CPU models, memory) for running the experiments. |
| Software Dependencies | No | The paper mentions the use of Adam optimizer but does not specify version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | In the meta-training stage, we use Adam optimizer to train DMTNet for 19 epochs with a learning rate of 1e3. In the self-finetuning of the meta-testing stage, we use Adam optimizer with a learning rate of 1e-6 for ISIC2018, Deepglobe and FSS-1000, 1e-1 for Chest X-ray. All input images are resized to 400 400 resolution. |