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..
Weakly-Supervised Mirror Detection via Scribble Annotations
Authors: Mingfeng Zha, Yunqiang Pei, Guoqing Wang, Tianyu Li, Yang Yang, Wenbin Qian, Heng Tao Shen
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three mirror datasets show that our network outperforms relevant state-of-the-art methods on all evaluation metrics and achieves performance comparable to fully supervised approaches. |
| Researcher Affiliation | Academia | Mingfeng Zha1, Yunqiang Pei1, Guoqing Wang1*, Tianyu Li1, Yang Yang1, Wenbin Qian2, Heng Tao Shen1 1University of Electronic Science and Technology of China 2Jiangxi Agricultural University |
| Pseudocode | No | The paper describes the proposed modules and their operations using mathematical formulas and text, but it does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | The dataset and codes are available at https://github.com/winter-flow/WSMD. |
| Open Datasets | Yes | We collect training images from MSD, PMD, and Mirror-RGBD datasets, totaling 10,158 images, and relabel them as the training set of S-Mirror dataset. Models are evaluated using the testing sets of the above three datasets. |
| Dataset Splits | No | The paper specifies training and testing sets, but does not explicitly mention a separate validation set or its split details. |
| Hardware Specification | Yes | We implement our network using Py Torch and conduct experiments on an A100 GPU. |
| Software Dependencies | No | The paper only mentions "Py Torch" without specifying its version or other software dependencies with their respective version numbers. |
| Experiment Setup | Yes | All images are resized to 352 x 352. During the training phase, the batch size is 16, the initial learning rate is 1e-4, the decay rate is 0.9, Adam is used as the optimizer, and the epoch is 150. |