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..
Co-Saliency Detection Within a Single Image
Authors: Hongkai Yu, Kang Zheng, Jianwu Fang, Hao Guo, Wei Feng, Song Wang
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the experiment, we collect a new dataset of 364 color images with within-image cosaliency. Experiment results show that the proposed method can better detect the within-image co-saliency than existing algorithms. |
| Researcher Affiliation | Academia | 1 School of Computer Science and Technology, Tianjin University, Tianjin, China 2 Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 3 Institute of Artificial Intelligence and Robotics, Xi an Jiaotong University, Xi an, China 4 School of Electronic and Control Engineering, Chang an University, Xi an, China |
| Pseudocode | Yes | Algorithm 1 Co-saliency detection within a single image. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | Therefore, we collect a new image dataset, consisting of 364 color images. Each image shows certain level of withinimage co-saliency, e.g., the presence of multiple instances of the same object class with very similar appearance. |
| Dataset Splits | No | The paper mentions evaluating on the full dataset (364 images) and on subsets like 'easy' (299 images) and 'challenging' (65 images), but it does not specify explicit train/validation/test dataset splits needed for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using the "CVX convex optimization toolbox" but does not specify its version number, nor does it list other software dependencies with specific version numbers. |
| Experiment Setup | Yes | In our experiment, we generate M = 100 object proposals. The number of proposal groups is set to N = 10. The number of proposals in each group is set to K = 2. We set the balance factors λ = 0.01 in Eq. (4) and β = 0.05 in Eq. (6). The number of clusters is set to Z = 6 in the Kmeans algorithm. The initial within-image saliency map h(X) is computed using the algorithm developed in (Li and Yu 2016). Starting from the initial saliency map h(X), we first threshold this saliency map by a threshold (0.2 in our experiments) |