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..
Group-wise Deep Co-saliency Detection
Authors: Lina Wei, Shanshan Zhao, Omar El Farouk Bourahla, Xi Li, Fei Wu
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate the effectiveness of our approach in comparison with the state-of-the-art approaches. |
| Researcher Affiliation | Academia | Lina Wei1, Shanshan Zhao1, Omar El Farouk Bourahla1, Xi Li1,2, , Fei Wu1 1 Zhejiang University, Hangzhou, China 2 Alibaba-Zhejiang University Joint Institute of Frontier Technologies, Hangzhou, China |
| Pseudocode | No | The paper describes the network architecture and processes with text and diagrams but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the described methodology. |
| Open Datasets | Yes | We conduct a set of qualitative and quantitative experiments on three benchmark datasets annotated with pixel-wised ground-truth labeling, including the i Coseg dataset [Batra et al., 2010], the MSRC-v2 dataset [Winn et al., 2005] and the Cosal2015 dataset [Zhang et al., 2016b]. The training data we used in our approach are generated from existing image dataset(Coco dataset [Lin et al., 2014]) which has 9213 images with the masks information. |
| Dataset Splits | No | The paper mentions training data and test data, but it does not explicitly specify a validation dataset split for hyperparameter tuning or early stopping during training. |
| Hardware Specification | No | The paper does not explicitly provide specific hardware details such as GPU models, CPU models, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions that 'The fully convolutional network (FCN) is implemented by using the Caffe [Jia et al., 2014] toolbox' and uses 'VGG 16-layer net [Simonyan and Zisserman, 2014]', but it does not specify version numbers for Caffe or any other software dependencies. |
| Experiment Setup | Yes | We resize all the images and ground-truth maps to 128 256 pixels for training. The momentum parameter is chosen as 0.99, the learning rate is set to 1e-10, and the weight decay is 0.0005. We need about 60000 training iterations for convergence. |