Group-wise Deep Co-saliency Detection
Authors: Lina Wei, Shanshan Zhao, Omar El Farouk Bourahla, Xi Li, Fei Wu
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | 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. |