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..
F³Net: Fusion, Feedback and Focus for Salient Object Detection
Authors: Jun Wei, Shuhui Wang, Qingming Huang12321-12328
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on five benchmark datasets demonstrate that F3Net outperforms state-of-the-art approaches on six evaluation metrics. To demonstrate the performance of F3Net, we report experiment results on five popular SOD datasets and visualize some saliency maps. We conduct a series of ablation studies to evaluate the effect of each module. |
| Researcher Affiliation | Academia | 1Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, China 2University of Chinese Academy of Sciences, Beijing, 100049, China |
| Pseudocode | Yes | Algorithm 1: Cascaded Feedback Decoder |
| Open Source Code | Yes | Code will be released at https://github.com/weijun88/F3Net. Codes has been released. |
| Open Datasets | Yes | The performance of F3Net is evaluated on five popular datasets, including ECSSD (Yan et al. 2013) with 1000 images, PASCAL-S (Li et al. 2014) with 850 images, DUTOMRON (Yang et al. 2013) with 5168 images, HKU-IS (Li and Yu 2015) with 4,447 images and DUTS (Wang et al. 2017a) with 15,572 images. All datasets are human-labeled with pixel-wise ground-truth for quantitative evaluations. |
| Dataset Splits | No | The paper mentions DUTS-TR (training) and DUTS-TE (testing) splits but does not explicitly describe a separate validation split or its size/methodology. |
| Hardware Specification | Yes | An RTX 2080Ti GPU is used for acceleration. |
| Software Dependencies | Yes | We use Pytorch 1.3 to implement our model. |
| Experiment Setup | Yes | Maximum learning rate is set to 0.005 for Res Net-50 backbone and 0.05 for other parts. Warm-up and linear decay strategies are used to adjust the learning rate. The whole network is trained end-to-end, using stochastic gradient descent (SGD). Momentum and weight decay are set to 0.9 and 0.0005, respectively. Batchsize is set to 32 and maximum epoch is set to 32. |