Structure-Consistent Weakly Supervised Salient Object Detection with Local Saliency Coherence
Authors: Siyue Yu, Bingfeng Zhang, Jimin Xiao, Eng Gee Lim3234-3242
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that our method achieves a new state-of-the-art performance on six benchmarks (e.g. for the ECSSD dataset: Fβ = 0.8995, Eξ = 0.9079 and MAE = 0.0489), with an average gain of 4.60% for F-measure, 2.05% for E-measure and 1.88% for MAE over the previous best method on this task. |
| Researcher Affiliation | Academia | Siyue Yu, Bingfeng Zhang, Jimin Xiao*, Eng Gee Lim School of Advanced Technology, Xi an Jiaotong-Liverpool University, Suzhou, China {siyue.yu, bingfeng.zhang, jimin.xiao, enggee.lim}@xjtlu.edu.cn |
| Pseudocode | No | The paper describes the proposed methods in text and mathematical formulas but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Source code is available at http://github.com/siyueyu/SCWSSOD. |
| Open Datasets | Yes | We train our network on scribble annotated dataset S-DUTS (Zhang et al. 2020b) and evaluate our model on six widely-used salient object detection benchmarks: (1) ECSSD (Yan et al. 2013); (2) DUTOMRON (Yang et al. 2013); (3) PASCAL-S (Li et al. 2014); (4) HKU-IS (Li and Yu 2015); (5) THUR (Cheng et al. 2014); (6) DUTS-TEST (Wang et al. 2017). |
| Dataset Splits | No | The paper mentions training on S-DUTS and evaluating on six benchmarks. It details training parameters like epochs and image resizing but does not specify explicit training/validation/test dataset splits (e.g., percentages or sample counts for each split, or reference to standard splits for these roles). |
| Hardware Specification | Yes | All experiments are run on NVIDIA Ge Force RTX 2080 Ti. |
| Software Dependencies | No | The paper mentions using ResNet-50 as a backbone and GCPANet as a baseline, but does not provide specific version numbers for software libraries or frameworks (e.g., PyTorch, TensorFlow, CUDA). |
| Experiment Setup | Yes | The model is optimized by SGD with batch size of 16, momentum of 0.9 and weight decay of 5 * 10^-4. Additionally, we use triangular warm-up and decay strategies with the maximum learning rate of 0.01 and the minimum learning rate of 1 * 10^-5 to train the network with 40 epochs. During training, each image is resized to 320 * 320 with random horizontal flipping and random cropping. ... w, σP , and σI in Eq. (4) are set to 1, 6 and 0.1, respectively. β in Eq. (8) and Eq. (9) is set to 0.3. |