Few-Cost Salient Object Detection with Adversarial-Paced Learning
Authors: Dingwen Zhang, HaiBin Tian, Jungong Han
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on four widely-used benchmark datasets demonstrate that the proposed method can effectively approach to the existing supervised deep salient object detection models with only 1k human-annotated training images. |
| Researcher Affiliation | Academia | 1School of Mechano-Electronic Engineering, Xidian University, Xi an, Shaanxi 710071 2Computer Science Department, Aberystwyth University, Ceredigion, SY23 3FL |
| Pseudocode | No | The paper refers to "Alg. 1 and Alg. 2" as showing the optimization pipeline, but these algorithm blocks are not present in the provided text. |
| Open Source Code | Yes | The project page is available at https://github.com/hb-stone/FC-SOD. |
| Open Datasets | Yes | We use four widely-used benchmark datasets to implement the experiments, which include PASCALS [41], DUT-O [42], SOD [43], and DUTS [28]. Following the previous works [44, 20, 45], we use the training split of the DUT-S dataset for training and test the trained models on the other datasets. |
| Dataset Splits | No | Following the previous works [44, 20, 45], we use the training split of the DUT-S dataset for training and test the trained models on the other datasets. The paper specifies training and test splits but does not explicitly detail a separate validation split or its size/proportion. |
| Hardware Specification | Yes | We implement the proposed algorithm on the Py Torch framework using a NVIDIA GTX 1080Ti GPU. |
| Software Dependencies | No | We implement the proposed algorithm on the Py Torch framework using a NVIDIA GTX 1080Ti GPU. The paper mentions PyTorch but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | When training the saliency network, we use the Stochastic Gradient Descent (SGD) optimization method, where the momentum is set to 0.9, and the weight decay is set to 5 10 4. The initial learning rates of the taskpredictor and the pace-generator are 2.5 10 4 and 10 4, respectively, which are decreased with polynomial decay parameterized by 0.9. For training the pace network, we adopt the Adam optimizer [46] with the learning rate 10 4. The same polynomial decay as the saliency network is also used. We set beta = 0.01 and eta = 0.7 according to a heuristic grid search process. our method uses in total 24.5K iterations and the loss and performance curves are shown in Fig. 2. |