Learning Deep Relations to Promote Saliency Detection
Authors: Changrui Chen, Xin Sun, Yang Hua, Junyu Dong, Hongwei Xv10510-10517
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments demonstrate that our method can significantly boost the performance of the state-of-the-art saliency detectors on various benchmark datasets. 5 Experiments |
| Researcher Affiliation | Academia | 1Ocean University of China, 2Queen s University Belfast |
| Pseudocode | No | The paper describes the method using prose and a diagram, but no structured pseudocode or algorithm block is provided. |
| Open Source Code | No | The code will be published on https://github.com/ouc-ocean-group/LDPS soon. |
| Open Datasets | Yes | In our experiment, we use four well-known saliency benchmark datasets to evaluate our method. HKU-IS (Li and Yu 2016) contains 4447 images with multiple salient objects. DUT-OMRON (Yang et al. 2013) includes 5168 complicated images with one or two salient objects. Pascal-S (Li et al. 2014) which contains 850 natural images is a subset of the PASCAL VOC2010 dataset. ECSSD (Yan et al. 2013) contains 1000 images with multiple objects of varying sizes. The training dataset of our model is DUTS-TE (Wang et al. 2017a) which has 5019 images collected from the Image Net DET dataset (Deng et al. 2009). |
| Dataset Splits | No | The paper mentions datasets used for training and testing (DUTS-TE for training, DUTS-TEST for training their model, and HKU-IS, DUT-OMRON, Pascal-S, ECSSD for evaluation), but does not provide specific percentages or counts for train/validation/test splits, nor does it explicitly mention a validation set. |
| Hardware Specification | Yes | The training is operated on a PC with a GTX 2080ti GPU. We use a single GTX 1080 during the inference phase. |
| Software Dependencies | No | The paper mentions Mobile Net v2 as a feature extractor but does not provide specific version numbers for software dependencies such as deep learning frameworks or other libraries. |
| Experiment Setup | Yes | All the thresholds for generating the masks are set as: θfg = 0.9, θa fg = 0.8, θa bg = 0.3. We train our model on only 1 GPU for 20k iterations, with a learning rate of 5e-4 for backbone and 5e-3 for the rest components. The learning rates are decreased by the polynomial learning rate policy. |