ICNet: Intra-saliency Correlation Network for Co-Saliency Detection
Authors: Wen-Da Jin, Jun Xu, Ming-Ming Cheng, Yi Zhang, Wei Guo
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on three benchmarks show that our ICNet outperforms previous state-of-the-art methods on Co-SOD. Ablation studies validate the effectiveness of our contributions. The PyTorch code is available at https://github.com/blanclist/ICNet. |
| Researcher Affiliation | Academia | Wen-Da Jin1 Jun Xu2 Ming-Ming Cheng2 Yi Zhang1 Wei Guo1 1College of Intelligence and Computing, Tianjin University, Tianjin, China 2TKLNDST, CS, Nankai University, Tianjin, China {jwd331,yizhang}@tju.edu.cn, {csjunxu,cmm}@nankai.edu.cn |
| Pseudocode | No | The paper describes the proposed method using figures and equations, but it does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The PyTorch code is available at https://github.com/blanclist/ICNet. |
| Open Datasets | Yes | The training set is a subset of the COCO dataset [17], containing 9213 images, as suggested by [13, 32, 43]. |
| Dataset Splits | No | The paper mentions a "training set" and "test phases" but does not explicitly describe a separate validation dataset split with specific percentages or sample counts for hyperparameter tuning. Table 4 refers to `ntrain` and `ntest`, but not `nval`. |
| Hardware Specification | Yes | The training and test are performed on an Nvidia Titan Xp GPU. |
| Software Dependencies | No | Our ICNet is implemented in PyTorch [22]. While PyTorch is named, a specific version number is not provided in the text. |
| Experiment Setup | Yes | The additional parameters in our proposed modules and the last three layers are initialized with the random normal distribution of which µ = 0, σ = 0.1. We use Adam [12] as the optimizer to train our ICNet with 60 epochs. The learning rate is 10^-5, and the weight decay is 10^-4. All images are resized into 224 224 in both training and test phases. The training images are randomly flipped horizontally for augmentation. In each training iteration, we randomly select a batch of 10 images from an image group due to limited GPU memory. |