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..
ICNet: Intra-saliency Correlation Network for Co-Saliency Detection
Authors: Wen-Da Jin, Jun Xu, Ming-Ming Cheng, Yi Zhang, Wei Guo
NeurIPS 2020 | Venue PDF | 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 EMAIL, EMAIL |
| 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 ยต = 0, ฯ = 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. |