Memory-Aided Contrastive Consensus Learning for Co-salient Object Detection
Authors: Peng Zheng, Jie Qin, Shuo Wang, Tian-Zhu Xiang, Huan Xiong
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on all the latest Co SOD benchmarks demonstrate that our lite MCCL outperforms 13 cutting-edge models, achieving the new state of the art ( 5.9% and 6.2% improvement in S-measure on Co SOD3k and Co Sal2015, respectively). |
| Researcher Affiliation | Collaboration | 1 Nanjing University of Aeronautics and Astronautics, Nanjing, China 2 ETH Zurich, Zurich, Switzerland 3 Inception Institute of Artificial Intelligence, Abu Dhabi, UAE 4 Harbin Institute of Technology, China 5 Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE |
| Pseudocode | No | No pseudocode or clearly labeled algorithm block was found in the paper. |
| Open Source Code | Yes | Our source codes, saliency maps, and online demos are publicly available at https://github.com/Zheng Peng7/MCCL. |
| Open Datasets | Yes | We follow (Zhang et al. 2020b) to use DUTS class (Zhang et al. 2020c) and COCO-SEG (Wang et al. 2019) as our training sets. |
| Dataset Splits | No | The paper mentions training and testing sets, but does not explicitly provide details about a validation dataset split (e.g., percentages or sample counts for validation). |
| Hardware Specification | Yes | All the experiments are implemented based on the Py Torch library (Paszke et al. 2019) with a single NVIDIA RTX3090 GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch library' but does not specify a version number or other software dependencies with versions. |
| Experiment Setup | Yes | batchsize = min(#group1, ..., #group N, 48), The images are resized to 256x256 for training and inference. Our MCCL is trained for 250 epochs with the Adam W optimizer (Loshchilov and Hutter 2019). The initial learning rate is 1e-4 and divided by 10 at the last 20th epoch. |