Context-aware Cross-level Fusion Network for Camouflaged Object Detection
Authors: Yujia Sun, Geng Chen, Tao Zhou, Yi Zhang, Nian Liu
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three widely used benchmark datasets demonstrate that our C2F-Net is an effective COD model and outperforms state-of-the-art models remarkably. In this section, we first provide the implementation details, datasets and evaluation metrics. Then, we provide quantitative and qualitative comparison results, and present results of ablation studies to validate the effectiveness of key modules. |
| Researcher Affiliation | Collaboration | 1 School of Computer Science, Inner Mongolia University, China 2 National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, China 3PCA Lab, the Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China 4Institut National des Sciences Appliqu ees de Rennes, Rennes, France 5Inception Institute of Artificial Intelligence, Abu Dhabi, UAE |
| Pseudocode | No | The paper describes its proposed method and modules using text and diagrams, but does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is publicly available at: https://github.com/thograce/C2FNet. |
| Open Datasets | Yes | We conduct experiments on three public benchmark datasets for camouflaged object detection. The details of each dataset are as follows: CHAMELEON [Fan et al., 2021a] contains 76 camouflaged images... CAMO [Le et al., 2019] has 1,25k images (1k for training, 0.25k for testing)... COD10K [Fan et al., 2021a] is currently the largest camouflaged object detection dataset. |
| Dataset Splits | No | The paper specifies training and testing sets for CAMO and COD10K datasets (e.g., 'CAMO [Le et al., 2019] has 1,25k images (1k for training, 0.25k for testing)', 'COD10K [Fan et al., 2021a]... (3040 for training, 2026 for testing)'), but does not explicitly provide details about a separate validation dataset split. |
| Hardware Specification | Yes | Accelerated by an NVIDIA Tesla P40 GPU, the whole network takes about 4 hours to converge over 40 epochs for training with a batch size of 32. |
| Software Dependencies | No | The paper mentions software like 'Py Torch' and 'Ada X' but does not specify their version numbers or any other software dependencies with version information. |
| Experiment Setup | Yes | We carry out a multi-scale training strategy {0.75, 1, 1.25} instead of data augmentation and resize all input images to 352 352. We utilize the Ada X [Li et al., 2020] optimization algorithm to optimize the overall parameters by setting the initial learning rate as 1e 4. Accelerated by an NVIDIA Tesla P40 GPU, the whole network takes about 4 hours to converge over 40 epochs for training with a batch size of 32. During training, the learning rate decays 10 times after 30 epochs. |