Inferring Camouflaged Objects by Texture-Aware Interactive Guidance Network
Authors: Jinchao Zhu, Xiaoyu Zhang, Shuo Zhang, Junnan Liu3599-3607
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Sufficient experiments conducted on COD and SOD datasets demonstrate that the proposed method performs favorably against 23 state-of-the-art methods. |
| Researcher Affiliation | Academia | 1 Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin, China 2 Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China 3 College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China |
| Pseudocode | No | The paper describes methods using text and equations but does not include an explicitly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | Yes | We combine the training datasets of CAMO-Train, CPD1K-Train, COD10K-Train and take them as the COD training dataset, which follows SINet (Fan et al. 2020a). We use DUTS-TR (Wang et al. 2017) as training dataset for SOD. |
| Dataset Splits | No | The paper extensively discusses training and testing datasets, but does not explicitly mention a distinct validation dataset split or its use for model tuning or evaluation during training. |
| Hardware Specification | Yes | We train the model on a PC with 16GB RAM and an RTX 2080Ti GPU. |
| Software Dependencies | No | The paper mentions 'Res Net-50 (He et al. 2016) are adopted as backbone' but does not provide specific software dependencies with version numbers like deep learning frameworks (e.g., PyTorch, TensorFlow) or Python. |
| Experiment Setup | Yes | Warm-up and linear decay strategies are used. The maximum learning rate is 5e-3 for the backbone and 0.05 for other parts. Stochastic gradient descent is adopted to train the network with the momentum of 0.9 and the weight decay of 5e-4. Batchsize and maximum epoch are set to 32 and 45 respectively. We resize images to 352 x 352 in the inference stage. |