Two-Stream Contextualized CNN for Fine-Grained Image Classification
Authors: Jiang Liu, Chenqiang Gao, Deyu Meng, Wangmeng Zuo
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | According to our experiments on public datasets, our approach achieves considerable high recognition accuracy without any tedious human s involvements, as compared with the state-of-the-art approaches. |
| Researcher Affiliation | Academia | 1Chongqing University of Posts and Telecommunications, Chongqing, China 2Xi an Jiaotong University, Xi an, China 3Harbin Institute of Technology, Harbin, China |
| Pseudocode | No | The paper describes the method procedurally but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that source code for the methodology is openly available. |
| Open Datasets | Yes | We test our two-stream contextualized CNN framework on three popular datasets: Oxford Flower 102(Flower102)(Nilsback and Zisserman 2008), Caltech UCSD Birds 200-2010(CUB2010)(Welinder et al. 2010) and Caltech-UCSD Birds 200-2011(CUB2011)(Wah et al. 2011) using their corresponding evaluation metrics. |
| Dataset Splits | No | The paper mentions using a 'training set' to calculate a mean image, but it does not provide specific percentages or counts for training, validation, or test splits, nor does it cite predefined splits for these datasets. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions deep learning frameworks and algorithms but does not provide specific software dependencies with version numbers (e.g., 'Python 3.8', 'PyTorch 1.9'). |
| Experiment Setup | No | The paper describes the overall architecture and training strategy (e.g., 'fine-tuned from vgg-16', 'SGD method') but does not provide specific experimental setup details such as hyperparameter values (learning rate, batch size, epochs) or detailed training configurations. |