Localizing by Describing: Attribute-Guided Attention Localization for Fine-Grained Recognition
Authors: Xiao Liu, Jiang Wang, Shilei Wen, Errui Ding, Yuanqing Lin
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on the CUB-200-2011 dataset (Wah et al. 2011) demonstrate the superiority of the proposed scheme on both fine-grained recognition and attribute recognition. |
| Researcher Affiliation | Industry | Xiao Liu, Jiang Wang, Shilei Wen, Errui Ding, Yuanqing Lin Baidu Research {liuxiao12, wenshilei, dingerrui, linyuanqing}@baidu.com wangjiangb@gmail.com |
| Pseudocode | Yes | Algorithm 1 Localizing by describing algorithm: |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of its source code. |
| Open Datasets | Yes | We conduct experiments on the CUB-200-2011 datasets (Wah et al. 2011). |
| Dataset Splits | No | The paper states 'where 5, 994 images are for training, and the rest 5, 794 images are for testing' but does not explicitly detail a separate validation split or its size/methodology. |
| Hardware Specification | Yes | We train the models using Stochastic Gradient Descent (SGD) with momentum of 0.9, epoch number of 150, weight decay of 0.001, and a mini-batch size of 28 on four K40 GPUs. |
| Software Dependencies | No | The paper mentions using 'Res Net-50 (He et al. 2016)' and 'ROI-pooled feature maps (Girshick 2015)' but does not specify software dependencies with version numbers (e.g., Python, TensorFlow, PyTorch versions). |
| Experiment Setup | Yes | We train the models using Stochastic Gradient Descent (SGD) with momentum of 0.9, epoch number of 150, weight decay of 0.001, and a mini-batch size of 28 on four K40 GPUs. The initial learning rate is set at 0.0001 and reduced twice with a ratio of 0.1 after 50 and 100 epoches. An additional dropout layer with an ratio of 0.5 is added after res5c , and the size of fc15 is changed from 1000 to 200. |