Collaborative Learning for Weakly Supervised Object Detection
Authors: Jiajie Wang, Jiangchao Yao, Ya Zhang, Rui Zhang
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on PASCAL VOC 2007 and 2012 data sets have demonstrated the effectiveness of the proposed framework. |
| Researcher Affiliation | Academia | Jiajie Wang, Jiangchao Yao, Ya Zhang , Rui Zhang Cooperative Medianet Innovation Center, Shanghai Jiao Tong University, China {ww1024,sunarker,ya zhang,zhang rui}@sjtu.edu.cn |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We experiment with two widely used benchmark data sets: PASCAL VOC 2007 and 2012 [Everingham et al., 2010] |
| Dataset Splits | Yes | We follow the standard splits of the data sets and use the trainval set with only image-level labels for training and the test set with groundtruth bounding boxes for testing. |
| Hardware Specification | No | The paper mentions models like VGG16 and Faster-RCNN, but does not specify the hardware (e.g., specific GPU or CPU models, memory details) on which these models were trained or experiments were run. |
| Software Dependencies | No | The paper mentions using VGG16 and Faster-RCNN but does not provide specific version numbers for these or other software dependencies to replicate the experiment. |
| Experiment Setup | Yes | We empirically set the hyper parameter β to 0.8. We train our networks for total 20 epochs, setting the learning rate of the first 12 epochs to 1e-3, and the last 8 epochs to 1e-4. |