Deep Descriptor Transforming for Image Co-Localization
Authors: Xiu-Shen Wei, Chen-Lin Zhang, Yao Li, Chen-Wei Xie, Jianxin Wu, Chunhua Shen, Zhi-Hua Zhou
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical studies validate the effectiveness of the proposed DDT method. On benchmark image co-localization datasets, DDT consistently outperforms existing state-of-the-art methods by a large margin. |
| Researcher Affiliation | Academia | 1National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China 2The University of Adelaide, Adelaide, Australia |
| Pseudocode | No | The paper describes the method procedurally in text but does not include formally labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link confirming the release of their own source code for the described methodology. |
| Open Datasets | Yes | Our experiments are conducted on four challenging datasets commonly used in image co-localization, i.e., the Object Discovery dataset [Rubinstein et al., 2013], the PASCAL VOC 2007 / VOC 2012 dataset [Everingham et al., 2015] and the Image Net Subsets [Li et al., 2016]. |
| Dataset Splits | No | The paper mentions using the 'trainval' set for VOC datasets, but does not explicitly provide specific percentages, counts, or a detailed methodology for a validation split. |
| Hardware Specification | Yes | All the experiments are run on a computer with Intel Xeon E5-2660 v3, 500G main memory, and a K80 GPU. |
| Software Dependencies | No | The paper mentions using 'Mat Conv Net' but does not specify a version number for this software dependency. |
| Experiment Setup | Yes | In our experiments, the images keep the original image resolutions. For the pre-trained deep model, the publicly available VGG-19 model [Simonyan and Zisserman, 2015] is employed to extract deep convolution descriptors from the last convolution layer (before pool5). |