Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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). |