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..
Saliency Guided End-to-End Learning for Weakly Supervised Object Detection
Authors: Baisheng Lai, Xiaojin Gong
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on PASCAL VOC demonstrate that our approach outperforms all state-of-the-arts. |
| Researcher Affiliation | Academia | College of Information Science & Electronic Engineering, Zhejiang University, China EMAIL |
| Pseudocode | No | The paper describes the network architecture and training procedure in text and with diagrams (Figure 1) but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any links to open-source code for the described methodology, nor does it state that the code will be made publicly available. |
| Open Datasets | Yes | The experiments are conducted on the PASCAL VOC 2007 and 2012 datasets [Everingham et al., 2010], which are the benchmark most widely used in WSOD. |
| Dataset Splits | Yes | The VOC 2007 dataset contains 2501 training, 2510 validation, and 4952 test images. VOC 2012 has 5717 training, 5823 validation, and 10991 test images. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. It only mentions "Our approach is implemented using the Mat Conv Net toolbox" without further hardware details. |
| Software Dependencies | No | The paper states "Our approach is implemented using the Mat Conv Net toolbox [Vedaldi and Lenc, 2015]" but does not provide specific version numbers for this or any other software dependencies. |
| Experiment Setup | Yes | For training, we run 20 epochs, in which the first 10 epochs take a learning rate of 10 5 and the second 10 epochs take 10 6. Each image is randomly flipped and scaled to have maximal width or height of {480, 576, 688, 864, 1200} with respect to the original aspect ratio. The hyper parameters in our network are set empirically as σ = 103, λ1 = 0.1, λ2 = 1 and λ3 = 5 10 4. |