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..
Object Detection Meets Knowledge Graphs
Authors: Yuan Fang, Kingsley Kuan, Jie Lin, Cheston Tan, Vijay Chandrasekhar
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | empirical evaluation on two benchmark datasets show that our approach can significantly increase recall by up to 6.3 points without compromising mean average precision, when compared to the state-of-the-art baseline. |
| Researcher Affiliation | Academia | Yuan Fang, Kingsley Kuan, Jie Lin, Cheston Tan and Vijay Chandrasekhar Institute for Infocomm Research, A*STAR, Singapore EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes mathematical formulations and optimization steps (Eq. 4-7) but does not include a formally structured pseudocode or algorithm block. |
| Open Source Code | No | The paper mentions using a 'public Python Caffe implementation' with a GitHub link, but this refers to a third-party baseline framework, not the authors' own source code for their proposed methodology. |
| Open Datasets | Yes | We use benchmark data MSCOCO15 [Lin et al., 2014] and PASCAL07 [Everingham et al., 2010], summarized in Table 1. |
| Dataset Splits | Yes | For MSCOCO15, we combine their training and validation sets for training the baseline, except for a subset of 5000 images named minival . We further split minival into 1000 and 4000 images, named minival-1k and minival-4k respectively. We use minival-1k to choose hyperparameter for our approach, and minival-4k for offline testing. ... For PASCAL07, we use their training set for training the baseline, validation set for choosing our hyperparameter, and test set for evaluation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU models, CPU types, memory specifications). |
| Software Dependencies | No | The paper mentions 'Python Caffe implementation' but does not specify version numbers for Caffe, Python, or any other critical libraries, which are necessary for full reproducibility. |
| Experiment Setup | Yes | Models are trained using stochastic gradient descent with a momentum of 0.9, a minibatch size of 2 and a weight decay of 5e-4. ... We use a learning rate of 1e-3 for the first 350K/50K iterations on MSCOCO15/PASCAL07, followed by 1e-4 for another 140K/10K iterations. ... On the validation data, we choose the hyperparameter ϵ in Eq. (4) from {0.1, 0.25, 0.5, 0.75, 0.9}. ... We set the random walk restarting probability α = 0.15, a typical value known to be stable [Fang et al., 2013]. |