Object Detection Meets Knowledge Graphs
Authors: Yuan Fang, Kingsley Kuan, Jie Lin, Cheston Tan, Vijay Chandrasekhar
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | 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 yfang@i2r.a-star.edu.sg, kingsley.kuan@gmail.com, {lin-j,cheston-tan,vijay}@i2r.a-star.edu.sg |
| 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]. |