Deep Structured Learning for Visual Relationship Detection
Authors: Yaohui Zhu, Shuqiang Jiang
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on the Visual Relationship Detection (VRD) dataset and the large-scale Visual Genome (VG) dataset validate the effectiveness of our method, which outperforms state-of-the-art methods. |
| Researcher Affiliation | Academia | 1Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, 100190, China 2University of Chinese Academy of Sciences, Beijing, China |
| Pseudocode | No | The paper includes a network architecture diagram (Figure 2) and describes the method in text and mathematical formulas, but it does not provide any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statements about releasing open-source code for the described methodology, nor does it provide any links to a code repository. |
| Open Datasets | Yes | we evaluate our proposed method on Visual relationship detection (VRD) (Lu et al. 2016) and Visual Genome (VG) (Zhang et al. 2017) datasets. |
| Dataset Splits | No | The paper specifies 'train/test split' for both VRD and VG datasets, providing detailed numbers for training and test images/relationships. However, it does not explicitly mention or specify details for a 'validation' split. |
| Hardware Specification | No | The paper mentions using a 'VGG-16 network' for Faster R-CNN, but it does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Faster R-CNN' and 'VGG-16 network' as components but does not provide specific version numbers for any software dependencies (e.g., deep learning frameworks, libraries, or programming languages with versions). |
| Experiment Setup | Yes | In the prediction of relationship, we empirically set (si, s) = 3, (pi, p) = 3, (oi, o) = 3, a momentum of 0.9, α = r = 0.005, β = 0.2, a weight decay of 0.05 for the VRD dataset, and α = r = 0.1, β = 0.3, a weight decay of 0.001 for the VG dataset. |