Resistance Training Using Prior Bias: Toward Unbiased Scene Graph Generation
Authors: Chao Chen, Yibing Zhan, Baosheng Yu, Liu Liu, Yong Luo, Bo Du212-220
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform extensive experiments on a very popular benchmark, VG150, to demonstrate the effectiveness of our method for the unbiased scene graph generation. |
| Researcher Affiliation | Collaboration | 1 School of Computer Science, Wuhan University 2 JD Explore Academy 3 The University of Sydney |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/Ch Ch1999/RTPB |
| Open Datasets | Yes | We perform extensive experiments on Visual Genome (VG) (Krishna et al. 2016) dataset. |
| Dataset Splits | Yes | The original split only has training set (70%) and test set (30%). We follow (Tang et al. 2020) to sample a 5k validation set for parameter tuning. |
| Hardware Specification | Yes | We perform our experiments using a single NVIDIA V100 GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch (Paszke et al. 2019)' but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | For the DTrans, the number of object encoder layers is no = 4 and the number of relationship encoder layers is nr = 2. For the proposed resistance bias, we use a = 1 and ϵ = 0.001 if not otherwise stated. ... We train the DTrans model for 18000 iterations with batch size 16. |