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..
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 | Venue PDF | 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. |