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..
TD²-Net: Toward Denoising and Debiasing for Video Scene Graph Generation
Authors: Xin Lin, Chong Shi, Yibing Zhan, Zuopeng Yang, Yaqi Wu, Dacheng Tao
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Systematic experimental results demonstrate the superiority of our proposed TD2-Net over existing state-of-the-art approaches on Action Genome databases. |
| Researcher Affiliation | Collaboration | Xin Lin1*, Chong Shi1, Yibing Zhan2, Zuopeng Yang1*, Yaqi Wu1, Dacheng Tao3 1 Guangzhou University 2 JD Explore Academy 3 The University of Sydney |
| Pseudocode | No | The paper describes its methods in detail using natural language and mathematical equations, but it does not include a dedicated 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing its source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | Our experiments are conducted on the AG dataset (Ji et al. 2020), which is the benchmark dataset of dynamic scene graph generation. |
| Dataset Splits | No | The paper mentions training for 10 epochs and using a batch size of 1, and evaluates on the AG dataset under 'With Constraints' and 'No Constraints' settings. However, it does not explicitly provide specific training/validation/test dataset split percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU or CPU models, memory, or cloud instance types. |
| Software Dependencies | No | The paper mentions using Faster R-CNN, Res Net-101 as backbone, and Adam W optimizer, but it does not specify version numbers for these software components or any other libraries. |
| Experiment Setup | Yes | During training, we utilize the Adam W optimizer (Loshchilov and Hutter 2017) with an initial learning rate of 1e 5 and a batch size of 1. The model is trained for 10 epochs. Additionally, we apply gradient clipping, restricting the gradients to a maximum norm of 5. In the Eq. (3) and Eq. (4), we set parameters M = N = 3. |