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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Sign-IDD: Iconicity Disentangled Diffusion for Sign Language Production
Authors: Shengeng Tang, Jiayi He, Dan Guo, Yanyan Wei, Feng Li, Richang Hong
AAAI 2025 | Venue PDF | LLM Run Details | Input Tokens: 16,294 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,018 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on PHOENIX14T and USTC-CSL datasets validate the effectiveness of our method. [...] Experiments are conducted using Py Torch on NVIDIA Ge Force RTX 2080 Ti GPUs. |
| Researcher Affiliation | Academia | Shengeng Tang, Jiayi He, Dan Guo, Yanyan Wei*, Feng Li, Richang Hong* School of Computer Science and Information Engineering, Hefei University of Technology EMAIL, EMAIL |
| Pseudocode | No | The paper describes methods using mathematical formulations and descriptive text, but no explicit pseudocode or algorithm blocks are provided. |
| Open Source Code | Yes | Code https://github.com/Na Vi-start/Sign-IDD |
| Open Datasets | Yes | We evaluate the proposed method on two benchmarks: PHOENIX14T (Camgoz et al. 2018) and USTC-CSL (Huang et al. 2018). |
| Dataset Splits | Yes | USTC-CSL encompasses 100 Chinese sign language sentences performed by 50 signers and is divided into 4,000 training instances and 1,000 testing instances (Guo et al. 2018). [...] PHOENIX14T Table 1 provides comparison results of the proposed Sign-IDD with other SOTA methods on PHOENIX14T. As shown in this table, Sign-IDD significantly outperforms other non-diffusion-based approaches, achieving 25.40% and 24.80% BLEU-1 on the DEV and TEST sets, respectively. |
| Hardware Specification | Yes | Experiments are conducted using Py Torch on NVIDIA Ge Force RTX 2080 Ti GPUs. |
| Software Dependencies | No | Experiments are conducted using Py Torch on NVIDIA Ge Force RTX 2080 Ti GPUs. The paper mentions PyTorch but does not specify a version number. |
| Experiment Setup | Yes | The Transformer-based Gloss Encoder is built with 2 layers, 4 heads, and an embedding size of 512. In addition, we set the timesteps t of the diffusion model to 1000 and the number of inferences i to 5. During training, we use the Adam optimizer (Kingma and Ba 2015) and a learning rate of 1 10 3. |