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 [1].
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
| 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. |