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..
Contrastive Learning for Sign Language Recognition and Translation
Authors: Shiwei Gan, Yafeng Yin, Zhiwei Jiang, Kang Xia, Lei Xie, Sanglu Lu
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on current sign language datasets demonstrate the effectiveness of our approach, which achieves state-of-the-art performance. |
| Researcher Affiliation | Academia | Shiwei Gan , Yafeng Yin , Zhiwei Jiang , Kang Xia , Lei Xie and Sanglu Lu State Key Laboratory for Novel Software Technology, Nanjing University, China EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a concrete access link or explicit statement about the release of source code. |
| Open Datasets | Yes | We test our model on public sign language datasets that currently are often used. (1) Phoenix14T [Camgoz et al., 2018] contains 7096, 519 and 642 samples from 9 signers for training, validation, and testing respectively. (2) The CSL-daily dataset [Zhou et al., 2021a] contains 18401, 1077 and 1176 labeled videos from 10 signers for training, validation and testing respectively. (3) Phoenix14 [Koller et al., 2015] contains 5672, 540, 629 samples from 9 signers for training, validation, and testing respectively and it has a vocabulary of 1295 glosses for CSLR only. |
| Dataset Splits | Yes | We test our model on public sign language datasets that currently are often used. (1) Phoenix14T [Camgoz et al., 2018] contains 7096, 519 and 642 samples from 9 signers for training, validation, and testing respectively. (2) The CSL-daily dataset [Zhou et al., 2021a] contains 18401, 1077 and 1176 labeled videos from 10 signers for training, validation and testing respectively. (3) Phoenix14 [Koller et al., 2015] contains 5672, 540, 629 samples from 9 signers for training, validation, and testing respectively and it has a vocabulary of 1295 glosses for CSLR only. |
| Hardware Specification | Yes | We adopt Adam optimizer with a weight decay of 0.0001 to train our model for 70 epochs on 2 Ge Force RTX 3090 GPUs. |
| Software Dependencies | Yes | Our architecture adopts the components provided by Py Torch 1.11. |
| Experiment Setup | Yes | Training setting. To train our model, we use the following settings for CSLR and SLT. We adopt Adam optimizer with a weight decay of 0.0001 to train our model for 70 epochs on 2 Ge Force RTX 3090 GPUs. The initial learning rate is 0.0001 with a decay factor of 0.5 and the batch size is set to 6. |