Weak Supervision Breakthrough: Cross-Dataset Sign Spotting Framework Based on Broadcast Data Released
By Mr.Xu
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Summary:A new study on arXiv introduces a cross-dataset sign spotting framework based on weak supervision, leveraging loosely aligned broadcast news data to recognize and localize sign language content. The research utilizes the Turkish broadcast corpus TSL-News for pretraining and develops an LLM-assisted normalization method, significantly improving the performance of the sign recognition model. Experimental results show that the method excels in cross-dataset sign detection tasks, enhancing temporal
Background and Challenges
In sign language research for resource-constrained languages, the high cost of dense linguistic labels (such as annotations, temporal boundaries, and sign order) limits progress. Broadcast news offers a practical alternative by pairing continuous signing with spoken language transcripts, but this supervision is weak due to the loose alignment between text and signing. Additionally, morphologically rich languages like Turkish further complicate the task, as the same lexical meaning can appear in various inflected forms, while some derived forms should remain distinct.
Methodology and Innovation
This study proposes a sign spotting framework based on weak supervision, aiming to leverage the loosely aligned information in broadcast data for pretraining and testing the model's performance in cross-dataset sign spotting tasks. The research team developed an LLM-assisted normalization method and compared it with a rule-based morphological lemmatization approach.
Key Experiments and Results
The study pretrains on the new Turkish broadcast corpus TSL-News and evaluates the model on the TSL Spotting Benchmark constructed from the TSL Dictionary corpus. The results show that the LLM-assisted encoder increases the top-5 temporal localization mean IoU from 0.235 to 0.465, with 56.2% of examples reaching at least 0.50 IoU. Furthermore, in the downstream translation task, the same pretraining improves BLEU-4 from 9.60 to 11.04 and ROUGE from 23.48 to 27.43.
Technical Highlights
- Weak Supervision: Utilizes loosely aligned broadcast data for pretraining, reducing reliance on dense linguistic labels.
- LLM-assisted Normalization: Enhances the accuracy of text-sign alignment through an LLM-assisted normalization method.
- Cross-Dataset Applicability: The pretrained model demonstrates strong generalization in cross-dataset sign spotting tasks.
- Downstream Task Improvement: Pretraining not only improves sign spotting accuracy but also enhances the performance of downstream translation tasks.
Industry Impact and Developer Recommendations
This research provides a new technical pathway for sign language studies of resource-constrained languages. Developers can leverage this framework to build more efficient sign recognition systems. Additionally, the method can be extended to other domains that require weak supervision, such as multimodal data processing and cross-lingual information retrieval.
— END —Source: ArXiv NLP/LLM (cs.CL) (2026-08-13)
Tags: #Sign Language Recognition #Weak Supervision #LLMs & Foundation Models #Multimodal AI #Broadcast Data
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