# SF-Net: Structured Feature Network for Continuous Sign Language Recognition

arXiv: https://arxiv.org/abs/1908.01341 · HTML: https://mlsanity.com/paper/1908.01341/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2019.

## Built on by (most cited first, 20 of 20)

- [Visual Alignment Constraint for Continuous Sign Language Recognition](https://mlsanity.com/paper/2104.02330/) (2021)
- [Continuous Sign Language Recognition with Correlation Network](https://mlsanity.com/paper/2303.03202/) (2023)
- [Self-Emphasizing Network for Continuous Sign Language Recognition](https://mlsanity.com/paper/2211.17081/) (2022)
- [Training Tensor Attention Efficiently: From Cubic to Almost Linear Time](https://mlsanity.com/paper/2405.16411/) (2024)
- [Theoretical Constraints on the Expressive Power of mathsfRoPE-based Tensor Attention Transformers](https://mlsanity.com/paper/2412.18040/) (2024)
- [PiSLTRc: Position-informed Sign Language Transformer with Content-aware Convolution](https://mlsanity.com/paper/2107.12600/) (2021)
- [Self-Sufficient Framework for Continuous Sign Language Recognition](https://mlsanity.com/paper/2303.11771/) (2023)
- [AdaBrowse: Adaptive Video Browser for Efficient Continuous Sign Language Recognition](https://mlsanity.com/paper/2308.08327/) (2023)
- [Signing Outside the Studio: Benchmarking Background Robustness for Continuous Sign Language Recognition](https://mlsanity.com/paper/2211.00448/) (2022)
- [Multi-Scale Local-Temporal Similarity Fusion for Continuous Sign Language Recognition](https://mlsanity.com/paper/2107.12762/) (2021)
- [Multi-View Spatial-Temporal Network for Continuous Sign Language Recognition](https://mlsanity.com/paper/2204.08747/) (2022)
- [CorrNet+: Sign Language Recognition and Translation via Spatial-Temporal Correlation](https://mlsanity.com/paper/2404.11111/) (2024)
- [Isharah: A Large-Scale Multi-Scene Dataset for Continuous Sign Language Recognition](https://mlsanity.com/paper/2506.03615/) (2025)
- [TCNet: Continuous Sign Language Recognition from Trajectories and Correlated Regions](https://mlsanity.com/paper/2403.11818/) (2024)
- [Multi-scale temporal network for continuous sign language recognition](https://mlsanity.com/paper/2204.03864/) (2022)
- [Improving Continuous Sign Language Recognition with Adapted Image Models](https://mlsanity.com/paper/2404.08226/) (2024)
- [Time and Memory Trade-off of KV-Cache Compression in Tensor Transformer Decoding](https://mlsanity.com/paper/2503.11108/) (2025)
- [Continuous sign language recognition based on cross-resolution knowledge distillation](https://mlsanity.com/paper/2303.06820/) (2023)
- [Bridging the Gap Between Semantics and Reconstruction:Unifying Sign Language Translation and Production](https://mlsanity.com/paper/2608.09045/) (2026)
- [OneSign: Unifying Sign Language Understanding Tasks with One Model](https://mlsanity.com/paper/2609.33090/) (2026)

## Abstract (arXiv)

Continuous sign language recognition (SLR) aims to translate a signing sequence into a sentence. It is very challenging as sign language is rich in vocabulary, while many among them contain similar gestures and motions. Moreover, it is weakly supervised as the alignment of signing glosses is not available. In this paper, we propose Structured Feature Network (SF-Net) to address these challenges by effectively learn multiple levels of semantic information in the data. The proposed SF-Net extracts features in a structured manner and gradually encodes information at the frame level, the gloss level and the sentence level into the feature representation. The proposed SF-Net can be trained end-to-end without the help of other models or pre-training. We tested the proposed SF-Net on two large scale public SLR datasets collected from different continuous SLR scenarios. Results show that the proposed SF-Net clearly outperforms previous sequence level supervision based methods in terms of both accuracy and adaptability.