# Fully Convolutional Networks for Continuous Sign Language Recognition

arXiv: https://arxiv.org/abs/2007.12402 · HTML: https://mlsanity.com/paper/2007.12402/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2020.

## Built on by (most cited first, 30 of 58)

- [Improving Sign Language Translation with Monolingual Data by Sign Back-Translation](https://mlsanity.com/paper/2105.12397/) (2021)
- [Two-Stream Network for Sign Language Recognition and Translation](https://mlsanity.com/paper/2211.01367/) (2022)
- [Visual Alignment Constraint for Continuous Sign Language Recognition](https://mlsanity.com/paper/2104.02330/) (2021)
- [SignBERT: Pre-Training of Hand-Model-Aware Representation for Sign Language Recognition](https://mlsanity.com/paper/2110.05382/) (2021)
- [SignBERT+: Hand-model-aware Self-supervised Pre-training for Sign Language Understanding](https://mlsanity.com/paper/2305.04868/) (2023)
- [Continuous Sign Language Recognition with Correlation Network](https://mlsanity.com/paper/2303.03202/) (2023)
- [Gloss Attention for Gloss-free Sign Language Translation](https://mlsanity.com/paper/2307.07361/) (2023)
- [CVT-SLR: Contrastive Visual-Textual Transformation for Sign Language Recognition with Variational Alignment](https://mlsanity.com/paper/2303.05725/) (2023)
- [Read and Attend: Temporal Localisation in Sign Language Videos](https://mlsanity.com/paper/2103.16481/) (2021)
- [Self-Emphasizing Network for Continuous Sign Language Recognition](https://mlsanity.com/paper/2211.17081/) (2022)
- [Improving Continuous Sign Language Recognition with Cross-Lingual Signs](https://mlsanity.com/paper/2308.10809/) (2023)
- [Aligning Subtitles in Sign Language Videos](https://mlsanity.com/paper/2105.02877/) (2021)
- [Temporal Lift Pooling for Continuous Sign Language Recognition](https://mlsanity.com/paper/2207.08734/) (2022)
- [Lost in Translation, Found in Context: Sign Language Translation with Contextual Cues](https://mlsanity.com/paper/2501.09754/) (2025)
- [Automatic dense annotation of large-vocabulary sign language videos](https://mlsanity.com/paper/2208.02802/) (2022)
- [A Simple Baseline for Spoken Language to Sign Language Translation with 3D Avatars](https://mlsanity.com/paper/2401.04730/) (2024)
- [A Tale of Two Languages: Large-Vocabulary Continuous Sign Language Recognition from Spoken Language Supervision](https://mlsanity.com/paper/2405.10266/) (2024)
- [Jointly Harnessing Prior Structures and Temporal Consistency for Sign Language Video Generation](https://mlsanity.com/paper/2207.03714/) (2022)
- [Towards Online Continuous Sign Language Recognition and Translation](https://mlsanity.com/paper/2401.05336/) (2024)
- [Sign Language Production with Latent Motion Transformer](https://mlsanity.com/paper/2312.12917/) (2023)
- [PiSLTRc: Position-informed Sign Language Transformer with Content-aware Convolution](https://mlsanity.com/paper/2107.12600/) (2021)
- [Improving Continuous Sign Language Recognition with Consistency Constraints and Signer Removal](https://mlsanity.com/paper/2212.13023/) (2022)
- [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)
- [SlowFast Network for Continuous Sign Language Recognition](https://mlsanity.com/paper/2309.12304/) (2023)
- [Multi-Stream Keypoint Attention Network for Sign Language Recognition and Translation](https://mlsanity.com/paper/2405.05672/) (2024)
- [Signing Outside the Studio: Benchmarking Background Robustness for Continuous Sign Language Recognition](https://mlsanity.com/paper/2211.00448/) (2022)
- [Human Part-wise 3D Motion Context Learning for Sign Language Recognition](https://mlsanity.com/paper/2308.09305/) (2023)
- [A Methodological and Structural Review of Hand Gesture Recognition Across Diverse Data Modalities](https://mlsanity.com/paper/2408.05436/) (2024)
- [Multi-Scale Local-Temporal Similarity Fusion for Continuous Sign Language Recognition](https://mlsanity.com/paper/2107.12762/) (2021)

## Abstract (arXiv)

Continuous sign language recognition (SLR) is a challenging task that requires learning on both spatial and temporal dimensions of signing frame sequences. Most recent work accomplishes this by using CNN and RNN hybrid networks. However, training these networks is generally non-trivial, and most of them fail in learning unseen sequence patterns, causing an unsatisfactory performance for online recognition. In this paper, we propose a fully convolutional network (FCN) for online SLR to concurrently learn spatial and temporal features from weakly annotated video sequences with only sentence-level annotations given. A gloss feature enhancement (GFE) module is introduced in the proposed network to enforce better sequence alignment learning. The proposed network is end-to-end trainable without any pre-training. We conduct experiments on two large scale SLR datasets. Experiments show that our method for continuous SLR is effective and performs well in online recognition.