# A Chinese Continuous Sign Language Dataset Based on Complex Environments

arXiv: https://arxiv.org/abs/2409.11960 · HTML: https://mlsanity.com/paper/2409.11960/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 45 references that are in the map.
- First posted on arXiv 18 Sep 2024.

## Builds on (most cited first, 27 of 27)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [Improving Sign Language Translation with Monolingual Data by Sign Back-Translation](https://mlsanity.com/paper/2105.12397/) (2021)
- [How2Sign: A Large-scale Multimodal Dataset for Continuous American Sign Language](https://mlsanity.com/paper/2008.08143/) (2020)
- [Two-Stream Network for Sign Language Recognition and Translation](https://mlsanity.com/paper/2211.01367/) (2022)
- [Video-based Sign Language Recognition without Temporal Segmentation](https://mlsanity.com/paper/1801.10111/) (2018)
- [Visual Alignment Constraint for Continuous Sign Language Recognition](https://mlsanity.com/paper/2104.02330/) (2021)
- [YouTube-ASL: A Large-Scale, Open-Domain American Sign Language-English Parallel Corpus](https://mlsanity.com/paper/2306.15162/) (2023)
- [Spatial-Temporal Multi-Cue Network for Continuous Sign Language Recognition](https://mlsanity.com/paper/2002.03187/) (2020)
- [SignBERT+: Hand-model-aware Self-supervised Pre-training for Sign Language Understanding](https://mlsanity.com/paper/2305.04868/) (2023)
- [Fully Convolutional Networks for Continuous Sign Language Recognition](https://mlsanity.com/paper/2007.12402/) (2020)
- [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)
- [Frequency Domain Image Translation: More Photo-realistic, Better Identity-preserving](https://mlsanity.com/paper/2011.13611/) (2020)
- [Boosting Continuous Sign Language Recognition via Cross Modality Augmentation](https://mlsanity.com/paper/2010.05264/) (2020)
- [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)
- [Sign Language Translation with Hierarchical Spatio-TemporalGraph Neural Network](https://mlsanity.com/paper/2111.07258/) (2021)
- [Temporal Lift Pooling for Continuous Sign Language Recognition](https://mlsanity.com/paper/2207.08734/) (2022)
- [BosphorusSign22k Sign Language Recognition Dataset](https://mlsanity.com/paper/2004.01283/) (2020)
- [SlowFast Network for Continuous Sign Language Recognition](https://mlsanity.com/paper/2309.12304/) (2023)
- [Multi-Scale Local-Temporal Similarity Fusion for Continuous Sign Language Recognition](https://mlsanity.com/paper/2107.12762/) (2021)
- [A Hong Kong Sign Language Corpus Collected from Sign-interpreted TV News](https://mlsanity.com/paper/2405.00980/) (2024)
- [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)
- [Continuous Sign Language Recognition Based on Motor attention mechanism and frame-level Self-distillation](https://mlsanity.com/paper/2402.19118/) (2024)
- [Improving Continuous Sign Language Recognition with Adapted Image Models](https://mlsanity.com/paper/2404.08226/) (2024)

## Built on by (most cited first, 2 of 2)

- [iLSU-T: an Open Dataset for Uruguayan Sign Language Translation](https://mlsanity.com/paper/2507.21104/) (2025)
- [SignRAG: Unified Retrieval-Augmented Gloss-Free Sign Language Translation](https://mlsanity.com/paper/2610.11371/) (2026)

## Abstract (arXiv)

The current bottleneck in continuous sign language recognition (CSLR) research lies in the fact that most publicly available datasets are limited to laboratory environments or television program recordings, resulting in a single background environment with uniform lighting, which significantly deviates from the diversity and complexity found in real-life scenarios. To address this challenge, we have constructed a new, large-scale dataset for Chinese continuous sign language (CSL) based on complex environments, termed the complex environment - chinese sign language dataset (CE-CSL). This dataset encompasses 5,988 continuous CSL video clips collected from daily life scenes, featuring more than 70 different complex backgrounds to ensure representativeness and generalization capability. To tackle the impact of complex backgrounds on CSLR performance, we propose a time-frequency network (TFNet) model for continuous sign language recognition. This model extracts frame-level features and then utilizes both temporal and spectral information to separately derive sequence features before fusion, aiming to achieve efficient and accurate CSLR. Experimental results demonstrate that our approach achieves significant performance improvements on the CE-CSL, validating its effectiveness under complex background conditions. Additionally, our proposed method has also yielded highly competitive results when applied to three publicly available CSL datasets.