# Self-Sufficient Framework for Continuous Sign Language Recognition

arXiv: https://arxiv.org/abs/2303.11771 · HTML: https://mlsanity.com/paper/2303.11771/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 38 references that are in the map.
- First posted on arXiv 21 Mar 2023.

## Builds on (most cited first, 13 of 13)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation](https://mlsanity.com/paper/2003.13830/) (2020)
- [How2Sign: A Large-scale Multimodal Dataset for Continuous American Sign Language](https://mlsanity.com/paper/2008.08143/) (2020)
- [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)
- [Spatial-Temporal Multi-Cue Network for Continuous Sign Language Recognition](https://mlsanity.com/paper/2002.03187/) (2020)
- [Fully Convolutional Networks for Continuous Sign Language Recognition](https://mlsanity.com/paper/2007.12402/) (2020)
- [Boosting Continuous Sign Language Recognition via Cross Modality Augmentation](https://mlsanity.com/paper/2010.05264/) (2020)
- [SF-Net: Structured Feature Network for Continuous Sign Language Recognition](https://mlsanity.com/paper/1908.01341/) (2019)
- [Dense Relational Image Captioning via Multi-task Triple-Stream Networks](https://mlsanity.com/paper/2010.03855/) (2020)
- [ACP++: Action Co-occurrence Priors for Human-Object Interaction Detection](https://mlsanity.com/paper/2109.04047/) (2021)
- [Signing Outside the Studio: Benchmarking Background Robustness for Continuous Sign Language Recognition](https://mlsanity.com/paper/2211.00448/) (2022)

## Built on by (most cited first, 11 of 11)

- [SlowFast Network for Continuous Sign Language Recognition](https://mlsanity.com/paper/2309.12304/) (2023)
- [Deep Understanding of Sign Language for Sign to Subtitle Alignment](https://mlsanity.com/paper/2503.03287/) (2025)
- [Isharah: A Large-Scale Multi-Scene Dataset for Continuous Sign Language Recognition](https://mlsanity.com/paper/2506.03615/) (2025)
- [Lost in Translation, Found in Embeddings: Sign Language Translation and Alignment](https://mlsanity.com/paper/2512.08040/) (2025)
- [CLIP-SLA: Parameter-Efficient CLIP Adaptation for Continuous Sign Language Recognition](https://mlsanity.com/paper/2504.01666/) (2025)
- [A Comparative Study of Continuous Sign Language Recognition Techniques](https://mlsanity.com/paper/2406.12369/) (2024)
- [Towards Inclusive Communication: A Unified Framework for Generating Spoken Language from Sign, Lip, and Audio](https://mlsanity.com/paper/2508.20476/) (2025)
- [USTM: Unified Spatial and Temporal Modeling for Continuous Sign Language Recognition](https://mlsanity.com/paper/2512.13415/) (2025)
- [Stay in your Lane: Role Specific Queries with Overlap Suppression Loss for Dense Video Captioning](https://mlsanity.com/paper/2603.11439/) (2026)
- [SMART: MLLM-guided Temporal Alignment for Unifying Sign Language Recognition and Spotting](https://mlsanity.com/paper/2608.25493/) (2026)
- [SignMatch: Matching Dictionary Signs to Continuous Sign Language Video](https://mlsanity.com/paper/2609.01886/) (2026)

## Abstract (arXiv)

The goal of this work is to develop self-sufficient framework for Continuous Sign Language Recognition (CSLR) that addresses key issues of sign language recognition. These include the need for complex multi-scale features such as hands, face, and mouth for understanding, and absence of frame-level annotations. To this end, we propose (1) Divide and Focus Convolution (DFConv) which extracts both manual and non-manual features without the need for additional networks or annotations, and (2) Dense Pseudo-Label Refinement (DPLR) which propagates non-spiky frame-level pseudo-labels by combining the ground truth gloss sequence labels with the predicted sequence. We demonstrate that our model achieves state-of-the-art performance among RGB-based methods on large-scale CSLR benchmarks, PHOENIX-2014 and PHOENIX-2014-T, while showing comparable results with better efficiency when compared to other approaches that use multi-modality or extra annotations.