# SlowFast Network for Continuous Sign Language Recognition

arXiv: https://arxiv.org/abs/2309.12304 · HTML: https://mlsanity.com/paper/2309.12304/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 35 references that are in the map.
- First posted on arXiv 21 Sep 2023.

## Builds on (most cited first, 20 of 20)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [A Short Note about Kinetics-600](https://mlsanity.com/paper/1808.01340/) (2018)
- [Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation](https://mlsanity.com/paper/2003.13830/) (2020)
- [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)
- [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)
- [A Simple Multi-Modality Transfer Learning Baseline for Sign Language Translation](https://mlsanity.com/paper/2203.04287/) (2022)
- [Spatial-Temporal Multi-Cue Network for Continuous Sign Language Recognition](https://mlsanity.com/paper/2002.03187/) (2020)
- [Connectionist Temporal Modeling for Weakly Supervised Action Labeling](https://mlsanity.com/paper/1607.08584/) (2016)
- [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)
- [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)
- [Temporal Lift Pooling for Continuous Sign Language Recognition](https://mlsanity.com/paper/2207.08734/) (2022)
- [Self-Sufficient Framework for Continuous Sign Language Recognition](https://mlsanity.com/paper/2303.11771/) (2023)
- [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, 10 of 10)

- [Lost in Translation, Found in Context: Sign Language Translation with Contextual Cues](https://mlsanity.com/paper/2501.09754/) (2025)
- [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)
- [A Chinese Continuous Sign Language Dataset Based on Complex Environments](https://mlsanity.com/paper/2409.11960/) (2024)
- [Towards Inclusive Communication: A Unified Framework for Generating Spoken Language from Sign, Lip, and Audio](https://mlsanity.com/paper/2508.20476/) (2025)
- [POSESTITCH-SLT: Linguistically Inspired Pose-Stitching for End-to-End Sign Language Translation](https://mlsanity.com/paper/2511.00270/) (2025)
- [USTM: Unified Spatial and Temporal Modeling for Continuous Sign Language Recognition](https://mlsanity.com/paper/2512.13415/) (2025)
- [DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognition](https://mlsanity.com/paper/2507.03339/) (2025)
- [Generation of Indian Sign Language Letters, Numbers, and Words](https://mlsanity.com/paper/2508.09522/) (2025)
- [SignMatch: Matching Dictionary Signs to Continuous Sign Language Video](https://mlsanity.com/paper/2609.01886/) (2026)

## Abstract (arXiv)

The objective of this work is the effective extraction of spatial and dynamic features for Continuous Sign Language Recognition (CSLR). To accomplish this, we utilise a two-pathway SlowFast network, where each pathway operates at distinct temporal resolutions to separately capture spatial (hand shapes, facial expressions) and dynamic (movements) information. In addition, we introduce two distinct feature fusion methods, carefully designed for the characteristics of CSLR: (1) Bi-directional Feature Fusion (BFF), which facilitates the transfer of dynamic semantics into spatial semantics and vice versa; and (2) Pathway Feature Enhancement (PFE), which enriches dynamic and spatial representations through auxiliary subnetworks, while avoiding the need for extra inference time. As a result, our model further strengthens spatial and dynamic representations in parallel. We demonstrate that the proposed framework outperforms the current state-of-the-art performance on popular CSLR datasets, including PHOENIX14, PHOENIX14-T, and CSL-Daily.