Efficient Spatial-Temporal Correlation Network for Sign Language Recognition and Translation

Core Concepts

The proposed CorrNet+ framework explicitly models human body trajectories across adjacent frames to enhance the comprehension of sign language, achieving new state-of-the-art performance on continuous sign language recognition and sign language translation tasks while significantly reducing computational overhead.

Abstract

The paper introduces CorrNet+, a novel spatial-temporal correlation network for sign language recognition and translation tasks. The key highlights are: CorrNet+ employs two parallel modules - a correlation module and an identification module - to effectively capture the cross-frame interactions and identify informative spatial regions that convey critical information for sign language understanding. The correlation module computes correlation maps between neighboring frames to model human body trajectories, while the identification module dynamically emphasizes the informative regions. This is followed by a temporal attention module to highlight the critical body movements in the whole video. CorrNet+ achieves new state-of-the-art performance on three large-scale continuous sign language recognition benchmarks (PHOENIX2014, PHOENIX2014-T, CSL-Daily) and two sign language translation benchmarks (PHOENIX2014-T, CSL-Daily), outperforming previous methods that rely on resource-intensive pose estimation networks or pre-extracted heatmaps. Compared to the previous CorrNet model, CorrNet+ brings notable performance gain across all benchmarks while halving the computational overhead, achieving a better computation-accuracy trade-off. Comprehensive ablation studies demonstrate the effectiveness of each proposed component in CorrNet+, including the correlation module, identification module and temporal attention module.

Stats

The proposed CorrNet+ model achieves 18.0% and 18.2% word error rate (WER) on the development and test sets of the PHOENIX2014 dataset, respectively, outperforming previous state-of-the-art methods. On the PHOENIX2014-T dataset, CorrNet+ achieves 18.1% and 18.4% WER on the development and test sets for the CSLR task, and 24.1 BLEU-4 and 57.1 Rouge-L F1 on the development and test sets for the SLT task. On the CSL-Daily dataset, CorrNet+ achieves 17.8% and 18.0% WER on the development and test sets for the CSLR task, and 25.3 BLEU-4 and 58.4 Rouge-L F1 on the development and test sets for the SLT task.

Quotes

"CorrNet+ employs two parallel modules to build human body trajectories: a correlation module and an identification module." "The correlation module computes correlation maps between neighboring frames to model human body trajectories, while the identification module dynamically emphasizes the informative regions." "CorrNet+ achieves new state-of-the-art performance on three large-scale continuous sign language recognition benchmarks and two sign language translation benchmarks, outperforming previous methods that rely on resource-intensive pose estimation networks or pre-extracted heatmaps."

Key Insights Distilled From

CorrNet+: Sign Language Recognition and Translation via Spatial-Temporal Correlation

by Lianyu Hu,We... at arxiv.org 04-18-2024

https://arxiv.org/pdf/2404.11111.pdf

Deeper Inquiries

How can the proposed CorrNet+ framework be extended to other human-centric video understanding tasks beyond sign language recognition and translation

The CorrNet+ framework, with its focus on capturing human body trajectories across multiple frames, can be extended to other human-centric video understanding tasks beyond sign language recognition and translation. One potential application is in action recognition, where the framework can be used to model and track body movements to identify and classify different actions. By adapting the correlation-based approach to analyze the spatial-temporal relationships in action sequences, the framework can effectively recognize and understand various human actions in videos. Additionally, the framework can also be applied to gesture recognition tasks, where hand movements and gestures play a crucial role in conveying information. By leveraging the CorrNet+ framework to capture and analyze the spatial-temporal correlations of hand movements, it can accurately recognize and interpret different gestures and hand signals in videos. Overall, the CorrNet+ framework can be a versatile tool for a wide range of human-centric video understanding tasks beyond sign language recognition and translation.

What are the potential limitations of the correlation-based approach in modeling human body trajectories, and how can they be addressed in future work

While the correlation-based approach in modeling human body trajectories offers significant advantages in capturing cross-frame interactions and identifying informative spatial regions, there are potential limitations that need to be addressed in future work. One limitation is the computational complexity associated with computing correlation maps between all spatial patches among consecutive frames, as seen in the initial CorrNet framework. This high computational overhead can hinder the scalability and efficiency of the model, especially when applied to large-scale datasets or real-time applications. Future work could focus on optimizing the correlation calculation process to reduce computational costs without compromising performance. Additionally, the correlation-based approach may struggle with handling occlusions or complex interactions between body parts in crowded or dynamic scenes. Future research could explore advanced techniques to improve the robustness of the correlation-based model in such challenging scenarios, such as incorporating attention mechanisms or hierarchical modeling to better capture complex spatial-temporal relationships.

Given the importance of facial expressions and hand gestures in sign language, how can the CorrNet+ framework be further improved to better capture and integrate these crucial non-manual components

To better capture and integrate crucial non-manual components like facial expressions and hand gestures in sign language recognition, the CorrNet+ framework can be further improved in several ways. One approach is to incorporate multi-modal features, including visual cues from facial expressions and hand gestures, alongside the spatial-temporal correlations of body movements. By integrating these multi-modal features into the correlation-based model, the framework can effectively capture the holistic information conveyed in sign language, enhancing the overall understanding and translation accuracy. Additionally, the framework can benefit from fine-grained feature extraction techniques specifically designed for capturing facial expressions and hand gestures. By incorporating specialized modules or networks for extracting and analyzing these non-manual components, the CorrNet+ framework can improve its ability to recognize and interpret the nuanced details of sign language expressions. Furthermore, exploring advanced fusion strategies, such as attention mechanisms or graph neural networks, can help in effectively combining information from different modalities to enhance the overall performance of the framework in capturing and integrating crucial non-manual components in sign language recognition.