Two-Stream Network for Sign Language Recognition and Translation (2211.01367v2)

Abstract: Sign languages are visual languages using manual articulations and non-manual elements to convey information. For sign language recognition and translation, the majority of existing approaches directly encode RGB videos into hidden representations. RGB videos, however, are raw signals with substantial visual redundancy, leading the encoder to overlook the key information for sign language understanding. To mitigate this problem and better incorporate domain knowledge, such as handshape and body movement, we introduce a dual visual encoder containing two separate streams to model both the raw videos and the keypoint sequences generated by an off-the-shelf keypoint estimator. To make the two streams interact with each other, we explore a variety of techniques, including bidirectional lateral connection, sign pyramid network with auxiliary supervision, and frame-level self-distillation. The resulting model is called TwoStream-SLR, which is competent for sign language recognition (SLR). TwoStream-SLR is extended to a sign language translation (SLT) model, TwoStream-SLT, by simply attaching an extra translation network. Experimentally, our TwoStream-SLR and TwoStream-SLT achieve state-of-the-art performance on SLR and SLT tasks across a series of datasets including Phoenix-2014, Phoenix-2014T, and CSL-Daily. Code and models are available at: https://github.com/FangyunWei/SLRT.

Overview of Two-Stream Network for Sign Language Recognition and Translation

The paper "Two-Stream Network for Sign Language Recognition and Translation" introduces a model designed to improve upon the typical challenges faced in sign language recognition (SLR) and translation (SLT). The authors propose a novel two-stream network architecture, referred to as TwoStream-SLR for recognition tasks, which is adapted into TwoStream-SLT for translation. This architecture aims at mitigating issues stemming from the visual redundancy prevalent in encoding raw RGB video data and enriches feature extraction by incorporating domain-specific knowledge such as keypoints from face, hands, and upper body.

Dual Visual Encoder Architecture

Central to the paper is the dual visual encoder that processes both RGB video data and keypoint sequences. This design leverages two separate streams facilitated by S3D backbones and employs techniques such as a bidirectional lateral connection module for intra-stream communication. This interaction is pivotal as it enables the alleviation of noise from motion blur and other video artifacts while emphasizing salient features pertinent to sign language.

Key Components and Techniques

Several novel architectural features are introduced:

1. Bidirectional Lateral Connection: This promotes effective feature sharing between the two streams, enhancing robustness against typical video noise and redundancy issues.
2. Sign Pyramid Network and Auxiliary Losses: These aim to tackle data scarcity by capturing glosses of varying temporal durations and provide intermediate supervisory signals for more effective learning.
3. Frame-Level Self-Distillation: This technique disseminates learned knowledge across the network by employing averaged prediction outputs as pseudo-targets to improve frame-level prediction accuracy.

Experimental Validation

The TwoStream-SLR and TwoStream-SLT models are evaluated across notable sign language datasets, such as Phoenix-2014, Phoenix-2014T, and CSL-Daily. The empirical results demonstrate state-of-the-art performance, underscoring significant advancements in both SLR and SLT tasks. The findings provide quantitative evidence of the efficacy of employing domain-specific keypoints alongside raw visual data, challenging traditional paradigms which rely solely on RGB inputs.

Implications and Future Directions

The two-stream architecture provides a promising direction for addressing longstanding challenges in SLR and SLT. Incorporating domain-specific keypoints reduces error rates and improves translation accuracy by providing a more nuanced understanding of sign language components. This model not only sets a new standard in terms of accuracy but also opens new avenues for research, such as further refining keypoint estimation techniques and exploring the use of alternative intermediate representations for sign-to-text translation.

Moreover, the integration of visual and skeletal data paves the way for developing more reliable sign language systems that could be adapted for use in real-world applications, including education, accessibility technology, and human-computer interaction interfaces.

In summary, this paper contributes a seminal advancement in the field of automatic sign language processing, encouraging further exploration of multimodal data fusion and interaction for robust language understanding systems.