Spatial-Temporal Multi-Cue Network for Continuous Sign Language Recognition (2002.03187v1)

Abstract: Despite the recent success of deep learning in continuous sign language recognition (CSLR), deep models typically focus on the most discriminative features, ignoring other potentially non-trivial and informative contents. Such characteristic heavily constrains their capability to learn implicit visual grammars behind the collaboration of different visual cues (i,e., hand shape, facial expression and body posture). By injecting multi-cue learning into neural network design, we propose a spatial-temporal multi-cue (STMC) network to solve the vision-based sequence learning problem. Our STMC network consists of a spatial multi-cue (SMC) module and a temporal multi-cue (TMC) module. The SMC module is dedicated to spatial representation and explicitly decomposes visual features of different cues with the aid of a self-contained pose estimation branch. The TMC module models temporal correlations along two parallel paths, i.e., intra-cue and inter-cue, which aims to preserve the uniqueness and explore the collaboration of multiple cues. Finally, we design a joint optimization strategy to achieve the end-to-end sequence learning of the STMC network. To validate the effectiveness, we perform experiments on three large-scale CSLR benchmarks: PHOENIX-2014, CSL and PHOENIX-2014-T. Experimental results demonstrate that the proposed method achieves new state-of-the-art performance on all three benchmarks.

• The paper presents a Spatial-Temporal Multi-Cue (STMC) network for Continuous Sign Language Recognition (CSLR) that integrates diverse visual cues like hand shape, face, and body posture.
• The STMC network uses a Spatial Multi-Cue module for feature extraction and a Temporal Multi-Cue module with intra- and inter-cue paths for modeling temporal relationships.
• Optimized end-to-end with CTC, the STMC network achieved state-of-the-art results on PHOENIX-2014, CSL, and PHOENIX-2014-T benchmarks, improving recognition accuracy.

Spatial-Temporal Multi-Cue Network for Continuous Sign Language Recognition

The paper presents a sophisticated approach to Continuous Sign Language Recognition (CSLR) through the development of a Spatial-Temporal Multi-Cue (STMC) network. The authors aim to address limitations in existing deep learning models for CSLR which, while successful to some degree, tend to focus on the most discriminative features at the expense of other potentially informative visual cues. They propose a method that enhances the neural network's ability to capture implicit visual grammars by integrating multiple visual cues, such as hand shape, facial expressions, and body posture, into its design.

Overview of Approach

The proposed STMC network features two main modules:

• Spatial Multi-Cue (SMC) Module: This component is responsible for spatial representation and segmentation of visual features, facilitated by a self-contained pose estimation branch. It identifies and extracts the relevant visual cues across different body parts, such as the face and hands, within each video frame.
• Temporal Multi-Cue (TMC) Module: This module focuses on modeling temporal relationships in visual data through two paths. The intra-cue path preserves the distinctiveness of each visual cue along the time axis. In contrast, the inter-cue path explores cooperative dynamics among different cues over time, employing temporal convolutional layers to integrate these features.

To optimize learning, the authors implement a joint optimization strategy using connectionist temporal classification (CTC) allowing end-to-end sequence learning. This framework is validated through empirical testing on three large-scale CSLR benchmarks: PHOENIX-2014, CSL, and PHOENIX-2014-T, achieving new state-of-the-art results on these datasets.

Experimental Results and Implications

The experimental findings reveal superior performance of the STMC network compared to existing models. Most notably, the STMC network outperforms multi-cue sequential parallelism methods on PHOENIX-2014-T and surpasses the traditional HMM-based models as well as contemporary neural architectures that utilize external cues and tools.

The CTC-driven optimization aids in effectively mapping video sequences to the sign gloss sequences, accounting for the temporal variations inherent to sign language communication. The comprehensive multi-cue approach underscores the potential for improved interpretative accuracy, which is critical for practical applications ranging from human-computer interaction interfaces to communication aids for the deaf community.

Future Prospects

By embedding multi-cue learning within the network's architecture, the findings encourage further exploration into deeper integration of diverse sensory data for CSLR. Potential directions include enhancements in cue detection fidelity, extending the multi-cue paradigm to other languages, or integrating sensor data to enrich the visual information. Furthermore, the synergistic approach can stimulate broader innovations in AI-driven sequence-to-sequence models, particularly in areas requiring nuanced recognition capabilities such as action detection, gesture recognition, or emotionally intelligent systems.

In conclusion, this research significantly contributes to the advancement of sign language technology by refining the mechanisms through which neural networks interpret complex, temporally dynamic visual data. This improvement promises to enhance communication tools for the deaf community, forging a path towards more inclusive technologies.