Natural Language-Assisted Sign Language Recognition (2303.12080v1)

Abstract: Sign languages are visual languages which convey information by signers' handshape, facial expression, body movement, and so forth. Due to the inherent restriction of combinations of these visual ingredients, there exist a significant number of visually indistinguishable signs (VISigns) in sign languages, which limits the recognition capacity of vision neural networks. To mitigate the problem, we propose the Natural Language-Assisted Sign Language Recognition (NLA-SLR) framework, which exploits semantic information contained in glosses (sign labels). First, for VISigns with similar semantic meanings, we propose language-aware label smoothing by generating soft labels for each training sign whose smoothing weights are computed from the normalized semantic similarities among the glosses to ease training. Second, for VISigns with distinct semantic meanings, we present an inter-modality mixup technique which blends vision and gloss features to further maximize the separability of different signs under the supervision of blended labels. Besides, we also introduce a novel backbone, video-keypoint network, which not only models both RGB videos and human body keypoints but also derives knowledge from sign videos of different temporal receptive fields. Empirically, our method achieves state-of-the-art performance on three widely-adopted benchmarks: MSASL, WLASL, and NMFs-CSL. Codes are available at https://github.com/FangyunWei/SLRT.

• The paper introduces a framework that leverages natural language semantics from gloss data to differentiate visually similar sign gestures.
• It employs a language-aware label smoothing technique that generates soft labels based on normalized semantic similarities, enhancing model training.
• By combining inter-modality mixup with a novel video-keypoint network, the framework achieves state-of-the-art accuracy across multiple sign language datasets.

An Examination of Natural Language-Assisted Sign Language Recognition

The paper "Natural Language-Assisted Sign Language Recognition" introduces an innovative framework designed to enhance the recognition of sign languages through the integration of natural language information, particularly focusing on visually indistinguishable signs (VISigns). The authors identify that existing vision neural networks face challenges in distinguishing VISigns due to limited visual cues or overlapping characteristics among various signs.

Key Contributions

1. Natural Language Integration: The proposed framework, termed Natural Language-Assisted Sign Language Recognition (NLA-SLR), leverages semantic information found in glosses or sign labels. This linguistic information, often overlooked in prior SLR models, can be crucial in differentiating signs that appear visually similar.
2. Language-Aware Label Smoothing: For VISigns sharing semantic meanings, a novel language-aware label smoothing approach is introduced. This technique generates soft labels during the training phase, where smoothing weights are computed from normalized semantic similarities among glosses, providing a more nuanced reflection of inter-sign similarities than traditional one-hot labels.
3. Inter-Modality Mixup: To maximize the separability of VISigns with distinct semantic meanings, the paper proposes an inter-modality mixup strategy. This technique combines vision and gloss features to increase semantic distance in the feature space, thereby improving model differentiation capabilities.
4. Advanced Backbone Architecture: A new video-keypoint network (VKNet) is developed. This architecture models both RGB video data and human body keypoints, allowing for better capture of the multimodal nature of sign languages. It combines information from videos of differing temporal receptive fields, capturing dynamics across various temporal scales.

Experimental Results

The NLA-SLR framework demonstrates state-of-the-art performance across several benchmarks, including MSASL, WLASL, and NMFs-CSL. Notably, the framework surpasses prior methods that also leveraged additional data sources. The paper provides compelling empirical evidence that integrating fastText-derived gloss semantic similarities into the training process significantly boosts recognition rates, as seen with improvements in top-1 accuracy across these datasets.

Implications and Future Work

Theoretically, the integration of natural language data into SLR models represents a significant shift, emphasizing the overlooked potential of semantic information in enhancing computer vision tasks. Practically, the successes observed suggest broad applicability across diverse sign language recognition systems, potentially extending to domains where fine-grained classification within a semantically rich feature space is required.

Moving forward, research could explore extending this approach beyond sign language recognition, potentially adapting it to scenarios where multimodal interactions and semantic-laden labels exist. Additionally, while fastText provides a strong baseline for word representations, exploring other advanced LLMs like BERT or word2vec variants may yield further performance enhancements, although with increased computational overhead.

Conclusion

The NLA-SLR framework offers a methodologically sound approach to addressing the challenge of visually indistinguishable signs in sign language recognition. By cleverly leveraging semantic information inherent in natural language, the paper advances both theoretical understanding and practical capability within the field of SLR. This approach highlights the significant, untapped potential of hybrid models that blend linguistic insights with visual recognition tasks, pointing towards a promising avenue for future research innovation in artificial intelligence.