Logos as a Well-Tempered Pre-train for Sign Language Recognition

Abstract: This paper examines two aspects of the isolated sign language recognition (ISLR) task. First, despite the availability of a number of datasets, the amount of data for most individual sign languages is limited. It poses the challenge of cross-language ISLR model training, including transfer learning. Second, similar signs can have different semantic meanings. It leads to ambiguity in dataset labeling and raises the question of the best policy for annotating such signs. To address these issues, this study presents Logos, a novel Russian Sign Language (RSL) dataset, the most extensive ISLR dataset by the number of signers and one of the largest available datasets while also the largest RSL dataset in size and vocabulary. It is shown that a model, pre-trained on the Logos dataset can be used as a universal encoder for other language SLR tasks, including few-shot learning. We explore cross-language transfer learning approaches and find that joint training using multiple classification heads benefits accuracy for the target lowresource datasets the most. The key feature of the Logos dataset is explicitly annotated visually similar sign groups. We show that explicitly labeling visually similar signs improves trained model quality as a visual encoder for downstream tasks. Based on the proposed contributions, we outperform current state-of-the-art results for the WLASL dataset and get competitive results for the AUTSL dataset, with a single stream model processing solely RGB video. The source code, dataset, and pre-trained models are publicly available.

Evaluation of Transfer Learning Approaches in Sign Language Recognition Tasks

The paper titled "Logos as a Well-Tempered Pre-train for Sign Language Recognition" constitutes a significant contribution to the field of Sign Language Recognition (SLR). It offers a critical examination of two specific challenges within isolated sign language recognition (ISLR): cross-lingual model training and the ambiguity introduced by visually similar signs (VSSigns). The authors propose Logos, a novel and extensive Russian Sign Language dataset that serves as the central training platform for this research.

The dataset Logos stands out as the single most extensive ISLR dataset available in terms of both size and signer diversity. With 381 varied signers and 199,668 video samples covering 2,863 gloss classes, it offers comprehensive training material. This richness of data is foundational to the research, highlighting the critical importance of large-scale data availability and diversity in training effective SLR models—especially for languages with limited resources.

The study presents a detailed exploration of cross-language transfer learning methodologies, emphasizing the utility of a pre-trained model on the Logos dataset as a universal encoder across different sign languages. The findings indicate that cross-lingual transfer learning can substantially benefit from a robust initial pre-training on large-scale datasets. In particular, the research demonstrates that simultaneous pre-training and fine-tuning with multiple language-specific classification heads, called multi-dataset co-training, optimizes accuracy for low-resource target datasets more effectively than traditional sequential methods.

The paper also delves into the issue of VSSigns—signs that are visually alike but semantically divergent. By integrating explicit VSSign groupings into the dataset, the researchers improve model performance when acting as a visual encoder for downstream applications. Explicit labeling of these ambiguities aids the model in distinguishing between signs based not solely on manual components but enhanced by non-manual elements such as facial expressions and body movements.

Key numerical results affirm the effectiveness of these methods. Cross-language models trained from Logos outperform existing state-of-the-art (SOTA) models on the American Sign Language dataset WLASL, achieving significant gains despite employing a simplified architecture reliant solely on RGB video input. Moreover, competitive results are attained on the Turkish Sign Language AUTSL dataset. These achievements underscore the potential of using large-scale datasets and advanced training architectures to elevate ISLR model performances.

The implications of this research are manifold. Practically, the advancements propose ways to refine SLR systems for improved accessibility and communication across linguistic and cultural barriers. Theoretically, the findings open avenues for developing universal sign language recognition frameworks that transcend language-specific limitations. Future directions may include leveraging this pre-training strategy to tackle continuous sign language translation (CSLT) tasks, advancing recognition capabilities in real-world scenarios.

In summary, the authors provide substantial insights into the challenges and strategies for cross-language ISLR, spotlighting both the significance of comprehensive datasets like Logos and the benefit of strategic label structuring for improving recognition fidelity. The study's methodologies contribute valuable knowledge to the SLR domain, with potential impacts on broader artificial intelligence research related to language processing and computer vision.