Fine-Tuning Video Transformers for Word-Level Bangla Sign Language: A Comparative Analysis for Classification Tasks

Sign Language Recognition (SLR) involves the automatic identification and classification of sign gestures from images or video, converting them into text or speech to improve accessibility for the hearing-impaired community. In Bangladesh, Bangla Sign Language (BdSL) serves as the primary mode of communication for many individuals with hearing impairments. This study fine-tunes state-of-the-art video transformer architectures -- VideoMAE, ViViT, and TimeSformer -- on BdSLW60 (arXiv:2402.08635), a small-scale BdSL dataset with 60 frequent signs. We standardized the videos to 30 FPS, resulting in 9,307 user trial clips. To evaluate scalability and robustness, the models were also fine-tuned on BdSLW401 (arXiv:2503.02360), a large-scale dataset with 401 sign classes. Additionally, we benchmark performance against public datasets, including LSA64 and WLASL. Data augmentation techniques such as random cropping, horizontal flipping, and short-side scaling were applied to improve model robustness. To ensure balanced evaluation across folds during model selection, we employed 10-fold stratified cross-validation on the training set, while signer-independent evaluation was carried out using held-out test data from unseen users U4 and U8. Results show that video transformer models significantly outperform traditional machine learning and deep learning approaches. Performance is influenced by factors such as dataset size, video quality, frame distribution, frame rate, and model architecture. Among the models, the VideoMAE variant (MCG-NJU/videomae-base-finetuned-kinetics) achieved the highest accuracies of 95.5% on the frame rate corrected BdSLW60 dataset and 81.04% on the front-facing signs of BdSLW401 -- demonstrating strong potential for scalable and accurate BdSL recognition.

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

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.

SSLR: A Semi-Supervised Learning Method for Isolated Sign Language Recognition

Sign language is the primary communication language for people with disabling hearing loss. Sign language recognition (SLR) systems aim to recognize sign gestures and translate them into spoken language. One of the main challenges in SLR is the scarcity of annotated datasets. To address this issue, we propose a semi-supervised learning (SSL) approach for SLR (SSLR), employing a pseudo-label method to annotate unlabeled samples. The sign gestures are represented using pose information that encodes the signer's skeletal joint points. This information is used as input for the Transformer backbone model utilized in the proposed approach. To demonstrate the learning capabilities of SSL across various labeled data sizes, several experiments were conducted using different percentages of labeled data with varying numbers of classes. The performance of the SSL approach was compared with a fully supervised learning-based model on the WLASL-100 dataset. The obtained results of the SSL model outperformed the supervised learning-based model with less labeled data in many cases.

SignRep: Enhancing Self-Supervised Sign Representations

Sign language representation learning presents unique challenges due to the complex spatio-temporal nature of signs and the scarcity of labeled datasets. Existing methods often rely either on models pre-trained on general visual tasks, that lack sign-specific features, or use complex multimodal and multi-branch architectures. To bridge this gap, we introduce a scalable, self-supervised framework for sign representation learning. We leverage important inductive (sign) priors during the training of our RGB model. To do this, we leverage simple but important cues based on skeletons while pretraining a masked autoencoder. These sign specific priors alongside feature regularization and an adversarial style agnostic loss provide a powerful backbone. Notably, our model does not require skeletal keypoints during inference, avoiding the limitations of keypoint-based models during downstream tasks. When finetuned, we achieve state-of-the-art performance for sign recognition on the WLASL, ASL-Citizen and NMFs-CSL datasets, using a simpler architecture and with only a single-modality. Beyond recognition, our frozen model excels in sign dictionary retrieval and sign translation, surpassing standard MAE pretraining and skeletal-based representations in retrieval. It also reduces computational costs for training existing sign translation models while maintaining strong performance on Phoenix2014T, CSL-Daily and How2Sign.

Training Strategies for Isolated Sign Language Recognition

This paper introduces a comprehensive model training pipeline for Isolated Sign Language Recognition (ISLR) designed to accommodate the distinctive characteristics and constraints of the Sign Language (SL) domain. The constructed pipeline incorporates carefully selected image and video augmentations to tackle the challenges of low data quality and varying sign speeds. Including an additional regression head combined with IoU-balanced classification loss enhances the model's awareness of the gesture and simplifies capturing temporal information. Extensive experiments demonstrate that the developed training pipeline easily adapts to different datasets and architectures. Additionally, the ablation study shows that each proposed component expands the potential to consider ISLR task specifics. The presented strategies improve recognition performance on a broad set of ISLR benchmarks. Moreover, we achieved a state-of-the-art result on the WLASL and Slovo benchmarks with 1.63% and 14.12% improvements compared to the previous best solution, respectively.

Hierarchical Windowed Graph Attention Network and a Large Scale Dataset for Isolated Indian Sign Language Recognition

Automatic Sign Language (SL) recognition is an important task in the computer vision community. To build a robust SL recognition system, we need a considerable amount of data which is lacking particularly in Indian sign language (ISL). In this paper, we propose a large-scale isolated ISL dataset and a novel SL recognition model based on skeleton graph structure. The dataset covers 2,002 daily used common words in the deaf community recorded by 20 (10 male and 10 female) deaf adult signers (contains 40033 videos). We propose a SL recognition model namely Hierarchical Windowed Graph Attention Network (HWGAT) by utilizing the human upper body skeleton graph structure. The HWGAT tries to capture distinctive motions by giving attention to different body parts induced by the human skeleton graph structure. The utility of the proposed dataset and the usefulness of our model are evaluated through extensive experiments. We pre-trained the proposed model on the proposed dataset and fine-tuned it across different sign language datasets further boosting the performance of 1.10, 0.46, 0.78, and 6.84 percentage points on INCLUDE, LSA64, AUTSL and WLASL respectively compared to the existing state-of-the-art skeleton-based models.

Sign Language Production with Latent Motion Transformer

Sign Language Production (SLP) is the tough task of turning sign language into sign videos. The main goal of SLP is to create these videos using a sign gloss. In this research, we've developed a new method to make high-quality sign videos without using human poses as a middle step. Our model works in two main parts: first, it learns from a generator and the video's hidden features, and next, it uses another model to understand the order of these hidden features. To make this method even better for sign videos, we make several significant improvements. (i) In the first stage, we take an improved 3D VQ-GAN to learn downsampled latent representations. (ii) In the second stage, we introduce sequence-to-sequence attention to better leverage conditional information. (iii) The separated two-stage training discards the realistic visual semantic of the latent codes in the second stage. To endow the latent sequences semantic information, we extend the token-level autoregressive latent codes learning with perceptual loss and reconstruction loss for the prior model with visual perception. Compared with previous state-of-the-art approaches, our model performs consistently better on two word-level sign language datasets, i.e., WLASL and NMFs-CSL.

Human Part-wise 3D Motion Context Learning for Sign Language Recognition

In this paper, we propose P3D, the human part-wise motion context learning framework for sign language recognition. Our main contributions lie in two dimensions: learning the part-wise motion context and employing the pose ensemble to utilize 2D and 3D pose jointly. First, our empirical observation implies that part-wise context encoding benefits the performance of sign language recognition. While previous methods of sign language recognition learned motion context from the sequence of the entire pose, we argue that such methods cannot exploit part-specific motion context. In order to utilize part-wise motion context, we propose the alternating combination of a part-wise encoding Transformer (PET) and a whole-body encoding Transformer (WET). PET encodes the motion contexts from a part sequence, while WET merges them into a unified context. By learning part-wise motion context, our P3D achieves superior performance on WLASL compared to previous state-of-the-art methods. Second, our framework is the first to ensemble 2D and 3D poses for sign language recognition. Since the 3D pose holds rich motion context and depth information to distinguish the words, our P3D outperformed the previous state-of-the-art methods employing a pose ensemble.

Learnt Contrastive Concept Embeddings for Sign Recognition

In natural language processing (NLP) of spoken languages, word embeddings have been shown to be a useful method to encode the meaning of words. Sign languages are visual languages, which require sign embeddings to capture the visual and linguistic semantics of sign. Unlike many common approaches to Sign Recognition, we focus on explicitly creating sign embeddings that bridge the gap between sign language and spoken language. We propose a learning framework to derive LCC (Learnt Contrastive Concept) embeddings for sign language, a weakly supervised contrastive approach to learning sign embeddings. We train a vocabulary of embeddings that are based on the linguistic labels for sign video. Additionally, we develop a conceptual similarity loss which is able to utilise word embeddings from NLP methods to create sign embeddings that have better sign language to spoken language correspondence. These learnt representations allow the model to automatically localise the sign in time. Our approach achieves state-of-the-art keypoint-based sign recognition performance on the WLASL and BOBSL datasets.

Isolated Sign Language Recognition based on Tree Structure Skeleton Images

Sign Language Recognition (SLR) systems aim to be embedded in video stream platforms to recognize the sign performed in front of a camera. SLR research has taken advantage of recent advances in pose estimation models to use skeleton sequences estimated from videos instead of RGB information to predict signs. This approach can make HAR-related tasks less complex and more robust to diverse backgrounds, lightning conditions, and physical appearances. In this work, we explore the use of a spatio-temporal skeleton representation such as Tree Structure Skeleton Image (TSSI) as an alternative input to improve the accuracy of skeleton-based models for SLR. TSSI converts a skeleton sequence into an RGB image where the columns represent the joints of the skeleton in a depth-first tree traversal order, the rows represent the temporal evolution of the joints, and the three channels represent the (x, y, z) coordinates of the joints. We trained a DenseNet-121 using this type of input and compared it with other skeleton-based deep learning methods using a large-scale American Sign Language (ASL) dataset, WLASL. Our model (SL-TSSI-DenseNet) overcomes the state-of-the-art of other skeleton-based models. Moreover, when including data augmentation our proposal achieves better results than both skeleton-based and RGB-based models. We evaluated the effectiveness of our model on the Ankara University Turkish Sign Language (TSL) dataset, AUTSL, and a Mexican Sign Language (LSM) dataset. On the AUTSL dataset, the model achieves similar results to the state-of-the-art of other skeleton-based models. On the LSM dataset, the model achieves higher results than the baseline. Code has been made available at: https://github.com/davidlainesv/SL-TSSI-DenseNet.