Recent progress in fine-grained gesture and action classification, and machine translation, point to the possibility of automated sign language recognition becoming a reality. A key stumbling block in making progress towards this goal is a lack of appropriate training data, stemming from the high complexity of sign annotation and a limited supply of qualified annotators. In this work, we introduce a new scalable approach to data collection for sign recognition in continuous videos. We make use of weakly-aligned subtitles for broadcast footage together with a keyword spotting method to automatically localise sign-instances for a vocabulary of 1,000 signs in 1,000 h of video. We make the following contributions: (1) We show how to use mouthing cues from signers to obtain high-quality annotations from video data—the result is the BSL-1K dataset, a collection of British Sign Language (BSL) signs of unprecedented scale; (2) We show that we can use BSL-1K to train strong sign recognition models for co-articulated signs in BSL and that these models additionally form excellent pretraining for other sign languages and benchmarks—we exceed the state of the art on both the MSASL and WLASL benchmarks. Finally, (3) we propose new large-scale evaluation sets for the tasks of sign recognition and sign spotting and provide baselines which we hope will serve to stimulate research in this area.

BSL-1K: Scaling Up Co-articulated Sign Language Recognition Using Mouthing Cues

Sign Language Translation (SLT) first uses a Sign Language Recognition (SLR) system to extract sign language glosses from videos. Then, a translation system generates spoken language translations from the sign language glosses. This paper focuses on the translation system and introduces the STMC-Transformer which improves on the current state-of-the-art by over 5 and 7 BLEU respectively on gloss-to-text and video-to-text translation of the PHOENIX-Weather 2014T dataset. On the ASLG-PC12 corpus, we report an increase of over 16 BLEU. We also demonstrate the problem in current methods that rely on gloss supervision. The video-to-text translation of our STMC-Transformer outperforms translation of GT glosses. This contradicts previous claims that GT gloss translation acts as an upper bound for SLT performance and reveals that glosses are an inefficient representation of sign language. For future SLT research, we therefore suggest an end-to-end training of the recognition and translation models, or using a different sign language annotation scheme.

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Better Sign Language Translation with STMC-Transformer

This work presents a meta study covering around 300 published sign language recognition papers with over 400 experimental results. It includes most papers between the start of the field in 1983 and 2020. Additionally, it covers a fine-grained analysis on over 25 studies that have compared their recognition approaches on RWTH-PHOENIX-Weather 2014, the standard benchmark task of the field. Research in the domain of sign language recognition has progressed significantly in the last decade, reaching a point where the task attracts much more attention than ever before. This study compiles the state of the art in a concise way to help advance the field and reveal open questions. Moreover, all of this meta study's source data is made public, easing future work with it and further expansion. The analyzed papers have been manually labeled with a set of categories. The data reveals many insights, such as, among others, shifts in the field from intrusive to non-intrusive capturing, from local to global features and the lack of non-manual parameters included in medium and larger vocabulary recognition systems. Surprisingly, RWTH-PHOENIX-Weather with a vocabulary of 1080 signs represents the only resource for large vocabulary continuous sign language recognition benchmarking world wide.

Quantitative Survey of the State of the Art in Sign Language Recognition.

Despite existing pioneering works on sign language translation (SLT), there is a non-trivial obstacle, i.e., the limited quantity of parallel sign-text data. To tackle this parallel data bottleneck, we propose a sign back-translation (SignBT) approach, which incorporates massive spoken language texts into SLT training. With a text-to-gloss translation model, we first back-translate the monolingual text to its gloss sequence. Then, the paired sign sequence is generated by splicing pieces from an estimated gloss-to-sign bank at the feature level. Finally, the synthetic parallel data serves as a strong supplement for the end-to-end training of the encoder-decoder SLT framework.To promote the SLT research, we further contribute CSL-Daily, a large-scale continuous SLT dataset. It provides both spoken language translations and gloss-level annotations. The topic revolves around people’s daily lives (e.g., travel, shopping, medical care), the most likely SLT application scenario. Extensive experimental results and analysis of SLT methods are reported on CSL-Daily. With the proposed sign back-translation method, we obtain a substantial improvement over previous state-of-the-art SLT methods.

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Improving Sign Language Translation with Monolingual Data by Sign Back-Translation

Continuous sign language recognition (SLR) is a challenging task that requires learning on both spatial and temporal dimensions of signing frame sequences. Most recent work accomplishes this by using CNN and RNN hybrid networks. However, training these networks is generally non-trivial, and most of them fail in learning unseen sequence patterns, causing an unsatisfactory performance for online recognition. In this paper, we propose a fully convolutional network (FCN) for online SLR to concurrently learn spatial and temporal features from weakly annotated video sequences with only sentence-level annotations given. A gloss feature enhancement (GFE) module is introduced in the proposed network to enforce better sequence alignment learning. The proposed network is end-to-end trainable without any pre-training. We conduct experiments on two large scale SLR datasets. Experiments show that our method for continuous SLR is effective and performs well in online recognition.

Fully Convolutional Networks for Continuous Sign Language Recognition

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.

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

Despite the recent success of deep learning in video-related tasks, 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 in sign videos behind the collaboration of different visual cues (i.e., hand shape, facial expression and body posture). To this end, we approach video-based sign language understanding with multi-cue learning and 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 learns to spatial representation of different cues with a self-contained pose estimation branch. The TMC module models temporal corrections from intra-cue and inter-cue perspectives to explore the collaboration of multiple cues. A joint optimization strategy and a segmented attention mechanism are designed to make the best of multi-cue sources for SL recognition and translation. To validate the effectiveness, we perform experiments on three large-scale sign language 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.

Spatial-Temporal Multi-Cue Network for Sign Language Recognition and Translation

Video-based person re-identification has received considerable attention in recent years due to its significant application in video surveillance. Compared with image-based person re-identification, video-based person re-identification is characterized by a much richer context, which raises the significance of identifying informative regions and fusing the temporal information across frames. In this paper, we propose two relation-guided modules to learn reinforced feature representations for effective re-identification. First, a relation-guided spatial attention (RGSA) module is designed to explore the discriminative regions globally. The weight at each position is determined by its feature as well as the relation features from other positions, revealing the dependence between local and global contents. Based on the adaptively weighted frame-level feature, then, a relation-guided temporal refinement (RGTR) module is proposed to further refine the feature representations across frames. The learned relation information via the RGTR module enables the individual frames to complement each other in an aggregation manner, leading to robust video-level feature representations. Extensive experiments on four prevalent benchmarks verify the state-of-the-art performance of the proposed method.

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Relation-Guided Spatial Attention and Temporal Refinement for Video-Based Person Re-Identification

Through well-designed optimization paradigm and deep neural networks as feature extractor, deep reinforcement learning (DRL) algorithms learn optimal policy on discrete and continuous action space. However, such capability is restricted by the low sampling efficiency. By inspecting the importance of feature extraction in DRL, we find that state feature learning is one of the key obstacles for sampling efficiently. To this end, we propose a new state representation learning scheme with adjacent state consistency loss (ASC loss). The loss is based on the hypothesis that the distance between adjacent states is smaller than that of far apart ones since scenes in videos generally evolve smoothly. We exploit ASC loss as an assistant of RL loss in the training phase to boost the state feature learning, and make evaluation on existing DRL algorithms as well as behavioral cloning algorithm. Experiments on Atari games and MuJoCo continuous control tasks demonstrate the effectiveness of our scheme.

State Representation Learning With Adjacent State Consistency Loss for Deep Reinforcement Learning

Recent years have witnessed the great success of deep reinforcement learning (DRL) on a variety of vision games. Although DNN has demonstrated strong power in representation learning, such capacity is under-explored in most DRL works whose focus is usually on optimization solvers. In fact, we discover that the state feature learning is the main obstacle for further improvement of DRL algorithms. To address this issue, we propose a new state representation learning scheme with our Adjacent State Consistency Loss (ASC Loss). The loss is defined based on the hypothesis that there are fewer changes between adjacent states than that of far apart ones, since scenes in videos generally evolve smoothly. In this paper, we exploit ASC loss as an assistant of RL loss in the training phase to boost the state feature learning. We conduct evaluation on Atari games and MuJoCo continuous control tasks, which demonstrates that our method is superior to OpenAI baselines.

Yun Zhou

Papers

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

Spatial-Temporal Multi-Cue Network for Sign Language Recognition and Translation

Relation-Guided Spatial Attention and Temporal Refinement for Video-Based Person Re-Identification

State Representation Learning With Adjacent State Consistency Loss for Deep Reinforcement Learning

State Representation Learning For Effective Deep Reinforcement Learning