@inproceedings{zhao:26014:sign-lang:lrec,
  author    = {Zhao, Mingyu and Yang, Zhanfu and Zhou, Yang and Xia, Zhaoyang and Jin, Can and He, Xiaoxiao and Lin, Shuhang and Neidle, Carol and Metaxas, Dimitris},
  title     = {Continuous Sign Language Recognition using Multimodal Input and Handshape-aware Boundary Detection},
  pages     = {501--512},
  editor    = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Mesch, Johanna and Schulder, Marc},
  booktitle = {Proceedings of the {LREC2026} 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion},
  maintitle = {15th International Conference on Language Resources and Evaluation ({LREC} 2026)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Palma, Mallorca, Spain},
  day       = {16},
  month     = may,
  year      = {2026},
  isbn      = {978-2-493814-82-1},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/26014.html},
  abstract  = {This paper employs a multimodal approach for continuous sign recognition by first using ML for detecting the start and end frames of signs in videos of American Sign Language (ASL) sentences, and then by recognizing the segmented signs. For improved robustness, we use 3D skeletal features extracted from sign language videos to take into account the convergence of sign properties and their dynamics that tend to cluster at sign boundaries. Another focus of this paper is the incorporation of information from 3D hand configuration for boundary detection. To detect handshapes normally expected at the beginning and end of signs, we pretrain a handshape classifier for detection of 87 linguistically defined canonical handshape categories using a dataset that we created by integrating and normalizing several existing datasets. A multimodal fusion module is then used to unify the pretrained sign video segmentation framework and handshape classification models. Finally, the estimated boundaries are used for sign recognition, where the recognition model is trained on a large database containing both citation-form isolated signs and signs pre-segmented (based on manual annotations) from continuous signing---as such signs often differ a bit in certain respects. We evaluate our method on the ASLLRP corpus and demonstrate significant improvements over previous work.}
}

@inproceedings{xia:24014:sign-lang:lrec,
  author    = {Xia, Zhaoyang and Zhou, Yang and Han, Ligong and Neidle, Carol and Metaxas, Dimitris},
  title     = {Diffusion Models for Sign Language Video Anonymization},
  pages     = {395--407},
  editor    = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Mesch, Johanna and Schulder, Marc},
  booktitle = {Proceedings of the {LREC-COLING} 2024 11th Workshop on the Representation and Processing of Sign Languages: Evaluation of Sign Language Resources},
  maintitle = {2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation ({LREC-COLING} 2024)},
  publisher = {{ELRA Language Resources Association (ELRA) and the International Committee on Computational Linguistics (ICCL)}},
  address   = {Torino, Italy},
  day       = {25},
  month     = may,
  year      = {2024},
  isbn      = {978-2-493814-30-2},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/24014.html},
  abstract  = {Since American Sign Language (ASL) has no standard written form, Deaf signers frequently share videos in order to communicate in their native language. However, this does not preserve privacy. Since critical linguistic information is transmitted through facial expressions, the face cannot be obscured. While signers have expressed interest, for a variety of applications, in sign language video anonymization that would effectively preserve linguistic content, attempts to develop such technology have had limited success and generally require pose estimation that cannot be readily carried out in the wild. To address current limitations, our research introduces DiffSLVA, a novel methodology that uses pre-trained large-scale diffusion models for text-guided sign language video anonymization. We incorporate ControlNet, which leverages low-level image features such as HED (Holistically-Nested Edge Detection) edges, to circumvent the need for pose estimation. Additionally, we develop a specialized module to capture linguistically essential facial expressions. We then combine the above methods to achieve anonymization that preserves the essential linguistic content of the original signer. This innovative methodology makes possible, for the first time, sign language video anonymization that could be used for real-world applications, which would offer significant benefits to the Deaf and Hard-of-Hearing communities.}
}

@inproceedings{zhou:24015:sign-lang:lrec,
  author    = {Zhou, Yang and Xia, Zhaoyang and Chen, Yuxiao and Neidle, Carol and Metaxas, Dimitris},
  title     = {A Multimodal Spatio-Temporal {GCN} Model with Enhancements for Isolated Sign Recognition},
  pages     = {408--419},
  editor    = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Mesch, Johanna and Schulder, Marc},
  booktitle = {Proceedings of the {LREC-COLING} 2024 11th Workshop on the Representation and Processing of Sign Languages: Evaluation of Sign Language Resources},
  maintitle = {2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation ({LREC-COLING} 2024)},
  publisher = {{ELRA Language Resources Association (ELRA) and the International Committee on Computational Linguistics (ICCL)}},
  address   = {Torino, Italy},
  day       = {25},
  month     = may,
  year      = {2024},
  isbn      = {978-2-493814-30-2},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/24015.html},
  abstract  = {We propose a multimodal network using skeletons and handshapes as input to recognize individual signs and detect their boundaries in American Sign Language (ASL) videos. Our method integrates a spatio-temporal Graph Convolutional Network (GCN) architecture to estimate human skeleton keypoints; it uses a late-fusion approach for both forward and backward processing of video streams. Our (core) method is designed for the extraction---and analysis of features from---ASL videos, to enhance accuracy and efficiency of recognition of individual signs. A Gating module based on per-channel multi-layer convolutions is employed to evaluate significant frames for recognition of isolated signs. Additionally, an auxiliary multimodal branch network, integrated with a transformer, is designed to estimate the linguistic start and end frames of an isolated sign within a video clip. We evaluated performance of our approach on multiple datasets that include isolated, citation-form signs and signs pre-segmented from continuous signing based on linguistic annotations of start and end points of signs within sentences. We have achieved very promising results when using both types of sign videos combined for training, with overall sign recognition accuracy of 80.8{\%} Top-1 and 95.2{\%} Top-5 for citation-form signs, and 80.4{\%} Top-1 and 93.0{\%} Top-5 for signs pre-segmented from continuous signing.}
}

@inproceedings{xia:22038:sign-lang:lrec,
  author    = {Xia, Zhaoyang and Chen, Yuxiao and Zhangli, Qilong and Huenerfauth, Matt and Neidle, Carol and Metaxas, Dimitris},
  title     = {Sign Language Video Anonymization},
  pages     = {202--211},
  editor    = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc},
  booktitle = {Proceedings of the {LREC2022} 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources},
  maintitle = {13th International Conference on Language Resources and Evaluation ({LREC} 2022)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Marseille, France},
  day       = {25},
  month     = jun,
  year      = {2022},
  isbn      = {979-10-95546-86-3},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/22038.html},
  abstract  = {Deaf signers who wish to communicate in their native language frequently share videos on the Web. However, videos cannot preserve privacy---as is often desirable for discussion of sensitive topics---since both hands and face convey critical linguistic information and therefore cannot be obscured without degrading communication. Deaf signers have expressed interest in video anonymization that would preserve linguistic content. However, attempts to develop such technology have thus far shown limited success. We are developing a new method for such anonymization, with input from ASL signers. We modify a motion-based image animation model to generate high-resolution videos with the signer identity changed, but with the preservation of linguistically significant motions and facial expressions. An asymmetric encoder-decoder structured image generator is used to generate the high-resolution target frame from the low-resolution source frame based on the optical flow and confidence map. We explicitly guide the model to attain a clear generation of hands and faces by using bounding boxes to improve the loss computation. FID and KID scores are used for the evaluation of the realism of the generated frames. This technology shows great potential for practical applications to benefit deaf signers.}
}