mlsanityData to 7 Oct 2026

Self-Sufficient Framework for Continuous Sign Language Recognition

arXiv 2303.11771 ↗First posted 21 Mar 2023cs.CVcited by 11

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Abstract (arXiv)

The goal of this work is to develop self-sufficient framework for Continuous Sign Language Recognition (CSLR) that addresses key issues of sign language recognition. These include the need for complex multi-scale features such as hands, face, and mouth for understanding, and absence of frame-level annotations. To this end, we propose (1) Divide and Focus Convolution (DFConv) which extracts both manual and non-manual features without the need for additional networks or annotations, and (2) Dense Pseudo-Label Refinement (DPLR) which propagates non-spiky frame-level pseudo-labels by combining the ground truth gloss sequence labels with the predicted sequence. We demonstrate that our model achieves state-of-the-art performance among RGB-based methods on large-scale CSLR benchmarks, PHOENIX-2014 and PHOENIX-2014-T, while showing comparable results with better efficiency when compared to other approaches that use multi-modality or extra annotations.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 13 of its 38 references that are in the map. First posted on arXiv 21 Mar 2023.

Builds on

13 of 38 references.

References · 13

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2019Deep High-Resolution Representation Learning for Human Pose Estimationcited by 1,339
  3. 2020Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translationcited by 254
  4. 2020How2Sign: A Large-scale Multimodal Dataset for Continuous American Sign Languagecited by 172
  5. 2018Video-based Sign Language Recognition without Temporal Segmentationcited by 88
  6. 2021Visual Alignment Constraint for Continuous Sign Language Recognitioncited by 79
  7. 2020Spatial-Temporal Multi-Cue Network for Continuous Sign Language Recognitioncited by 74
  8. 2020Fully Convolutional Networks for Continuous Sign Language Recognitioncited by 58
  9. 2020Boosting Continuous Sign Language Recognition via Cross Modality Augmentationcited by 40
  10. 2019SF-Net: Structured Feature Network for Continuous Sign Language Recognitioncited by 20
  11. 2020Dense Relational Image Captioning via Multi-task Triple-Stream Networkscited by 11
  12. 2021ACP++: Action Co-occurrence Priors for Human-Object Interaction Detectioncited by 11
1 more
  1. 2022Signing Outside the Studio: Benchmarking Background Robustness for Continuous Sign Language Recognitioncited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 6; 2026: 3.

11 papers.

  1. 2023SlowFast Network for Continuous Sign Language Recognitioncited by 10
  2. 2025Deep Understanding of Sign Language for Sign to Subtitle Alignmentcited by 6
  3. 2025Isharah: A Large-Scale Multi-Scene Dataset for Continuous Sign Language Recognitioncited by 6
  4. 2025Lost in Translation, Found in Embeddings: Sign Language Translation and Alignmentcited by 6
  5. 2025CLIP-SLA: Parameter-Efficient CLIP Adaptation for Continuous Sign Language Recognitioncited by 4
  6. 2024A Comparative Study of Continuous Sign Language Recognition Techniquescited by 1
  7. 2025Towards Inclusive Communication: A Unified Framework for Generating Spoken Language from Sign, Lip, and Audiocited by 1
  8. 2025USTM: Unified Spatial and Temporal Modeling for Continuous Sign Language Recognitioncited by 1
  9. 2026Stay in your Lane: Role Specific Queries with Overlap Suppression Loss for Dense Video Captioningcited by 1
  10. 2026SMART: MLLM-guided Temporal Alignment for Unifying Sign Language Recognition and Spottingcited by 0
  11. 2026SignMatch: Matching Dictionary Signs to Continuous Sign Language Videocited by 0