Self-Sufficient Framework for Continuous Sign Language Recognition

• URL: http://arxiv.org/abs/2303.11771v1
• Date: Tue, 21 Mar 2023 11:42:57 GMT
• Title: Self-Sufficient Framework for Continuous Sign Language Recognition
• Authors: Youngjoon Jang, Youngtaek Oh, Jae Won Cho, Myungchul Kim, Dong-Jin Kim, In So Kweon, Joon Son Chung
• Abstract summary: The goal of this work is to develop self-sufficient framework for Continuous 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. We propose Divide and Focus Convolution (DFConv) which extracts both manual and non-manual features without the need for additional networks or annotations. DPLR propagates non-spiky frame-level pseudo-labels by combining the ground truth gloss sequence labels with the predicted sequence.
• Score: 75.60327502570242
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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