no code implementations • ICCV 2023 • Peiqi Jiao, Yuecong Min, Yanan Li, Xiaotao Wang, Lei Lei, Xilin Chen
The co-occurrence signals (e. g., hand shape, facial expression, and lip pattern) play a critical role in Continuous Sign Language Recognition (CSLR).
no code implementations • European Conference on Computer Vision 2022 • Yuecong Min, Peiqi Jiao, Yanan Li, Xiaotao Wang, Lei Lei, Xiujuan Chai, Xilin Chen
The blank class of CTC plays a crucial role in the alignment process and is often considered responsible for the peaky behavior of CTC.
2 code implementations • ICCV 2021 • Yuecong Min, Aiming Hao, Xiujuan Chai, Xilin Chen
Specifically, the proposed VAC comprises two auxiliary losses: one focuses on visual features only, and the other enforces prediction alignment between the feature extractor and the alignment module.
Ranked #10 on Sign Language Recognition on RWTH-PHOENIX-Weather 2014
1 code implementation • ICCV 2021 • Aiming Hao, Yuecong Min, Xilin Chen
Currently, a typical network combination for CSLR includes a visual module, which focuses on spatial and short-temporal information, followed by a contextual module, which focuses on long-temporal information, and the Connectionist Temporal Classification (CTC) loss is adopted to train the network.
1 code implementation • CVPR 2020 • Yuecong Min, Yanxiao Zhang, Xiujuan Chai, Xilin Chen
The proposed PointLSTM combines state information from neighboring points in the past with current features to update the current states by a weight-shared LSTM layer.