137 citations · 328 across the 15 of their papers we have counts for
15 papers
Cross-Modal Consistency Learning for Sign Language Recognition
Kepeng Wu, Zecheng Li, Hezhen Hu +2
Pre-training has been proven to be effective in boosting the performance of Isolated Sign Language Recognition (ISLR). Existing pre-training methods solely focus on the compact pos…
Uni-Sign: Toward Unified Sign Language Understanding at Scale
Zecheng Li, Wengang Zhou, Weichao Zhao +3
Sign language pre-training has gained increasing attention for its ability to enhance performance across various sign language understanding (SLU) tasks. However, existing methods…
Scaling up Multimodal Pre-training for Sign Language Understanding
Wengang Zhou, Weichao Zhao, Hezhen Hu +2
Sign language serves as the primary meaning of communication for the deaf-mute community. Different from spoken language, it commonly conveys information by the collaboration of ma…
Self-Supervised Representation Learning with Spatial-Temporal Consistency for Sign Language Recognition
Weichao Zhao, Wengang Zhou, Hezhen Hu +2
Recently, there have been efforts to improve the performance in sign language recognition by designing self-supervised learning methods. However, these methods capture limited info…
MASA: Motion-aware Masked Autoencoder with Semantic Alignment for Sign Language Recognition
Weichao Zhao, Hezhen Hu, Wengang Zhou +3
Sign language recognition (SLR) has long been plagued by insufficient model representation capabilities. Although current pre-training approaches have alleviated this dilemma to so…
PersonMAE: Person Re-Identification Pre-Training with Masked AutoEncoders
Hezhen Hu, Xiaoyi Dong, Jianmin Bao +4
Pre-training is playing an increasingly important role in learning generic feature representation for Person Re-identification (ReID). We argue that a high-quality ReID representat…
Sign Language Translation with Iterative Prototype
Huijie Yao, Wengang Zhou, Hao Feng +3
This paper presents IP-SLT, a simple yet effective framework for sign language translation (SLT). Our IP-SLT adopts a recurrent structure and enhances the semantic representation (…
Exploiting Spatial-Temporal Context for Interacting Hand Reconstruction on Monocular RGB Video
Weichao Zhao, Hezhen Hu, Wengang Zhou +2
Reconstructing interacting hands from monocular RGB data is a challenging task, as it involves many interfering factors, e.g. self- and mutual occlusion and similar textures. Previ…
SignBERT+: Hand-model-aware Self-supervised Pre-training for Sign Language Understanding
Hezhen Hu, Weichao Zhao, Wengang Zhou +1
Hand gesture serves as a crucial role during the expression of sign language. Current deep learning based methods for sign language understanding (SLU) are prone to over-fitting du…
BEST: BERT Pre-Training for Sign Language Recognition with Coupling Tokenization
Weichao Zhao, Hezhen Hu, Wengang Zhou +2
In this work, we are dedicated to leveraging the BERT pre-training success and modeling the domain-specific statistics to fertilize the sign language recognition~(SLR) model. Consi…
DIRE for Diffusion-Generated Image Detection
Zhendong Wang, Jianmin Bao, Wengang Zhou +4
Diffusion models have shown remarkable success in visual synthesis, but have also raised concerns about potential abuse for malicious purposes. In this paper, we seek to build a de…
Hand-Object Interaction Image Generation
Hezhen Hu, Weilun Wang, Wengang Zhou +1
In this work, we are dedicated to a new task, i.e., hand-object interaction image generation, which aims to conditionally generate the hand-object image under the given hand, objec…
SignBERT: Pre-Training of Hand-Model-Aware Representation for Sign Language Recognition
Hezhen Hu, Weichao Zhao, Wengang Zhou +2
Hand gesture serves as a critical role in sign language. Current deep-learning-based sign language recognition (SLR) methods may suffer insufficient interpretability and overfittin…
Boosting Continuous Sign Language Recognition via Cross Modality Augmentation
Junfu Pu, Wengang Zhou, Hezhen Hu +1
Continuous sign language recognition (SLR) deals with unaligned video-text pair and uses the word error rate (WER), i.e., edit distance, as the main evaluation metric. Since it is…
Global-local Enhancement Network for NMFs-aware Sign Language Recognition
Hezhen Hu, Wengang Zhou, Junfu Pu +1
Sign language recognition (SLR) is a challenging problem, involving complex manual features, i.e., hand gestures, and fine-grained non-manual features (NMFs), i.e., facial expressi…