activity
20212024
most citedGloss-free Sign Language Translation: Improving from Visual-Language Pretraining

8 citations · 18 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2024

CFPL-FAS: Class Free Prompt Learning for Generalizable Face Anti-spoofing

Ajian Liu, Shuai Xue, Jianwen Gan +5

Domain generalization (DG) based Face Anti-Spoofing (FAS) aims to improve the model's performance on unseen domains. Existing methods either rely on domain labels to align domain-i…

cs.CV2024

Distilling Temporal Knowledge with Masked Feature Reconstruction for 3D Object Detection

Haowen Zheng, Dong Cao, Jintao Xu +4

Striking a balance between precision and efficiency presents a prominent challenge in the bird's-eye-view (BEV) 3D object detection. Although previous camera-based BEV methods achi…

cs.CV20231 cited

Long-Range Grouping Transformer for Multi-View 3D Reconstruction

Liying Yang, Zhenwei Zhu, Xuxin Lin +2

Nowadays, transformer networks have demonstrated superior performance in many computer vision tasks. In a multi-view 3D reconstruction algorithm following this paradigm, self-atten…

cs.CV20238 cited

Gloss-free Sign Language Translation: Improving from Visual-Language Pretraining

Benjia Zhou, Zhigang Chen, Albert Clapés +5

Sign Language Translation (SLT) is a challenging task due to its cross-domain nature, involving the translation of visual-gestural language to text. Many previous methods employ an…

cs.CV20234 cited

FM-ViT: Flexible Modal Vision Transformers for Face Anti-Spoofing

Ajian Liu, Zichang Tan, Zitong Yu +7

The availability of handy multi-modal (i.e., RGB-D) sensors has brought about a surge of face anti-spoofing research. However, the current multi-modal face presentation attack dete…

cs.CV20232 cited

MA-ViT: Modality-Agnostic Vision Transformers for Face Anti-Spoofing

Ajian Liu, Yanyan Liang

The existing multi-modal face anti-spoofing (FAS) frameworks are designed based on two strategies: halfway and late fusion. However, the former requires test modalities consistent…