17 citations · 35 across the 9 of their papers we have counts for
10 papers · 1 filter
LocalAgent: Collaborative Agentic Verification for Fine-Grained Instance-Level Consistency
Jiachen Guo, Xinshan Zhu, Boyun Wang +4
Multimodal large language models (MLLMs) have made rapid progress in general visual understanding, yet they still struggle with fine-grained instance-level consistency verification…
Human4K: A Large-Scale 4K Multi-View Mocap Dataset for Whole-Body 3D Human Reconstruction
Tianshun Han, Ziyu Shi, Lijian Liu +7
Recent advances in 3D human reconstruction have improved overall performance, yet current models still fail in the most challenging real-world scenarios. They often produce unstabl…
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…
PMMTalk: Speech-Driven 3D Facial Animation from Complementary Pseudo Multi-modal Features
Tianshun Han, Shengnan Gui, Yiqing Huang +9
Speech-driven 3D facial animation has improved a lot recently while most related works only utilize acoustic modality and neglect the influence of visual and textual cues, leading…
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…
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…
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…
Decoupling and Recoupling Spatiotemporal Representation for RGB-D-based Motion Recognition
Benjia Zhou, Pichao Wang, Jun Wan +6
Decoupling spatiotemporal representation refers to decomposing the spatial and temporal features into dimension-independent factors. Although previous RGB-D-based motion recognitio…
Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detection
Ajian Liu, Chenxu Zhao, Zitong Yu +11
Face presentation attack detection (PAD) is essential to secure face recognition systems primarily from high-fidelity mask attacks. Most existing 3D mask PAD benchmarks suffer from…
CASIA-SURF: A Large-scale Multi-modal Benchmark for Face Anti-spoofing
Shifeng Zhang, Ajian Liu, Jun Wan +5
Face anti-spoofing is essential to prevent face recognition systems from a security breach. Much of the progresses have been made by the availability of face anti-spoofing benchmar…