1 citations · 2 across the 6 of their papers we have counts for
7 papers
Benchmarking Unified Face Attack Detection via Hierarchical Prompt Tuning
Ajian Liu, Haocheng Yuan, Xiao Guo +13
PAD and FFD are proposed to protect face data from physical media-based Presentation Attacks and digital editing-based DeepFakes, respectively. However, isolated training of these…
FA^{3}-CLIP: Frequency-Aware Cues Fusion and Attack-Agnostic Prompt Learning for Unified Face Attack Detection
Yongze Li, Ning Li, Ajian Liu +7
Facial recognition systems are vulnerable to physical (e.g., printed photos) and digital (e.g., DeepFake) face attacks. Existing methods struggle to simultaneously detect physical…
Not All Frame Features Are Equal: Video-to-4D Generation via Decoupling Dynamic-Static Features
Liying Yang, Chen Liu, Zhenwei Zhu +4
Recently, the generation of dynamic 3D objects from a video has shown impressive results. Existing methods directly optimize Gaussians using whole information in frames. However, w…
CRL: Content and Context Representation Learning for Gloss-free Sign Language Translation and Retrieval
Zhigang Chen, Benjia Zhou, Yiqing Huang +6
Sign Language Representation Learning (SLRL) is crucial for a range of sign language-related downstream tasks such as Sign Language Translation (SLT) and Sign Language Retrieval (S…
FeTT: Continual Class Incremental Learning via Feature Transformation Tuning
Sunyuan Qiang, Xuxin Lin, Yanyan Liang +2
Continual learning (CL) aims to extend deep models from static and enclosed environments to dynamic and complex scenarios, enabling systems to continuously acquire new knowledge of…
Open-Vocabulary Object Detection via Neighboring Region Attention Alignment
Sunyuan Qiang, Xianfei Li, Yanyan Liang +3
The nature of diversity in real-world environments necessitates neural network models to expand from closed category settings to accommodate novel emerging categories. In this pape…
Dynamic Feature Learning and Matching for Class-Incremental Learning
Sunyuan Qiang, Yanyan Liang, Jun Wan +1
Class-incremental learning (CIL) has emerged as a means to learn new classes incrementally without catastrophic forgetting of previous classes. Recently, CIL has undergone a paradi…