132 research outputs found
Regularized Fine-grained Meta Face Anti-spoofing
Face presentation attacks have become an increasingly critical concern when
face recognition is widely applied. Many face anti-spoofing methods have been
proposed, but most of them ignore the generalization ability to unseen attacks.
To overcome the limitation, this work casts face anti-spoofing as a domain
generalization (DG) problem, and attempts to address this problem by developing
a new meta-learning framework called Regularized Fine-grained Meta-learning. To
let our face anti-spoofing model generalize well to unseen attacks, the
proposed framework trains our model to perform well in the simulated domain
shift scenarios, which is achieved by finding generalized learning directions
in the meta-learning process. Specifically, the proposed framework incorporates
the domain knowledge of face anti-spoofing as the regularization so that
meta-learning is conducted in the feature space regularized by the supervision
of domain knowledge. This enables our model more likely to find generalized
learning directions with the regularized meta-learning for face anti-spoofing
task. Besides, to further enhance the generalization ability of our model, the
proposed framework adopts a fine-grained learning strategy that simultaneously
conducts meta-learning in a variety of domain shift scenarios in each
iteration. Extensive experiments on four public datasets validate the
effectiveness of the proposed method.Comment: Accepted by AAAI 2020. Codes are available at
https://github.com/rshaojimmy/AAAI2020-RFMetaFA
Heterogeneous Federated Learning: State-of-the-art and Research Challenges
Federated learning (FL) has drawn increasing attention owing to its potential
use in large-scale industrial applications. Existing federated learning works
mainly focus on model homogeneous settings. However, practical federated
learning typically faces the heterogeneity of data distributions, model
architectures, network environments, and hardware devices among participant
clients. Heterogeneous Federated Learning (HFL) is much more challenging, and
corresponding solutions are diverse and complex. Therefore, a systematic survey
on this topic about the research challenges and state-of-the-art is essential.
In this survey, we firstly summarize the various research challenges in HFL
from five aspects: statistical heterogeneity, model heterogeneity,
communication heterogeneity, device heterogeneity, and additional challenges.
In addition, recent advances in HFL are reviewed and a new taxonomy of existing
HFL methods is proposed with an in-depth analysis of their pros and cons. We
classify existing methods from three different levels according to the HFL
procedure: data-level, model-level, and server-level. Finally, several critical
and promising future research directions in HFL are discussed, which may
facilitate further developments in this field. A periodically updated
collection on HFL is available at https://github.com/marswhu/HFL_Survey.Comment: 42 pages, 11 figures, and 4 table
Modeling Spatial Relations of Human Body Parts for Indexing and Retrieving Close Character Interactions
Retrieving pre-captured human motion for analyzing and synthesizing virtual character movement have been widely used in Virtual Reality (VR) and interactive computer graphics applications. In this paper, we propose a new human pose representation, called Spatial Relations of Human Body Parts (SRBP), to represent spatial relations between body parts of the subject(s), which intuitively describes how much the body parts are interacting with each other. Since SRBP is computed from the local structure (i.e. multiple body parts in proximity) of the pose instead of the information from individual or pairwise joints as in previous approaches, the new representation is robust to minor variations of individual joint location. Experimental results show that SRBP outperforms the existing skeleton-based motion retrieval and classification approaches on benchmark databases
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