365 research outputs found
Learning Meta Model for Zero- and Few-shot Face Anti-spoofing
Face anti-spoofing is crucial to the security of face recognition systems.
Most previous methods formulate face anti-spoofing as a supervised learning
problem to detect various predefined presentation attacks, which need large
scale training data to cover as many attacks as possible. However, the trained
model is easy to overfit several common attacks and is still vulnerable to
unseen attacks. To overcome this challenge, the detector should: 1) learn
discriminative features that can generalize to unseen spoofing types from
predefined presentation attacks; 2) quickly adapt to new spoofing types by
learning from both the predefined attacks and a few examples of the new
spoofing types. Therefore, we define face anti-spoofing as a zero- and few-shot
learning problem. In this paper, we propose a novel Adaptive Inner-update Meta
Face Anti-Spoofing (AIM-FAS) method to tackle this problem through
meta-learning. Specifically, AIM-FAS trains a meta-learner focusing on the task
of detecting unseen spoofing types by learning from predefined living and
spoofing faces and a few examples of new attacks. To assess the proposed
approach, we propose several benchmarks for zero- and few-shot FAS. Experiments
show its superior performances on the presented benchmarks to existing methods
in existing zero-shot FAS protocols.Comment: Accepted by AAAI202
Cascaded deep monocular 3D human pose estimation with evolutionary training data
End-to-end deep representation learning has achieved remarkable accuracy for
monocular 3D human pose estimation, yet these models may fail for unseen poses
with limited and fixed training data. This paper proposes a novel data
augmentation method that: (1) is scalable for synthesizing massive amount of
training data (over 8 million valid 3D human poses with corresponding 2D
projections) for training 2D-to-3D networks, (2) can effectively reduce dataset
bias. Our method evolves a limited dataset to synthesize unseen 3D human
skeletons based on a hierarchical human representation and heuristics inspired
by prior knowledge. Extensive experiments show that our approach not only
achieves state-of-the-art accuracy on the largest public benchmark, but also
generalizes significantly better to unseen and rare poses. Code, pre-trained
models and tools are available at this HTTPS URL.Comment: Accepted to CVPR 2020 as Oral Presentatio
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Towards Universal Object Detection
Object detection is one of the most important and challenging research topics in computer vision. It is playing an important role in our everyday life and has many applications, e.g. surveillance, autonomous driving, robotics, drone, medical imaging, etc. The ultimate goal of object detection is a universal object detector that can work very well in any case under any condition like human vision system. However, there are multiple challenges on the universality of object detection, e.g. scale-variance, high-quality requirement, domain shift, computational constraint, etc. These will prevent the object detector from being widely used for various scales of objects, critical applications requiring extremely accurate localization, scenarios with changing domain priors, and diverse hardware settings. To address these challenges, multiple solutions have been proposed in this thesis. These include an efficient multi-scale architecture to achieve scale-invariant detection, a robust multi-stage framework effective for high-quality requirement, a cross-domain solution to extend the universality over various domains, and a design of complexity-aware cascades and a novel low-precision network to enhance the universality under different computational constraints. All these efforts have substantially improved the universality of object detection, and the advanced object detector can be applied to broader environments
Deep Learning for Face Anti-Spoofing: A Survey
Face anti-spoofing (FAS) has lately attracted increasing attention due to its
vital role in securing face recognition systems from presentation attacks
(PAs). As more and more realistic PAs with novel types spring up, traditional
FAS methods based on handcrafted features become unreliable due to their
limited representation capacity. With the emergence of large-scale academic
datasets in the recent decade, deep learning based FAS achieves remarkable
performance and dominates this area. However, existing reviews in this field
mainly focus on the handcrafted features, which are outdated and uninspiring
for the progress of FAS community. In this paper, to stimulate future research,
we present the first comprehensive review of recent advances in deep learning
based FAS. It covers several novel and insightful components: 1) besides
supervision with binary label (e.g., '0' for bonafide vs. '1' for PAs), we also
investigate recent methods with pixel-wise supervision (e.g., pseudo depth
map); 2) in addition to traditional intra-dataset evaluation, we collect and
analyze the latest methods specially designed for domain generalization and
open-set FAS; and 3) besides commercial RGB camera, we summarize the deep
learning applications under multi-modal (e.g., depth and infrared) or
specialized (e.g., light field and flash) sensors. We conclude this survey by
emphasizing current open issues and highlighting potential prospects.Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence
(TPAMI
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