5,305 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
A derivational rephrasing experiment for question answering
In Knowledge Management, variations in information expressions have proven a
real challenge. In particular, classical semantic relations (e.g. synonymy) do
not connect words with different parts-of-speech. The method proposed tries to
address this issue. It consists in building a derivational resource from a
morphological derivation tool together with derivational guidelines from a
dictionary in order to store only correct derivatives. This resource, combined
with a syntactic parser, a semantic disambiguator and some derivational
patterns, helps to reformulate an original sentence while keeping the initial
meaning in a convincing manner This approach has been evaluated in three
different ways: the precision of the derivatives produced from a lemma; its
ability to provide well-formed reformulations from an original sentence,
preserving the initial meaning; its impact on the results coping with a real
issue, ie a question answering task . The evaluation of this approach through a
question answering system shows the pros and cons of this system, while
foreshadowing some interesting future developments
FSS-1000: A 1000-Class Dataset for Few-Shot Segmentation
Over the past few years, we have witnessed the success of deep learning in
image recognition thanks to the availability of large-scale human-annotated
datasets such as PASCAL VOC, ImageNet, and COCO. Although these datasets have
covered a wide range of object categories, there are still a significant number
of objects that are not included. Can we perform the same task without a lot of
human annotations? In this paper, we are interested in few-shot object
segmentation where the number of annotated training examples are limited to 5
only. To evaluate and validate the performance of our approach, we have built a
few-shot segmentation dataset, FSS-1000, which consists of 1000 object classes
with pixelwise annotation of ground-truth segmentation. Unique in FSS-1000, our
dataset contains significant number of objects that have never been seen or
annotated in previous datasets, such as tiny daily objects, merchandise,
cartoon characters, logos, etc. We build our baseline model using standard
backbone networks such as VGG-16, ResNet-101, and Inception. To our surprise,
we found that training our model from scratch using FSS-1000 achieves
comparable and even better results than training with weights pre-trained by
ImageNet which is more than 100 times larger than FSS-1000. Both our approach
and dataset are simple, effective, and easily extensible to learn segmentation
of new object classes given very few annotated training examples. Dataset is
available at https://github.com/HKUSTCV/FSS-1000
One-Shot Learning for Semantic Segmentation
Low-shot learning methods for image classification support learning from
sparse data. We extend these techniques to support dense semantic image
segmentation. Specifically, we train a network that, given a small set of
annotated images, produces parameters for a Fully Convolutional Network (FCN).
We use this FCN to perform dense pixel-level prediction on a test image for the
new semantic class. Our architecture shows a 25% relative meanIoU improvement
compared to the best baseline methods for one-shot segmentation on unseen
classes in the PASCAL VOC 2012 dataset and is at least 3 times faster.Comment: To appear in the proceedings of the British Machine Vision Conference
(BMVC) 2017. The code is available at https://github.com/lzzcd001/OSLS
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