44,504 research outputs found
Multicolumn Networks for Face Recognition
The objective of this work is set-based face recognition, i.e. to decide if
two sets of images of a face are of the same person or not. Conventionally, the
set-wise feature descriptor is computed as an average of the descriptors from
individual face images within the set. In this paper, we design a neural
network architecture that learns to aggregate based on both "visual" quality
(resolution, illumination), and "content" quality (relative importance for
discriminative classification). To this end, we propose a Multicolumn Network
(MN) that takes a set of images (the number in the set can vary) as input, and
learns to compute a fix-sized feature descriptor for the entire set. To
encourage high-quality representations, each individual input image is first
weighted by its "visual" quality, determined by a self-quality assessment
module, and followed by a dynamic recalibration based on "content" qualities
relative to the other images within the set. Both of these qualities are learnt
implicitly during training for set-wise classification. Comparing with the
previous state-of-the-art architectures trained with the same dataset
(VGGFace2), our Multicolumn Networks show an improvement of between 2-6% on the
IARPA IJB face recognition benchmarks, and exceed the state of the art for all
methods on these benchmarks.Comment: To appear in BMVC201
Performance Pressure and Resource Allocation in Washington
Based on interviews with state, district, and school officials, explores how performance pressures have changed resource allocation decisions. Examines reform goals and how Washington's finance system impedes efforts to link resources to student learning
VGGFace2: A dataset for recognising faces across pose and age
In this paper, we introduce a new large-scale face dataset named VGGFace2.
The dataset contains 3.31 million images of 9131 subjects, with an average of
362.6 images for each subject. Images are downloaded from Google Image Search
and have large variations in pose, age, illumination, ethnicity and profession
(e.g. actors, athletes, politicians). The dataset was collected with three
goals in mind: (i) to have both a large number of identities and also a large
number of images for each identity; (ii) to cover a large range of pose, age
and ethnicity; and (iii) to minimize the label noise. We describe how the
dataset was collected, in particular the automated and manual filtering stages
to ensure a high accuracy for the images of each identity. To assess face
recognition performance using the new dataset, we train ResNet-50 (with and
without Squeeze-and-Excitation blocks) Convolutional Neural Networks on
VGGFace2, on MS- Celeb-1M, and on their union, and show that training on
VGGFace2 leads to improved recognition performance over pose and age. Finally,
using the models trained on these datasets, we demonstrate state-of-the-art
performance on all the IARPA Janus face recognition benchmarks, e.g. IJB-A,
IJB-B and IJB-C, exceeding the previous state-of-the-art by a large margin.
Datasets and models are publicly available.Comment: This paper has been accepted by IEEE Conference on Automatic Face and
Gesture Recognition (F&G), 2018. (Oral
Improvements to context based self-supervised learning
We develop a set of methods to improve on the results of self-supervised
learning using context. We start with a baseline of patch based arrangement
context learning and go from there. Our methods address some overt problems
such as chromatic aberration as well as other potential problems such as
spatial skew and mid-level feature neglect. We prevent problems with testing
generalization on common self-supervised benchmark tests by using different
datasets during our development. The results of our methods combined yield top
scores on all standard self-supervised benchmarks, including classification and
detection on PASCAL VOC 2007, segmentation on PASCAL VOC 2012, and "linear
tests" on the ImageNet and CSAIL Places datasets. We obtain an improvement over
our baseline method of between 4.0 to 7.1 percentage points on transfer
learning classification tests. We also show results on different standard
network architectures to demonstrate generalization as well as portability. All
data, models and programs are available at:
https://gdo-datasci.llnl.gov/selfsupervised/.Comment: Accepted paper at CVPR 201
Managing suppliers for collection development: the UK higher education perspective
This chapter follows the adoption of the new procurement discipline by academic libraries since the demise of the NBA. It first examines the standard procurement cycle, with particular reference to libraries and book supply. It then discusses library purchasing consortia and their contribution to managing and developing the library market place for books, identifying three phases of operation. It closes with some reflections on the future prospects of collection development. Traditional collection development is seen as being turned on its head â we no longer seek to collect the huge range of works of scholars of all other institutions in order to make them available to the (relatively) small number of our own scholars; instead we collect the works of our own and make them available to all
Biometric presentation attack detection: beyond the visible spectrum
The increased need for unattended authentication in
multiple scenarios has motivated a wide deployment of biometric
systems in the last few years. This has in turn led to the
disclosure of security concerns specifically related to biometric
systems. Among them, presentation attacks (PAs, i.e., attempts
to log into the system with a fake biometric characteristic or
presentation attack instrument) pose a severe threat to the
security of the system: any person could eventually fabricate
or order a gummy finger or face mask to impersonate someone
else. In this context, we present a novel fingerprint presentation
attack detection (PAD) scheme based on i) a new capture device
able to acquire images within the short wave infrared (SWIR)
spectrum, and i i) an in-depth analysis of several state-of-theart
techniques based on both handcrafted and deep learning
features. The approach is evaluated on a database comprising
over 4700 samples, stemming from 562 different subjects and
35 different presentation attack instrument (PAI) species. The
results show the soundness of the proposed approach with a
detection equal error rate (D-EER) as low as 1.35% even in a
realistic scenario where five different PAI species are considered
only for testing purposes (i.e., unknown attacks
Representing Conversations for Scalable Overhearing
Open distributed multi-agent systems are gaining interest in the academic
community and in industry. In such open settings, agents are often coordinated
using standardized agent conversation protocols. The representation of such
protocols (for analysis, validation, monitoring, etc) is an important aspect of
multi-agent applications. Recently, Petri nets have been shown to be an
interesting approach to such representation, and radically different approaches
using Petri nets have been proposed. However, their relative strengths and
weaknesses have not been examined. Moreover, their scalability and suitability
for different tasks have not been addressed. This paper addresses both these
challenges. First, we analyze existing Petri net representations in terms of
their scalability and appropriateness for overhearing, an important task in
monitoring open multi-agent systems. Then, building on the insights gained, we
introduce a novel representation using Colored Petri nets that explicitly
represent legal joint conversation states and messages. This representation
approach offers significant improvements in scalability and is particularly
suitable for overhearing. Furthermore, we show that this new representation
offers a comprehensive coverage of all conversation features of FIPA
conversation standards. We also present a procedure for transforming AUML
conversation protocol diagrams (a standard human-readable representation), to
our Colored Petri net representation
- âŠ