419 research outputs found
Learning Discriminative Features with Class Encoder
Deep neural networks usually benefit from unsupervised pre-training, e.g.
auto-encoders. However, the classifier further needs supervised fine-tuning
methods for good discrimination. Besides, due to the limits of full-connection,
the application of auto-encoders is usually limited to small, well aligned
images. In this paper, we incorporate the supervised information to propose a
novel formulation, namely class-encoder, whose training objective is to
reconstruct a sample from another one of which the labels are identical.
Class-encoder aims to minimize the intra-class variations in the feature space,
and to learn a good discriminative manifolds on a class scale. We impose the
class-encoder as a constraint into the softmax for better supervised training,
and extend the reconstruction on feature-level to tackle the parameter size
issue and translation issue. The experiments show that the class-encoder helps
to improve the performance on benchmarks of classification and face
recognition. This could also be a promising direction for fast training of face
recognition models.Comment: Accepted by CVPR2016 Workshop of Robust Features for Computer Visio
Extracting Protocol Format as State Machine via Controlled Static Loop Analysis
Reverse engineering of protocol message formats is critical for many security
applications. Mainstream techniques use dynamic analysis and inherit its
low-coverage problem -- the inferred message formats only reflect the features
of their inputs. To achieve high coverage, we choose to use static analysis to
infer message formats from the implementation of protocol parsers. In this
work, we focus on a class of extremely challenging protocols whose formats are
described via constraint-enhanced regular expressions and parsed using
finite-state machines. Such state machines are often implemented as complicated
parsing loops, which are inherently difficult to analyze via conventional
static analysis. Our new technique extracts a state machine by regarding each
loop iteration as a state and the dependency between loop iterations as state
transitions. To achieve high, i.e., path-sensitive, precision but avoid path
explosion, the analysis is controlled to merge as many paths as possible based
on carefully-designed rules. The evaluation results show that we can infer a
state machine and, thus, the message formats, in five minutes with over 90%
precision and recall, far better than state of the art. We also applied the
state machines to enhance protocol fuzzers, which are improved by 20% to 230%
in terms of coverage and detect ten more zero-days compared to baselines
SFD: Single Shot Scale-invariant Face Detector
This paper presents a real-time face detector, named Single Shot
Scale-invariant Face Detector (SFD), which performs superiorly on various
scales of faces with a single deep neural network, especially for small faces.
Specifically, we try to solve the common problem that anchor-based detectors
deteriorate dramatically as the objects become smaller. We make contributions
in the following three aspects: 1) proposing a scale-equitable face detection
framework to handle different scales of faces well. We tile anchors on a wide
range of layers to ensure that all scales of faces have enough features for
detection. Besides, we design anchor scales based on the effective receptive
field and a proposed equal proportion interval principle; 2) improving the
recall rate of small faces by a scale compensation anchor matching strategy; 3)
reducing the false positive rate of small faces via a max-out background label.
As a consequence, our method achieves state-of-the-art detection performance on
all the common face detection benchmarks, including the AFW, PASCAL face, FDDB
and WIDER FACE datasets, and can run at 36 FPS on a Nvidia Titan X (Pascal) for
VGA-resolution images.Comment: Accepted by ICCV 2017 + its supplementary materials; Updated the
latest results on WIDER FAC
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