1,848 research outputs found
Person Search in Videos with One Portrait Through Visual and Temporal Links
In real-world applications, e.g. law enforcement and video retrieval, one
often needs to search a certain person in long videos with just one portrait.
This is much more challenging than the conventional settings for person
re-identification, as the search may need to be carried out in the environments
different from where the portrait was taken. In this paper, we aim to tackle
this challenge and propose a novel framework, which takes into account the
identity invariance along a tracklet, thus allowing person identities to be
propagated via both the visual and the temporal links. We also develop a novel
scheme called Progressive Propagation via Competitive Consensus, which
significantly improves the reliability of the propagation process. To promote
the study of person search, we construct a large-scale benchmark, which
contains 127K manually annotated tracklets from 192 movies. Experiments show
that our approach remarkably outperforms mainstream person re-id methods,
raising the mAP from 42.16% to 62.27%.Comment: European Conference on Computer Vision (ECCV), 201
DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification
In this work, we present a fully automated lung computed tomography (CT)
cancer diagnosis system, DeepLung. DeepLung consists of two components, nodule
detection (identifying the locations of candidate nodules) and classification
(classifying candidate nodules into benign or malignant). Considering the 3D
nature of lung CT data and the compactness of dual path networks (DPN), two
deep 3D DPN are designed for nodule detection and classification respectively.
Specifically, a 3D Faster Regions with Convolutional Neural Net (R-CNN) is
designed for nodule detection with 3D dual path blocks and a U-net-like
encoder-decoder structure to effectively learn nodule features. For nodule
classification, gradient boosting machine (GBM) with 3D dual path network
features is proposed. The nodule classification subnetwork was validated on a
public dataset from LIDC-IDRI, on which it achieved better performance than
state-of-the-art approaches and surpassed the performance of experienced
doctors based on image modality. Within the DeepLung system, candidate nodules
are detected first by the nodule detection subnetwork, and nodule diagnosis is
conducted by the classification subnetwork. Extensive experimental results
demonstrate that DeepLung has performance comparable to experienced doctors
both for the nodule-level and patient-level diagnosis on the LIDC-IDRI
dataset.\footnote{https://github.com/uci-cbcl/DeepLung.git}Comment: 9 pages, 8 figures, IEEE WACV conference. arXiv admin note:
substantial text overlap with arXiv:1709.0553
DeepLung: 3D Deep Convolutional Nets for Automated Pulmonary Nodule Detection and Classification
In this work, we present a fully automated lung CT cancer diagnosis system,
DeepLung. DeepLung contains two parts, nodule detection and classification.
Considering the 3D nature of lung CT data, two 3D networks are designed for the
nodule detection and classification respectively. Specifically, a 3D Faster
R-CNN is designed for nodule detection with a U-net-like encoder-decoder
structure to effectively learn nodule features. For nodule classification,
gradient boosting machine (GBM) with 3D dual path network (DPN) features is
proposed. The nodule classification subnetwork is validated on a public dataset
from LIDC-IDRI, on which it achieves better performance than state-of-the-art
approaches, and surpasses the average performance of four experienced doctors.
For the DeepLung system, candidate nodules are detected first by the nodule
detection subnetwork, and nodule diagnosis is conducted by the classification
subnetwork. Extensive experimental results demonstrate the DeepLung is
comparable to the experienced doctors both for the nodule-level and
patient-level diagnosis on the LIDC-IDRI dataset
dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs
Objective assessment of image quality is fundamentally important in many
image processing tasks. In this work, we focus on learning blind image quality
assessment (BIQA) models which predict the quality of a digital image with no
access to its original pristine-quality counterpart as reference. One of the
biggest challenges in learning BIQA models is the conflict between the gigantic
image space (which is in the dimension of the number of image pixels) and the
extremely limited reliable ground truth data for training. Such data are
typically collected via subjective testing, which is cumbersome, slow, and
expensive. Here we first show that a vast amount of reliable training data in
the form of quality-discriminable image pairs (DIP) can be obtained
automatically at low cost by exploiting large-scale databases with diverse
image content. We then learn an opinion-unaware BIQA (OU-BIQA, meaning that no
subjective opinions are used for training) model using RankNet, a pairwise
learning-to-rank (L2R) algorithm, from millions of DIPs, each associated with a
perceptual uncertainty level, leading to a DIP inferred quality (dipIQ) index.
Extensive experiments on four benchmark IQA databases demonstrate that dipIQ
outperforms state-of-the-art OU-BIQA models. The robustness of dipIQ is also
significantly improved as confirmed by the group MAximum Differentiation (gMAD)
competition method. Furthermore, we extend the proposed framework by learning
models with ListNet (a listwise L2R algorithm) on quality-discriminable image
lists (DIL). The resulting DIL Inferred Quality (dilIQ) index achieves an
additional performance gain
BEBP: An Poisoning Method Against Machine Learning Based IDSs
In big data era, machine learning is one of fundamental techniques in
intrusion detection systems (IDSs). However, practical IDSs generally update
their decision module by feeding new data then retraining learning models in a
periodical way. Hence, some attacks that comprise the data for training or
testing classifiers significantly challenge the detecting capability of machine
learning-based IDSs. Poisoning attack, which is one of the most recognized
security threats towards machine learning-based IDSs, injects some adversarial
samples into the training phase, inducing data drifting of training data and a
significant performance decrease of target IDSs over testing data. In this
paper, we adopt the Edge Pattern Detection (EPD) algorithm to design a novel
poisoning method that attack against several machine learning algorithms used
in IDSs. Specifically, we propose a boundary pattern detection algorithm to
efficiently generate the points that are near to abnormal data but considered
to be normal ones by current classifiers. Then, we introduce a Batch-EPD
Boundary Pattern (BEBP) detection algorithm to overcome the limitation of the
number of edge pattern points generated by EPD and to obtain more useful
adversarial samples. Based on BEBP, we further present a moderate but effective
poisoning method called chronic poisoning attack. Extensive experiments on
synthetic and three real network data sets demonstrate the performance of the
proposed poisoning method against several well-known machine learning
algorithms and a practical intrusion detection method named FMIFS-LSSVM-IDS.Comment: 7 pages,5figures, conferenc
MLBench: How Good Are Machine Learning Clouds for Binary Classification Tasks on Structured Data?
We conduct an empirical study of machine learning functionalities provided by
major cloud service providers, which we call machine learning clouds. Machine
learning clouds hold the promise of hiding all the sophistication of running
large-scale machine learning: Instead of specifying how to run a machine
learning task, users only specify what machine learning task to run and the
cloud figures out the rest. Raising the level of abstraction, however, rarely
comes free - a performance penalty is possible. How good, then, are current
machine learning clouds on real-world machine learning workloads?
We study this question with a focus on binary classication problems. We
present mlbench, a novel benchmark constructed by harvesting datasets from
Kaggle competitions. We then compare the performance of the top winning code
available from Kaggle with that of running machine learning clouds from both
Azure and Amazon on mlbench. Our comparative study reveals the strength and
weakness of existing machine learning clouds and points out potential future
directions for improvement
HFL-RC System at SemEval-2018 Task 11: Hybrid Multi-Aspects Model for Commonsense Reading Comprehension
This paper describes the system which got the state-of-the-art results at
SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge. In
this paper, we present a neural network called Hybrid Multi-Aspects (HMA)
model, which mimic the human's intuitions on dealing with the multiple-choice
reading comprehension. In this model, we aim to produce the predictions in
multiple aspects by calculating attention among the text, question and choices,
and combine these results for final predictions. Experimental results show that
our HMA model could give substantial improvements over the baseline system and
got the first place on the final test set leaderboard with the accuracy of
84.13%.Comment: 6 page
DRPose3D: Depth Ranking in 3D Human Pose Estimation
In this paper, we propose a two-stage depth ranking based method (DRPose3D)
to tackle the problem of 3D human pose estimation. Instead of accurate 3D
positions, the depth ranking can be identified by human intuitively and learned
using the deep neural network more easily by solving classification problems.
Moreover, depth ranking contains rich 3D information. It prevents the 2D-to-3D
pose regression in two-stage methods from being ill-posed. In our method,
firstly, we design a Pairwise Ranking Convolutional Neural Network (PRCNN) to
extract depth rankings of human joints from images. Secondly, a coarse-to-fine
3D Pose Network(DPNet) is proposed to estimate 3D poses from both depth
rankings and 2D human joint locations. Additionally, to improve the generality
of our model, we introduce a statistical method to augment depth rankings. Our
approach outperforms the state-of-the-art methods in the Human3.6M benchmark
for all three testing protocols, indicating that depth ranking is an essential
geometric feature which can be learned to improve the 3D pose estimation.Comment: Accepted by the 27th International Joint Conference on Artificial
Intelligence (IJCAI 2018
Benchmarking Robustness of Machine Reading Comprehension Models
Machine Reading Comprehension (MRC) is an important testbed for evaluating
models' natural language understanding (NLU) ability. There has been rapid
progress in this area, with new models achieving impressive performance on
various MRC benchmarks. However, most of these benchmarks only evaluate models
on in-domain test sets without considering their robustness under test-time
perturbations. To fill this important gap, we construct AdvRACE (Adversarial
RACE), a new model-agnostic benchmark for evaluating the robustness of MRC
models under six different types of test-time perturbations, including our
novel superimposed attack and distractor construction attack. We show that
current state-of-the-art (SOTA) models are vulnerable to these simple black-box
attacks. Our benchmark is constructed automatically based on the existing RACE
benchmark, and thus the construction pipeline can be easily adopted by other
tasks and datasets. We will release the data and source codes to facilitate
future work. We hope that our work will encourage more research on improving
the robustness of MRC and other NLU models.Comment: Work in progres
Weakly-Supervised Discovery of Geometry-Aware Representation for 3D Human Pose Estimation
Recent studies have shown remarkable advances in 3D human pose estimation
from monocular images, with the help of large-scale in-door 3D datasets and
sophisticated network architectures. However, the generalizability to different
environments remains an elusive goal. In this work, we propose a geometry-aware
3D representation for the human pose to address this limitation by using
multiple views in a simple auto-encoder model at the training stage and only 2D
keypoint information as supervision. A view synthesis framework is proposed to
learn the shared 3D representation between viewpoints with synthesizing the
human pose from one viewpoint to the other one. Instead of performing a direct
transfer in the raw image-level, we propose a skeleton-based encoder-decoder
mechanism to distil only pose-related representation in the latent space. A
learning-based representation consistency constraint is further introduced to
facilitate the robustness of latent 3D representation. Since the learnt
representation encodes 3D geometry information, mapping it to 3D pose will be
much easier than conventional frameworks that use an image or 2D coordinates as
the input of 3D pose estimator. We demonstrate our approach on the task of 3D
human pose estimation. Comprehensive experiments on three popular benchmarks
show that our model can significantly improve the performance of
state-of-the-art methods with simply injecting the representation as a robust
3D prior.Comment: Accepted as a CVPR 2019 oral paper. Project page:
https://kwanyeelin.github.io
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