pwc-archive/papers-with-abstracts huggingface dataset:
Learning Discriminative Features with Multiple Granularities for Person Re-Identification
Authors: Guanshuo Wang, Yufeng Yuan, Xiong Chen, Jiwei Li, Xi Zhou
Date: 2018-04-04 00:00:00
Tasks: Person Re-Identification, Re-Ranking

The combination of global and partial features has been an essential solution
to improve discriminative performances in person re-identification (Re-ID)
tasks. Previous part-based methods mainly focus on locating regions with
specific pre-defined semantics to learn local representations, which increases
learning difficulty but not efficient or robust to scenarios with large
variances. In this paper, we propose an end-to-end feature learning strategy
integrating discriminative information with various granularities. We carefully
design the Multiple Granularity Network (MGN), a multi-branch deep network
architecture consisting of one branch for global feature representations and
two branches for local feature representations. Instead of learning on semantic
regions, we uniformly partition the images into several stripes, and vary the
number of parts in different local branches to obtain local feature
representations with multiple granularities. Comprehensive experiments
implemented on the mainstream evaluation datasets including Market-1501,
DukeMTMC-reid and CUHK03 indicate that our method has robustly achieved
state-of-the-art performances and outperformed any existing approaches by a
large margin. For example, on Market-1501 dataset in single query mode, we
achieve a state-of-the-art result of Rank-1/mAP=96.6%/94.2% after re-ranking.
Experience-based Optimization: A Coevolutionary Approach
Authors: Shengcai Liu, Ke Tang, Xin Yao
Date: 2017-03-29 00:00:00

This paper studies improving solvers based on their past solving experiences,
and focuses on improving solvers by offline training. Specifically, the key
issues of offline training methods are discussed, and research belonging to
this category but from different areas are reviewed in a unified framework.
Existing training methods generally adopt a two-stage strategy in which
selecting the training instances and training instances are treated in two
independent phases. This paper proposes a new training method, dubbed LiangYi,
which addresses these two issues simultaneously. LiangYi includes a training
module for a population-based solver and an instance sampling module for
updating the training instances. The idea behind LiangYi is to promote the
population-based solver by training it (with the training module) to improve
its performance on those instances (discovered by the sampling module) on which
it performs badly, while keeping the good performances obtained by it on
previous instances. An instantiation of LiangYi on the Travelling Salesman
Problem is also proposed. Empirical results on a huge testing set containing
10000 instances showed LiangYi could train solvers that perform significantly
better than the solvers trained by other state-of-the-art training method.
Moreover, empirical investigation of the behaviours of LiangYi confirmed it was
able to continuously improve the solver through training.
Cross-Domain Adversarial Auto-Encoder
Authors: Haodi Hou, Jing Huo, Yang Gao
Date: 2018-04-17 00:00:00
Tasks: Domain Adaptation, Image Generation

In this paper, we propose the Cross-Domain Adversarial Auto-Encoder (CDAAE)
to address the problem of cross-domain image inference, generation and
transformation. We make the assumption that images from different domains share
the same latent code space for content, while having separate latent code space
for style. The proposed framework can map cross-domain data to a latent code
vector consisting of a content part and a style part. The latent code vector is
matched with a prior distribution so that we can generate meaningful samples
from any part of the prior space. Consequently, given a sample of one domain,
our framework can generate various samples of the other domain with the same
content of the input. This makes the proposed framework different from the
current work of cross-domain transformation. Besides, the proposed framework
can be trained with both labeled and unlabeled data, which makes it also
suitable for domain adaptation. Experimental results on data sets SVHN, MNIST
and CASIA show the proposed framework achieved visually appealing performance
for image generation task. Besides, we also demonstrate the proposed method
achieved superior results for domain adaptation. Code of our experiments is
available in https://github.com/luckycallor/CDAAE.
A Multi-task Selected Learning Approach for Solving 3D Flexible Bin Packing Problem
Authors: Lu Duan, Haoyuan Hu, Yu Qian, Yu Gong, Xiaodong Zhang, Yinghui Xu, Jiangwen Wei
Date: 2018-04-17 00:00:00
Tasks: 3D Bin Packing, Combinatorial Optimization

A 3D flexible bin packing problem (3D-FBPP) arises from the process of
warehouse packing in e-commerce. An online customer's order usually contains
several items and needs to be packed as a whole before shipping. In particular,
5% of tens of millions of packages are using plastic wrapping as outer
packaging every day, which brings pressure on the plastic surface minimization
to save traditional logistics costs. Because of the huge practical
significance, we focus on the issue of packing cuboid-shaped items orthogonally
into a least-surface-area bin. The existing heuristic methods for classic 3D
bin packing don't work well for this particular NP-hard problem and designing a
good problem-specific heuristic is non-trivial. In this paper, rather than
designing heuristics, we propose a novel multi-task framework based on Selected
Learning to learn a heuristic-like policy that generates the sequence and
orientations of items to be packed simultaneously. Through comprehensive
experiments on a large scale real-world transaction order dataset and online AB
tests, we show: 1) our selected learning method trades off the imbalance and
correlation among the tasks and significantly outperforms the single task
Pointer Network and the multi-task network without selected learning; 2) our
method obtains an average 5.47% cost reduction than the well-designed greedy
algorithm which is previously used in our online production system.
Cascaded multi-scale and multi-dimension convolutional neural network for stereo matching
Authors: Haihua Lu, Hai Xu, Li Zhang, Yong Zhao
Date: 2018-03-26 00:00:00
Tasks: Stereo Matching, Stereo Matching Hand

Convolutional neural networks(CNN) have been shown to perform better than the
conventional stereo algorithms for stereo estimation. Numerous efforts focus on
the pixel-wise matching cost computation, which is the important building block
for many start-of-the-art algorithms. However, those architectures are limited
to small and single scale receptive fields and use traditional methods for cost
aggregation or even ignore cost aggregation. Differently we take them both into
consideration. Firstly, we propose a new multi-scale matching cost computation
sub-network, in which two different sizes of receptive fields are implemented
parallelly. In this way, the network can make the best use of both variants and
balance the trade-off between the increase of receptive field and the loss of
detail. Furthermore, we show that our multi-dimension aggregation sub-network
which containing 2D convolution and 3D convolution operations can provide rich
context and semantic information for estimating an accurate initial disparity.
Finally, experiments on challenging stereo benchmark KITTI demonstrate that the
proposed method can achieve competitive results even without any additional
post-processing.
Improving Deep Binary Embedding Networks by Order-aware Reweighting of Triplets
Authors: Jikai Chen, Hanjiang Lai, Libing Geng, Yan Pan
Date: 2018-04-17 00:00:00
Tasks: Image Retrieval, Retrieval, Triplet

In this paper, we focus on triplet-based deep binary embedding networks for
image retrieval task. The triplet loss has been shown to be most effective for
the ranking problem. However, most of the previous works treat the triplets
equally or select the hard triplets based on the loss. Such strategies do not
consider the order relations, which is important for retrieval task. To this
end, we propose an order-aware reweighting method to effectively train the
triplet-based deep networks, which up-weights the important triplets and
down-weights the uninformative triplets. First, we present the order-aware
weighting factors to indicate the importance of the triplets, which depend on
the rank order of binary codes. Then, we reshape the triplet loss to the
squared triplet loss such that the loss function will put more weights on the
important triplets. Extensive evaluations on four benchmark datasets show that
the proposed method achieves significant performance compared with the
state-of-the-art baselines.
Modular Resource Centric Learning for Workflow Performance Prediction
Authors: Alok Singh, Mai Nguyen, Shweta Purawat, Daniel Crawl, Ilkay Altintas
Date: 2017-11-15 00:00:00
Tasks: BIG-bench Machine Learning, Distributed Computing, Prediction, Scheduling

Workflows provide an expressive programming model for fine-grained control of
large-scale applications in distributed computing environments. Accurate
estimates of complex workflow execution metrics on large-scale machines have
several key advantages. The performance of scheduling algorithms that rely on
estimates of execution metrics degrades when the accuracy of predicted
execution metrics decreases. This in-progress paper presents a technique being
developed to improve the accuracy of predicted performance metrics of
large-scale workflows on distributed platforms. The central idea of this work
is to train resource-centric machine learning agents to capture complex
relationships between a set of program instructions and their performance
metrics when executed on a specific resource. This resource-centric view of a
workflow exploits the fact that predicting execution times of sub-modules of a
workflow requires monitoring and modeling of a few dynamic and static features.
We transform the input workflow that is essentially a directed acyclic graph of
actions into a Physical Resource Execution Plan (PREP). This transformation
enables us to model an arbitrarily complex workflow as a set of simpler
programs running on physical nodes. We delegate a machine learning model to
capture performance metrics for each resource type when it executes different
program instructions under varying degrees of resource contention. Our
algorithm takes the prediction metrics from each resource agent and composes
the overall workflow performance metrics by utilizing the structure of the
corresponding Physical Resource Execution Plan.
Adversarial Example Generation with Syntactically Controlled Paraphrase Networks
Authors: Mohit Iyyer, John Wieting, Kevin Gimpel, Luke Zettlemoyer
Date: 2018-04-17 00:00:00
Proceeding: NAACL 2018 6
Tasks: Decoder, Sentence

We propose syntactically controlled paraphrase networks (SCPNs) and use them
to generate adversarial examples. Given a sentence and a target syntactic form
(e.g., a constituency parse), SCPNs are trained to produce a paraphrase of the
sentence with the desired syntax. We show it is possible to create training
data for this task by first doing backtranslation at a very large scale, and
then using a parser to label the syntactic transformations that naturally occur
during this process. Such data allows us to train a neural encoder-decoder
model with extra inputs to specify the target syntax. A combination of
automated and human evaluations show that SCPNs generate paraphrases that
follow their target specifications without decreasing paraphrase quality when
compared to baseline (uncontrolled) paraphrase systems. Furthermore, they are
more capable of generating syntactically adversarial examples that both (1)
"fool" pretrained models and (2) improve the robustness of these models to
syntactic variation when used to augment their training data.
Robust Dual View Deep Agent
Authors: Ibrahim M. Sobh, Nevin M. Darwish
Date: 2018-04-13 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Motivated by recent advance of machine learning using Deep Reinforcement
Learning this paper proposes a modified architecture that produces more robust
agents and speeds up the training process. Our architecture is based on
Asynchronous Advantage Actor-Critic (A3C) algorithm where the total input
dimensionality is halved by dividing the input into two independent streams. We
use ViZDoom, 3D world software that is based on the classical first person
shooter video game, Doom, as a test case. The experiments show that in
comparison to single input agents, the proposed architecture succeeds to have
the same playing performance and shows more robust behavior, achieving
significant reduction in the number of training parameters of almost 30%.
Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation
Authors: Chao Li, Qiaoyong Zhong, Di Xie, ShiLiang Pu
Date: 2018-04-17 00:00:00
Tasks: Action Recognition, RF-based Pose Estimation, Skeleton Based Action Recognition, Temporal Action Localization

Skeleton-based human action recognition has recently drawn increasing
attentions with the availability of large-scale skeleton datasets. The most
crucial factors for this task lie in two aspects: the intra-frame
representation for joint co-occurrences and the inter-frame representation for
skeletons' temporal evolutions. In this paper we propose an end-to-end
convolutional co-occurrence feature learning framework. The co-occurrence
features are learned with a hierarchical methodology, in which different levels
of contextual information are aggregated gradually. Firstly point-level
information of each joint is encoded independently. Then they are assembled
into semantic representation in both spatial and temporal domains.
Specifically, we introduce a global spatial aggregation scheme, which is able
to learn superior joint co-occurrence features over local aggregation. Besides,
raw skeleton coordinates as well as their temporal difference are integrated
with a two-stream paradigm. Experiments show that our approach consistently
outperforms other state-of-the-arts on action recognition and detection
benchmarks like NTU RGB+D, SBU Kinect Interaction and PKU-MMD.
Iterative Residual Image Deconvolution
Authors: Li Si-Yao, Dongwei Ren, Furong Zhao, Zijian Hu, Junfeng Li, Qian Yin
Date: 2018-04-17 00:00:00
Tasks: Deblurring, Image Deblurring, Image Deconvolution

Image deblurring, a.k.a. image deconvolution, recovers a clear image from
pixel superposition caused by blur degradation. Few deep convolutional neural
networks (CNN) succeed in addressing this task. In this paper, we first
demonstrate that the minimum-mean-square-error (MMSE) solution to image
deblurring can be interestingly unfolded into a series of residual components.
Based on this analysis, we propose a novel iterative residual deconvolution
(IRD) algorithm. Further, IRD motivates us to take one step forward to design
an explicable and effective CNN architecture for image deconvolution.
Specifically, a sequence of residual CNN units are deployed, whose intermediate
outputs are then concatenated and integrated, resulting in concatenated
residual convolutional network (CRCNet). The experimental results demonstrate
that proposed CRCNet not only achieves better quantitative metrics but also
recovers more visually plausible texture details compared with state-of-the-art
methods.
Real-Time Rotation-Invariant Face Detection with Progressive Calibration Networks
Authors: Xuepeng Shi, Shiguang Shan, Meina Kan, Shuzhe Wu, Xilin Chen
Date: 2018-04-17 00:00:00
Proceeding: CVPR 2018 6
Tasks: Binary Classification, Face Detection

Rotation-invariant face detection, i.e. detecting faces with arbitrary rotation-in-plane (RIP) angles, is widely required in unconstrained applications but still remains as a challenging task, due to the large variations of face appearances. Most existing methods compromise with speed or accuracy to handle the large RIP variations. To address this problem more efficiently, we propose Progressive Calibration Networks (PCN) to perform rotation-invariant face detection in a coarse-to-fine manner. PCN consists of three stages, each of which not only distinguishes the faces from non-faces, but also calibrates the RIP orientation of each face candidate to upright progressively. By dividing the calibration process into several progressive steps and only predicting coarse orientations in early stages, PCN can achieve precise and fast calibration. By performing binary classification of face vs. non-face with gradually decreasing RIP ranges, PCN can accurately detect faces with full $360^{\circ}$ RIP angles. Such designs lead to a real-time rotation-invariant face detector. The experiments on multi-oriented FDDB and a challenging subset of WIDER FACE containing rotated faces in the wild show that our PCN achieves quite promising performance.
Reinforced Co-Training
Authors: Jiawei Wu, Lei LI, William Yang Wang
Date: 2018-04-17 00:00:00
Proceeding: NAACL 2018 6
Tasks: Clickbait Detection, General Classification, Q-Learning, text-classification, Text Classification

Co-training is a popular semi-supervised learning framework to utilize a
large amount of unlabeled data in addition to a small labeled set. Co-training
methods exploit predicted labels on the unlabeled data and select samples based
on prediction confidence to augment the training. However, the selection of
samples in existing co-training methods is based on a predetermined policy,
which ignores the sampling bias between the unlabeled and the labeled subsets,
and fails to explore the data space. In this paper, we propose a novel method,
Reinforced Co-Training, to select high-quality unlabeled samples to better
co-train on. More specifically, our approach uses Q-learning to learn a data
selection policy with a small labeled dataset, and then exploits this policy to
train the co-training classifiers automatically. Experimental results on
clickbait detection and generic text classification tasks demonstrate that our
proposed method can obtain more accurate text classification results.
ListOps: A Diagnostic Dataset for Latent Tree Learning
Authors: Nikita Nangia, Samuel R. Bowman
Date: 2018-04-17 00:00:00
Proceeding: NAACL 2018 6
Tasks: Diagnostic, ListOps, Sentence, Sentence Classification, valid

Latent tree learning models learn to parse a sentence without syntactic
supervision, and use that parse to build the sentence representation. Existing
work on such models has shown that, while they perform well on tasks like
sentence classification, they do not learn grammars that conform to any
plausible semantic or syntactic formalism (Williams et al., 2018a). Studying
the parsing ability of such models in natural language can be challenging due
to the inherent complexities of natural language, like having several valid
parses for a single sentence. In this paper we introduce ListOps, a toy dataset
created to study the parsing ability of latent tree models. ListOps sequences
are in the style of prefix arithmetic. The dataset is designed to have a single
correct parsing strategy that a system needs to learn to succeed at the task.
We show that the current leading latent tree models are unable to learn to
parse and succeed at ListOps. These models achieve accuracies worse than purely
sequential RNNs.
A Boosting Framework of Factorization Machine
Authors: Longfei Li, Peilin Zhao, Jun Zhou, Xiaolong Li
Date: 2018-04-17 00:00:00
Tasks: Recommendation Systems

Recently, Factorization Machines (FM) has become more and more popular for
recommendation systems, due to its effectiveness in finding informative
interactions between features. Usually, the weights for the interactions is
learnt as a low rank weight matrix, which is formulated as an inner product of
two low rank matrices. This low rank can help improve the generalization
ability of Factorization Machines. However, to choose the rank properly, it
usually needs to run the algorithm for many times using different ranks, which
clearly is inefficient for some large-scale datasets. To alleviate this issue,
we propose an Adaptive Boosting framework of Factorization Machines (AdaFM),
which can adaptively search for proper ranks for different datasets without
re-training. Instead of using a fixed rank for FM, the proposed algorithm will
adaptively gradually increases its rank according to its performance until the
performance does not grow, using boosting strategy. To verify the performance
of our proposed framework, we conduct an extensive set of experiments on many
real-world datasets. Encouraging empirical results shows that the proposed
algorithms are generally more effective than state-of-the-art other
Factorization Machines.
Learning to Color from Language
Authors: Varun Manjunatha, Mohit Iyyer, Jordan Boyd-Graber, Larry Davis
Date: 2018-04-17 00:00:00
Proceeding: NAACL 2018 6
Tasks: Colorization, Descriptive

Automatic colorization is the process of adding color to greyscale images. We
condition this process on language, allowing end users to manipulate a
colorized image by feeding in different captions. We present two different
architectures for language-conditioned colorization, both of which produce more
accurate and plausible colorizations than a language-agnostic version. Through
this language-based framework, we can dramatically alter colorizations by
manipulating descriptive color words in captions.
Salient Object Detection by Lossless Feature Reflection
Authors: Pingping Zhang, Wei Liu, Huchuan Lu, Chunhua Shen
Date: 2018-02-19 00:00:00
Tasks: Object, object-detection, Object Detection, RGB Salient Object Detection, Saliency Detection, Salient Object Detection

Salient object detection, which aims to identify and locate the most salient
pixels or regions in images, has been attracting more and more interest due to
its various real-world applications. However, this vision task is quite
challenging, especially under complex image scenes. Inspired by the intrinsic
reflection of natural images, in this paper we propose a novel feature learning
framework for large-scale salient object detection. Specifically, we design a
symmetrical fully convolutional network (SFCN) to learn complementary saliency
features under the guidance of lossless feature reflection. The location
information, together with contextual and semantic information, of salient
objects are jointly utilized to supervise the proposed network for more
accurate saliency predictions. In addition, to overcome the blurry boundary
problem, we propose a new structural loss function to learn clear object
boundaries and spatially consistent saliency. The coarse prediction results are
effectively refined by these structural information for performance
improvements. Extensive experiments on seven saliency detection datasets
demonstrate that our approach achieves consistently superior performance and
outperforms the very recent state-of-the-art methods.