pwc-archive/papers-with-abstracts huggingface dataset:
Discriminative Feature Learning with Foreground Attention for Person Re-Identification
Authors: Sanping Zhou, Jinjun Wang, Deyu Meng, Yudong Liang, Yihong Gong, Nanning Zheng
Date: 2018-07-04 00:00:00
Tasks: Decoder, Multi-Task Learning, Person Re-Identification, Triplet

The performance of person re-identification (Re-ID) has been seriously
effected by the large cross-view appearance variations caused by mutual
occlusions and background clutters. Hence learning a feature representation
that can adaptively emphasize the foreground persons becomes very critical to
solve the person Re-ID problem. In this paper, we propose a simple yet
effective foreground attentive neural network (FANN) to learn a discriminative
feature representation for person Re-ID, which can adaptively enhance the
positive side of foreground and weaken the negative side of background.
Specifically, a novel foreground attentive subnetwork is designed to drive the
network's attention, in which a decoder network is used to reconstruct the
binary mask by using a novel local regression loss function, and an encoder
network is regularized by the decoder network to focus its attention on the
foreground persons. The resulting feature maps of encoder network are further
fed into the body part subnetwork and feature fusion subnetwork to learn
discriminative features. Besides, a novel symmetric triplet loss function is
introduced to supervise feature learning, in which the intra-class distance is
minimized and the inter-class distance is maximized in each triplet unit,
simultaneously. Training our FANN in a multi-task learning framework, a
discriminative feature representation can be learned to find out the matched
reference to each probe among various candidates in the gallery. Extensive
experimental results on several public benchmark datasets are evaluated, which
have shown clear improvements of our method over the state-of-the-art
approaches.
Semantic Instance Meets Salient Object: Study on Video Semantic Salient Instance Segmentation
Authors: Trung-Nghia Le, Akihiro Sugimoto
Date: 2018-07-04 00:00:00
Tasks: Instance Segmentation, Robot Navigation, Segmentation, Self-Driving Cars, Semantic Segmentation

Focusing on only semantic instances that only salient in a scene gains more
benefits for robot navigation and self-driving cars than looking at all objects
in the whole scene. This paper pushes the envelope on salient regions in a
video to decompose them into semantically meaningful components, namely,
semantic salient instances. We provide the baseline for the new task of video
semantic salient instance segmentation (VSSIS), that is, Semantic Instance -
Salient Object (SISO) framework. The SISO framework is simple yet efficient,
leveraging advantages of two different segmentation tasks, i.e. semantic
instance segmentation and salient object segmentation to eventually fuse them
for the final result. In SISO, we introduce a sequential fusion by looking at
overlapping pixels between semantic instances and salient regions to have
non-overlapping instances one by one. We also introduce a recurrent instance
propagation to refine the shapes and semantic meanings of instances, and an
identity tracking to maintain both the identity and the semantic meaning of
instances over the entire video. Experimental results demonstrated the
effectiveness of our SISO baseline, which can handle occlusions in videos. In
addition, to tackle the task of VSSIS, we augment the DAVIS-2017 benchmark
dataset by assigning semantic ground-truth for salient instance labels,
obtaining SEmantic Salient Instance Video (SESIV) dataset. Our SESIV dataset
consists of 84 high-quality video sequences with pixel-wisely per-frame
ground-truth labels.
Understanding Visual Ads by Aligning Symbols and Objects using Co-Attention
Authors: Karuna Ahuja, Karan Sikka, Anirban Roy, Ajay Divakaran
Date: 2018-07-04 00:00:00

We tackle the problem of understanding visual ads where given an ad image,
our goal is to rank appropriate human generated statements describing the
purpose of the ad. This problem is generally addressed by jointly embedding
images and candidate statements to establish correspondence. Decoding a visual
ad requires inference of both semantic and symbolic nuances referenced in an
image and prior methods may fail to capture such associations especially with
weakly annotated symbols. In order to create better embeddings, we leverage an
attention mechanism to associate image proposals with symbols and thus
effectively aggregate information from aligned multimodal representations. We
propose a multihop co-attention mechanism that iteratively refines the
attention map to ensure accurate attention estimation. Our attention based
embedding model is learned end-to-end guided by a max-margin loss function. We
show that our model outperforms other baselines on the benchmark Ad dataset and
also show qualitative results to highlight the advantages of using multihop
co-attention.
Modeling Sparse Deviations for Compressed Sensing using Generative Models
Authors: Manik Dhar, Aditya Grover, Stefano Ermon
Date: 2018-07-04 00:00:00
Proceeding: ICML 2018 7
Tasks: compressed sensing

In compressed sensing, a small number of linear measurements can be used to
reconstruct an unknown signal. Existing approaches leverage assumptions on the
structure of these signals, such as sparsity or the availability of a
generative model. A domain-specific generative model can provide a stronger
prior and thus allow for recovery with far fewer measurements. However, unlike
sparsity-based approaches, existing methods based on generative models
guarantee exact recovery only over their support, which is typically only a
small subset of the space on which the signals are defined. We propose
Sparse-Gen, a framework that allows for sparse deviations from the support set,
thereby achieving the best of both worlds by using a domain specific prior and
allowing reconstruction over the full space of signals. Theoretically, our
framework provides a new class of signals that can be acquired using compressed
sensing, reducing classic sparse vector recovery to a special case and avoiding
the restrictive support due to a generative model prior. Empirically, we
observe consistent improvements in reconstruction accuracy over competing
approaches, especially in the more practical setting of transfer compressed
sensing where a generative model for a data-rich, source domain aids sensing on
a data-scarce, target domain.
Multi-task Mid-level Feature Alignment Network for Unsupervised Cross-Dataset Person Re-Identification
Authors: Shan Lin, Haoliang Li, Chang-Tsun Li, Alex ChiChung Kot
Date: 2018-07-04 00:00:00
Tasks: Attribute, Person Re-Identification

Most existing person re-identification (Re-ID) approaches follow a supervised
learning framework, in which a large number of labelled matching pairs are
required for training. Such a setting severely limits their scalability in
real-world applications where no labelled samples are available during the
training phase. To overcome this limitation, we develop a novel unsupervised
Multi-task Mid-level Feature Alignment (MMFA) network for the unsupervised
cross-dataset person re-identification task. Under the assumption that the
source and target datasets share the same set of mid-level semantic attributes,
our proposed model can be jointly optimised under the person's identity
classification and the attribute learning task with a cross-dataset mid-level
feature alignment regularisation term. In this way, the learned feature
representation can be better generalised from one dataset to another which
further improve the person re-identification accuracy. Experimental results on
four benchmark datasets demonstrate that our proposed method outperforms the
state-of-the-art baselines.
Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation
Authors: Tao Song, Leiyu Sun, Di Xie, Haiming Sun, ShiLiang Pu
Date: 2018-07-04 00:00:00
Tasks: Pedestrian Detection

A critical issue in pedestrian detection is to detect small-scale objects
that will introduce feeble contrast and motion blur in images and videos, which
in our opinion should partially resort to deep-rooted annotation bias.
Motivated by this, we propose a novel method integrated with somatic
topological line localization (TLL) and temporal feature aggregation for
detecting multi-scale pedestrians, which works particularly well with
small-scale pedestrians that are relatively far from the camera. Moreover, a
post-processing scheme based on Markov Random Field (MRF) is introduced to
eliminate ambiguities in occlusion cases. Applying with these methodologies
comprehensively, we achieve best detection performance on Caltech benchmark and
improve performance of small-scale objects significantly (miss rate decreases
from 74.53% to 60.79%). Beyond this, we also achieve competitive performance on
CityPersons dataset and show the existence of annotation bias in KITTI dataset.
SGAD: Soft-Guided Adaptively-Dropped Neural Network
Authors: Zhisheng Wang, Fangxuan Sun, Jun Lin, Zhongfeng Wang, Bo Yuan
Date: 2018-07-04 00:00:00
Tasks: Model Compression

Deep neural networks (DNNs) have been proven to have many redundancies.
Hence, many efforts have been made to compress DNNs. However, the existing
model compression methods treat all the input samples equally while ignoring
the fact that the difficulties of various input samples being correctly
classified are different. To address this problem, DNNs with adaptive dropping
mechanism are well explored in this work. To inform the DNNs how difficult the
input samples can be classified, a guideline that contains the information of
input samples is introduced to improve the performance. Based on the developed
guideline and adaptive dropping mechanism, an innovative soft-guided
adaptively-dropped (SGAD) neural network is proposed in this paper. Compared
with the 32 layers residual neural networks, the presented SGAD can reduce the
FLOPs by 77% with less than 1% drop in accuracy on CIFAR-10.
Region Growing Curriculum Generation for Reinforcement Learning
Authors: Artem Molchanov, Karol Hausman, Stan Birchfield, Gaurav Sukhatme
Date: 2018-07-04 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Learning a policy capable of moving an agent between any two states in the
environment is important for many robotics problems involving navigation and
manipulation. Due to the sparsity of rewards in such tasks, applying
reinforcement learning in these scenarios can be challenging. Common approaches
for tackling this problem include reward engineering with auxiliary rewards,
requiring domain-specific knowledge or changing the objective.
  In this work, we introduce a method based on region-growing that allows
learning in an environment with any pair of initial and goal states. Our
algorithm first learns how to move between nearby states and then increases the
difficulty of the start-goal transitions as the agent's performance improves.
This approach creates an efficient curriculum for learning the objective
behavior of reaching any goal from any initial state. In addition, we describe
a method to adaptively adjust expansion of the growing region that allows
automatic adjustment of the key exploration hyperparameter to environments with
different requirements. We evaluate our approach on a set of simulated
navigation and manipulation tasks, where we demonstrate that our algorithm can
efficiently learn a policy in the presence of sparse rewards.
Unbiased Image Style Transfer
Authors: Hyun-Chul Choi, Minseong Kim
Date: 2018-07-04 00:00:00
Tasks: regression, Style Transfer

Recent fast image style transferring methods use feed-forward neural networks
to generate an output image of desired style strength from the input pair of a
content and a target style image. In the existing methods, the image of
intermediate style between the content and the target style is obtained by
decoding a linearly interpolated feature in encoded feature space. However,
there has been no work on analyzing the effectiveness of this kind of style
strength interpolation so far. In this paper, we tackle the missing work on the
in-depth analysis of style interpolation and propose a method that is more
effective in controlling style strength. We interpret the training task of a
style transfer network as a regression learning between the control parameter
and output style strength. In this understanding, the existing methods are
biased due to the fact that training is performed with one-sided data of full
style strength (alpha = 1.0). Thus, this biased learning does not guarantee the
generation of a desired intermediate style corresponding to the style control
parameter between 0.0 and 1.0. To solve this problem of the biased network, we
propose an unbiased learning technique which uses unbiased training data and
corresponding unbiased loss for alpha = 0.0 to make the feed-forward networks
to generate a zero-style image, i.e., content image when alpha = 0.0. Our
experimental results verified that our unbiased learning method achieved the
reconstruction of a content image with zero style strength, better regression
specification between style control parameter and output style, and more stable
style transfer that is insensitive to the weight of style loss without additive
complexity in image generating process.
Diagonal Discriminant Analysis with Feature Selection for High Dimensional Data
Authors: Sarah Elizabeth Romanes, John Thomas Ormerod, Jean YH Yang
Date: 2018-07-04 00:00:00
Tasks: feature selection, General Classification, Two-sample testing, Vocal Bursts Intensity Prediction

We introduce a new method of performing high dimensional discriminant
analysis, which we call multiDA. We achieve this by constructing a hybrid model
that seamlessly integrates a multiclass diagonal discriminant analysis model
and feature selection components. Our feature selection component naturally
simplifies to weights which are simple functions of likelihood ratio statistics
allowing natural comparisons with traditional hypothesis testing methods. We
provide heuristic arguments suggesting desirable asymptotic properties of our
algorithm with regards to feature selection. We compare our method with several
other approaches, showing marked improvements in regard to prediction accuracy,
interpretability of chosen features, and algorithm run time. We demonstrate
such strengths of our model by showing strong classification performance on
publicly available high dimensional datasets, as well as through multiple
simulation studies. We make an R package available implementing our approach.
Selective Deep Convolutional Neural Network for Low Cost Distorted Image Classification
Authors: Minho Ha, Younghoon Byeon, Youngjoo Lee, Sunggu Lee
Date: 2018-07-04 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Deep convolutional neural networks have proven to be well suited for image
classification applications. However, if there is distortion in the image, the
classification accuracy can be significantly degraded, even with
state-of-the-art neural networks. The accuracy cannot be significantly improved
by simply training with distorted images. Instead, this paper proposes a
multiple neural network topology referred to as a selective deep convolutional
neural network. By modifying existing state-of-the-art neural networks in the
proposed manner, it is shown that a similar level of classification accuracy
can be achieved, but at a significantly lower cost. The cost reduction is
obtained primarily through the use of fewer weight parameters. Using fewer
weights reduces the number of multiply-accumulate operations and also reduces
the energy required for data accesses. Finally, it is shown that the
effectiveness of the proposed selective deep convolutional neural network can
be further improved by combining it with previously proposed network cost
reduction methods.
Connecting Weighted Automata and Recurrent Neural Networks through Spectral Learning
Authors: Guillaume Rabusseau, Tianyu Li, Doina Precup
Date: 2018-07-04 00:00:00

In this paper, we unravel a fundamental connection between weighted finite
automata~(WFAs) and second-order recurrent neural networks~(2-RNNs): in the
case of sequences of discrete symbols, WFAs and 2-RNNs with linear activation
functions are expressively equivalent. Motivated by this result, we build upon
a recent extension of the spectral learning algorithm to vector-valued WFAs and
propose the first provable learning algorithm for linear 2-RNNs defined over
sequences of continuous input vectors. This algorithm relies on estimating low
rank sub-blocks of the so-called Hankel tensor, from which the parameters of a
linear 2-RNN can be provably recovered. The performances of the proposed method
are assessed in a simulation study.
Endmember Extraction on the Grassmannian
Authors: Elin Farnell, Henry Kvinge, Michael Kirby, Chris Peterson
Date: 2018-07-03 00:00:00

Endmember extraction plays a prominent role in a variety of data analysis
problems as endmembers often correspond to data representing the purest or best
representative of some feature. Identifying endmembers then can be useful for
further identification and classification tasks. In settings with
high-dimensional data, such as hyperspectral imagery, it can be useful to
consider endmembers that are subspaces as they are capable of capturing a wider
range of variations of a signature. The endmember extraction problem in this
setting thus translates to finding the vertices of the convex hull of a set of
points on a Grassmannian. In the presence of noise, it can be less clear
whether a point should be considered a vertex. In this paper, we propose an
algorithm to extract endmembers on a Grassmannian, identify subspaces of
interest that lie near the boundary of a convex hull, and demonstrate the use
of the algorithm on a synthetic example and on the 220 spectral band AVIRIS
Indian Pines hyperspectral image.
Simpler but More Accurate Semantic Dependency Parsing
Authors: Timothy Dozat, Christopher D. Manning
Date: 2018-07-03 00:00:00
Proceeding: ACL 2018 7
Tasks: Dependency Parsing, Semantic Dependency Parsing, Sentence

While syntactic dependency annotations concentrate on the surface or
functional structure of a sentence, semantic dependency annotations aim to
capture between-word relationships that are more closely related to the meaning
of a sentence, using graph-structured representations. We extend the LSTM-based
syntactic parser of Dozat and Manning (2017) to train on and generate these
graph structures. The resulting system on its own achieves state-of-the-art
performance, beating the previous, substantially more complex state-of-the-art
system by 0.6% labeled F1. Adding linguistically richer input representations
pushes the margin even higher, allowing us to beat it by 1.9% labeled F1.
Patient representation learning and interpretable evaluation using clinical notes
Authors: Madhumita Sushil, Simon Šuster, Kim Luyckx, Walter Daelemans
Date: 2018-07-03 00:00:00
Tasks: Denoising, Diagnostic, General Classification, Representation Learning

We have three contributions in this work: 1. We explore the utility of a
stacked denoising autoencoder and a paragraph vector model to learn
task-independent dense patient representations directly from clinical notes. To
analyze if these representations are transferable across tasks, we evaluate
them in multiple supervised setups to predict patient mortality, primary
diagnostic and procedural category, and gender. We compare their performance
with sparse representations obtained from a bag-of-words model. We observe that
the learned generalized representations significantly outperform the sparse
representations when we have few positive instances to learn from, and there is
an absence of strong lexical features. 2. We compare the model performance of
the feature set constructed from a bag of words to that obtained from medical
concepts. In the latter case, concepts represent problems, treatments, and
tests. We find that concept identification does not improve the classification
performance. 3. We propose novel techniques to facilitate model
interpretability. To understand and interpret the representations, we explore
the best encoded features within the patient representations obtained from the
autoencoder model. Further, we calculate feature sensitivity across two
networks to identify the most significant input features for different
classification tasks when we use these pretrained representations as the
supervised input. We successfully extract the most influential features for the
pipeline using this technique.
ModaNet: A Large-Scale Street Fashion Dataset with Polygon Annotations
Authors: Shuai Zheng, Fan Yang, M. Hadi Kiapour, Robinson Piramuthu
Date: 2018-07-03 00:00:00
Tasks: Fashion Understanding, object-detection, Object Detection, Semantic Segmentation

Understanding clothes from a single image has strong commercial and cultural
impacts on modern societies. However, this task remains a challenging computer
vision problem due to wide variations in the appearance, style, brand and
layering of clothing items. We present a new database called ModaNet, a
large-scale collection of images based on Paperdoll dataset. Our dataset
provides 55,176 street images, fully annotated with polygons on top of the 1
million weakly annotated street images in Paperdoll. ModaNet aims to provide a
technical benchmark to fairly evaluate the progress of applying the latest
computer vision techniques that rely on large data for fashion understanding.
The rich annotation of the dataset allows to measure the performance of
state-of-the-art algorithms for object detection, semantic segmentation and
polygon prediction on street fashion images in detail. The polygon-based
annotation dataset has been released https://github.com/eBay/modanet, we also
host the leaderboard at EvalAI:
https://evalai.cloudcv.org/featured-challenges/136/overview.
Efficient ConvNets for Analog Arrays
Authors: Malte J. Rasch, Tayfun Gokmen, Mattia Rigotti, Wilfried Haensch
Date: 2018-07-03 00:00:00

Analog arrays are a promising upcoming hardware technology with the potential
to drastically speed up deep learning. Their main advantage is that they
compute matrix-vector products in constant time, irrespective of the size of
the matrix. However, early convolution layers in ConvNets map very unfavorably
onto analog arrays, because kernel matrices are typically small and the
constant time operation needs to be sequentially iterated a large number of
times, reducing the speed up advantage for ConvNets. Here, we propose to
replicate the kernel matrix of a convolution layer on distinct analog arrays,
and randomly divide parts of the compute among them, so that multiple kernel
matrices are trained in parallel. With this modification, analog arrays execute
ConvNets with an acceleration factor that is proportional to the number of
kernel matrices used per layer (here tested 16-128). Despite having more free
parameters, we show analytically and in numerical experiments that this
convolution architecture is self-regularizing and implicitly learns similar
filters across arrays. We also report superior performance on a number of
datasets and increased robustness to adversarial attacks. Our investigation
suggests to revise the notion that mixed analog-digital hardware is not
suitable for ConvNets.