pwc-archive/papers-with-abstracts huggingface dataset:
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
Authors: Chenxi Liu, Liang-Chieh Chen, Florian Schroff, Hartwig Adam, Wei Hua, Alan Yuille, Li Fei-Fei
Date: 2019-01-10 00:00:00
Proceeding: CVPR 2019 6
Tasks: GPU, image-classification, Image Classification, Image Segmentation, Neural Architecture Search, Segmentation, Semantic Segmentation

Recently, Neural Architecture Search (NAS) has successfully identified neural
network architectures that exceed human designed ones on large-scale image
classification. In this paper, we study NAS for semantic image segmentation.
Existing works often focus on searching the repeatable cell structure, while
hand-designing the outer network structure that controls the spatial resolution
changes. This choice simplifies the search space, but becomes increasingly
problematic for dense image prediction which exhibits a lot more network level
architectural variations. Therefore, we propose to search the network level
structure in addition to the cell level structure, which forms a hierarchical
architecture search space. We present a network level search space that
includes many popular designs, and develop a formulation that allows efficient
gradient-based architecture search (3 P100 GPU days on Cityscapes images). We
demonstrate the effectiveness of the proposed method on the challenging
Cityscapes, PASCAL VOC 2012, and ADE20K datasets. Auto-DeepLab, our
architecture searched specifically for semantic image segmentation, attains
state-of-the-art performance without any ImageNet pretraining.
A Multi-Object Rectified Attention Network for Scene Text Recognition
Authors: Canjie Luo, Lianwen Jin, Zenghui Sun
Date: 2019-01-10 00:00:00
Tasks: Decoder, Object, Optical Character Recognition (OCR), Scene Text Recognition

Irregular text is widely used. However, it is considerably difficult to
recognize because of its various shapes and distorted patterns. In this paper,
we thus propose a multi-object rectified attention network (MORAN) for general
scene text recognition. The MORAN consists of a multi-object rectification
network and an attention-based sequence recognition network. The multi-object
rectification network is designed for rectifying images that contain irregular
text. It decreases the difficulty of recognition and enables the
attention-based sequence recognition network to more easily read irregular
text. It is trained in a weak supervision way, thus requiring only images and
corresponding text labels. The attention-based sequence recognition network
focuses on target characters and sequentially outputs the predictions.
Moreover, to improve the sensitivity of the attention-based sequence
recognition network, a fractional pickup method is proposed for an
attention-based decoder in the training phase. With the rectification
mechanism, the MORAN can read both regular and irregular scene text. Extensive
experiments on various benchmarks are conducted, which show that the MORAN
achieves state-of-the-art performance. The source code is available.
Multi-feature Distance Metric Learning for Non-rigid 3D Shape Retrieval
Authors: Huibing Wang, Haohao Li, Xianping Fu
Date: 2019-01-10 00:00:00
Tasks: 3D Shape Classification, 3D Shape Retrieval, Metric Learning, Retrieval

In the past decades, feature-learning-based 3D shape retrieval approaches
have been received widespread attention in the computer graphic community.
These approaches usually explored the hand-crafted distance metric or
conventional distance metric learning methods to compute the similarity of the
single feature. The single feature always contains onefold geometric
information, which cannot characterize the 3D shapes well. Therefore, the
multiple features should be used for the retrieval task to overcome the
limitation of single feature and further improve the performance. However, most
conventional distance metric learning methods fail to integrate the
complementary information from multiple features to construct the distance
metric. To address these issue, a novel multi-feature distance metric learning
method for non-rigid 3D shape retrieval is presented in this study, which can
make full use of the complimentary geometric information from multiple shape
features by utilizing the KL-divergences. Minimizing KL-divergence between
different metric of features and a common metric is a consistency constraints,
which can lead the consistency shared latent feature space of the multiple
features. We apply the proposed method to 3D model retrieval, and test our
method on well known benchmark database. The results show that our method
substantially outperforms the state-of-the-art non-rigid 3D shape retrieval
methods.
Image Transformation can make Neural Networks more robust against Adversarial Examples
Authors: Dang Duy Thang, Toshihiro Matsui
Date: 2019-01-10 00:00:00

Neural networks are being applied in many tasks related to IoT with
encouraging results. For example, neural networks can precisely detect human,
objects and animal via surveillance camera for security purpose. However,
neural networks have been recently found vulnerable to well-designed input
samples that called adversarial examples. Such issue causes neural networks to
misclassify adversarial examples that are imperceptible to humans. We found
giving a rotation to an adversarial example image can defeat the effect of
adversarial examples. Using MNIST number images as the original images, we
first generated adversarial examples to neural network recognizer, which was
completely fooled by the forged examples. Then we rotated the adversarial image
and gave them to the recognizer to find the recognizer to regain the correct
recognition. Thus, we empirically confirmed rotation to images can protect
pattern recognizer based on neural networks from adversarial example attacks.
Hierarchy Neighborhood Discriminative Hashing for An Unified View of Single-Label and Multi-Label Image retrieval
Authors: Lei Ma, Hongliang Li, Qingbo Wu, Fanman Meng, King Ngi Ngan
Date: 2019-01-10 00:00:00
Tasks: Image Retrieval, Multi-Label Image Retrieval, Retrieval, Semantic Similarity, Semantic Textual Similarity, Triplet

Recently, deep supervised hashing methods have become popular for large-scale
image retrieval task. To preserve the semantic similarity notion between
examples, they typically utilize the pairwise supervision or the triplet
supervised information for hash learning. However, these methods usually ignore
the semantic class information which can help the improvement of the semantic
discriminative ability of hash codes. In this paper, we propose a novel
hierarchy neighborhood discriminative hashing method. Specifically, we
construct a bipartite graph to build coarse semantic neighbourhood relationship
between the sub-class feature centers and the embeddings features. Moreover, we
utilize the pairwise supervised information to construct the fined semantic
neighbourhood relationship between embeddings features. Finally, we propose a
hierarchy neighborhood discriminative hashing loss to unify the single-label
and multilabel image retrieval problem with a one-stream deep neural network
architecture. Experimental results on two largescale datasets demonstrate that
the proposed method can outperform the state-of-the-art hashing methods.
PVSS: A Progressive Vehicle Search System for Video Surveillance Networks
Authors: Xinchen Liu, Wu Liu, Huadong Ma, Shuangqun Li
Date: 2019-01-10 00:00:00
Tasks: Attribute, Triplet, vehicle detection

This paper is focused on the task of searching for a specific vehicle that
appeared in the surveillance networks. Existing methods usually assume the
vehicle images are well cropped from the surveillance videos, then use visual
attributes, like colors and types, or license plate numbers to match the target
vehicle in the image set. However, a complete vehicle search system should
consider the problems of vehicle detection, representation, indexing, storage,
matching, and so on. Besides, attribute-based search cannot accurately find the
same vehicle due to intra-instance changes in different cameras and the
extremely uncertain environment. Moreover, the license plates may be
misrecognized in surveillance scenes due to the low resolution and noise. In
this paper, a Progressive Vehicle Search System, named as PVSS, is designed to
solve the above problems. PVSS is constituted of three modules: the crawler,
the indexer, and the searcher. The vehicle crawler aims to detect and track
vehicles in surveillance videos and transfer the captured vehicle images,
metadata and contextual information to the server or cloud. Then multi-grained
attributes, such as the visual features and license plate fingerprints, are
extracted and indexed by the vehicle indexer. At last, a query triplet with an
input vehicle image, the time range, and the spatial scope is taken as the
input by the vehicle searcher. The target vehicle will be searched in the
database by a progressive process. Extensive experiments on the public dataset
from a real surveillance network validate the effectiveness of the PVSS.
Multi-Granularity Reasoning for Social Relation Recognition from Images
Authors: Meng Zhang, Xinchen Liu, Wu Liu, Anfu Zhou, Huadong Ma, Tao Mei
Date: 2019-01-10 00:00:00
Tasks: Relation, Visual Social Relationship Recognition

Discovering social relations in images can make machines better interpret the
behavior of human beings. However, automatically recognizing social relations
in images is a challenging task due to the significant gap between the domains
of visual content and social relation. Existing studies separately process
various features such as faces expressions, body appearance, and contextual
objects, thus they cannot comprehensively capture the multi-granularity
semantics, such as scenes, regional cues of persons, and interactions among
persons and objects. To bridge the domain gap, we propose a Multi-Granularity
Reasoning framework for social relation recognition from images. The global
knowledge and mid-level details are learned from the whole scene and the
regions of persons and objects, respectively. Most importantly, we explore the
fine-granularity pose keypoints of persons to discover the interactions among
persons and objects. Specifically, the pose-guided Person-Object Graph and
Person-Pose Graph are proposed to model the actions from persons to object and
the interactions between paired persons, respectively. Based on the graphs,
social relation reasoning is performed by graph convolutional networks.
Finally, the global features and reasoned knowledge are integrated as a
comprehensive representation for social relation recognition. Extensive
experiments on two public datasets show the effectiveness of the proposed
framework.
New Radon Transform Based Texture Features of Handwritten Document
Authors: Rustam Latypov, Evgeni Stolov
Date: 2019-01-10 00:00:00
Tasks: General Classification

In this paper, we present some new features describing the handwritten
document as a texture. These features are based on the Radon transform. All
values can be obtained easily and suit for the coarse classification of
documents.
Fast GPU-Enabled Color Normalization for Digital Pathology
Authors: Goutham Ramakrishnan, Deepak Anand, Amit Sethi
Date: 2019-01-10 00:00:00
Tasks: Color Normalization, GPU, whole slide images

Normalizing unwanted color variations due to differences in staining
processes and scanner responses has been shown to aid machine learning in
computational pathology. Of the several popular techniques for color
normalization, structure preserving color normalization (SPCN) is
well-motivated, convincingly tested, and published with its code base. However,
SPCN makes occasional errors in color basis estimation leading to artifacts
such as swapping the color basis vectors between stains or giving a colored
tinge to the background with no tissue. We made several algorithmic
improvements to remove these artifacts. Additionally, the original SPCN code is
not readily usable on gigapixel whole slide images (WSIs) due to long run
times, use of proprietary software platform and libraries, and its inability to
automatically handle WSIs. We completely rewrote the software such that it can
automatically handle images of any size in popular WSI formats. Our software
utilizes GPU-acceleration and open-source libraries that are becoming
ubiquitous with the advent of deep learning. We also made several other small
improvements and achieved a multifold overall speedup on gigapixel images. Our
algorithm and software is usable right out-of-the-box by the computational
pathology community.
Cricket stroke extraction: Towards creation of a large-scale cricket actions dataset
Authors: Arpan Gupta, Sakthi Balan M
Date: 2019-01-10 00:00:00
Tasks: Action Localization, Game of Cricket, Temporal Action Localization

In this paper, we deal with the problem of temporal action localization for a
large-scale untrimmed cricket videos dataset. Our action of interest for
cricket videos is a cricket stroke played by a batsman, which is, usually,
covered by cameras placed at the stands of the cricket ground at both ends of
the cricket pitch. After applying a sequence of preprocessing steps, we have
~73 million frames for 1110 videos in the dataset at constant frame rate and
resolution. The method of localization is a generalized one which applies a
trained random forest model for CUTs detection(using summed up grayscale
histogram difference features) and two linear SVM camera models(CAM1 and CAM2)
for first frame detection, trained on HOG features of CAM1 and CAM2 video
shots. CAM1 and CAM2 are assumed to be part of the cricket stroke. At the
predicted boundary positions, the HOG features of the first frames are computed
and a simple algorithm was used to combine the positively predicted camera
shots. In order to make the process as generic as possible, we did not consider
any domain specific knowledge, such as tracking or specific shape and motion
features.
  The detailed analysis of our methodology is provided along with the metrics
used for evaluation of individual models, and the final predicted segments. We
achieved a weighted mean TIoU of 0.5097 over a small sample of the test set.
On Finding Gray Pixels
Authors: Yanlin Qian, Joni-Kristian Kämäräinen, Jarno Nikkanen, Jiri Matas
Date: 2019-01-09 00:00:00
Proceeding: CVPR 2019 6

We propose a novel grayness index for finding gray pixels and demonstrate its effectiveness and efficiency in illumination estimation. The grayness index, GI in short, is derived using the Dichromatic Reflection Model and is learning-free. GI allows to estimate one or multiple illumination sources in color-biased images. On standard single-illumination and multiple-illumination estimation benchmarks, GI outperforms state-of-the-art statistical methods and many recent deep methods. GI is simple and fast, written in a few dozen lines of code, processing a 1080p image in ~0.4 seconds with a non-optimized Matlab code.
Early recurrence enables figure border ownership
Authors: Paria Mehrani, John K. Tsotsos
Date: 2019-01-10 00:00:00

The face-vase illusion introduced by Rubin demonstrates how one can switch back and forth between two different interpretations depending on how the figure outlines are assigned [1]. This border ownership assignment is an important step in the perception of forms. Zhou et al. [2] found neurons in the visual cortex whose responses not only depend on the local features present in their classical receptive fields, but also on their contextual information. Various models proposed that feedback from higher ventral areas or lateral connections could provide the required contextual information. However, some studies [3, 4, 5] ruled out the plausibility of models exclusively based on lateral connections. In addition, further evidence [6] suggests that ventral feedback even from V4 is not fast enough to provide context to border ownership neurons in either V1 or V2. As a result, the border ownership assignment mechanism in the brain is a mystery yet to be solved. Here, we test with computational simulations the hypothesis that the dorsal stream provides the global information to border ownership cells in the ventral stream. Our proposed model incorporates early recurrence from the dorsal pathway as well as lateral modulations within the ventral stream. Our simulation experiments show that our model border ownership neurons, similar to their biological counterparts, exhibit different responses to figures on either side of the border.
Region Proposal by Guided Anchoring
Authors: Jiaqi Wang, Kai Chen, Shuo Yang, Chen Change Loy, Dahua Lin
Date: 2019-01-10 00:00:00
Proceeding: CVPR 2019 6
Tasks: object-detection, Object Detection, Region Proposal

Region anchors are the cornerstone of modern object detection techniques.
State-of-the-art detectors mostly rely on a dense anchoring scheme, where
anchors are sampled uniformly over the spatial domain with a predefined set of
scales and aspect ratios. In this paper, we revisit this foundational stage.
Our study shows that it can be done much more effectively and efficiently.
Specifically, we present an alternative scheme, named Guided Anchoring, which
leverages semantic features to guide the anchoring. The proposed method jointly
predicts the locations where the center of objects of interest are likely to
exist as well as the scales and aspect ratios at different locations. On top of
predicted anchor shapes, we mitigate the feature inconsistency with a feature
adaption module. We also study the use of high-quality proposals to improve
detection performance. The anchoring scheme can be seamlessly integrated into
proposal methods and detectors. With Guided Anchoring, we achieve 9.1% higher
recall on MS COCO with 90% fewer anchors than the RPN baseline. We also adopt
Guided Anchoring in Fast R-CNN, Faster R-CNN and RetinaNet, respectively
improving the detection mAP by 2.2%, 2.7% and 1.2%. Code will be available at
https://github.com/open-mmlab/mmdetection.
Multispectral and Hyperspectral Image Fusion by MS/HS Fusion Net
Authors: Qi Xie, Minghao Zhou, Qian Zhao, Deyu Meng, WangMeng Zuo, Zongben Xu
Date: 2019-01-10 00:00:00
Proceeding: CVPR 2019 6

Hyperspectral imaging can help better understand the characteristics of
different materials, compared with traditional image systems. However, only
high-resolution multispectral (HrMS) and low-resolution hyperspectral (LrHS)
images can generally be captured at video rate in practice. In this paper, we
propose a model-based deep learning approach for merging an HrMS and LrHS
images to generate a high-resolution hyperspectral (HrHS) image. In specific,
we construct a novel MS/HS fusion model which takes the observation models of
low-resolution images and the low-rankness knowledge along the spectral mode of
HrHS image into consideration. Then we design an iterative algorithm to solve
the model by exploiting the proximal gradient method. And then, by unfolding
the designed algorithm, we construct a deep network, called MS/HS Fusion Net,
with learning the proximal operators and model parameters by convolutional
neural networks. Experimental results on simulated and real data substantiate
the superiority of our method both visually and quantitatively as compared with
state-of-the-art methods along this line of research.
Automating the search for a patent's prior art with a full text similarity search
Authors: Lea Helmers, Franziska Horn, Franziska Biegler, Tim Oppermann, Klaus-Robert Müller
Date: 2019-01-10 00:00:00
Tasks: text similarity

More than ever, technical inventions are the symbol of our society's advance.
Patents guarantee their creators protection against infringement. For an
invention being patentable, its novelty and inventiveness have to be assessed.
Therefore, a search for published work that describes similar inventions to a
given patent application needs to be performed. Currently, this so-called
search for prior art is executed with semi-automatically composed keyword
queries, which is not only time consuming, but also prone to errors. In
particular, errors may systematically arise by the fact that different keywords
for the same technical concepts may exist across disciplines. In this paper, a
novel approach is proposed, where the full text of a given patent application
is compared to existing patents using machine learning and natural language
processing techniques to automatically detect inventions that are similar to
the one described in the submitted document. Various state-of-the-art
approaches for feature extraction and document comparison are evaluated. In
addition to that, the quality of the current search process is assessed based
on ratings of a domain expert. The evaluation results show that our automated
approach, besides accelerating the search process, also improves the search
results for prior art with respect to their quality.
Quantum-inspired sublinear algorithm for solving low-rank semidefinite programming
Authors: Nai-Hui Chia, Tongyang Li, Han-Hsuan Lin, Chunhao Wang
Date: 2019-01-10 00:00:00

Semidefinite programming (SDP) is a central topic in mathematical optimization with extensive studies on its efficient solvers. In this paper, we present a proof-of-principle sublinear-time algorithm for solving SDPs with low-rank constraints; specifically, given an SDP with $m$ constraint matrices, each of dimension $n$ and rank $r$, our algorithm can compute any entry and efficient descriptions of the spectral decomposition of the solution matrix. The algorithm runs in time $O(m\cdot\mathrm{poly}(\log n,r,1/\varepsilon))$ given access to a sampling-based low-overhead data structure for the constraint matrices, where $\varepsilon$ is the precision of the solution. In addition, we apply our algorithm to a quantum state learning task as an application. Technically, our approach aligns with 1) SDP solvers based on the matrix multiplicative weight (MMW) framework by Arora and Kale [TOC '12]; 2) sampling-based dequantizing framework pioneered by Tang [STOC '19]. In order to compute the matrix exponential required in the MMW framework, we introduce two new techniques that may be of independent interest: $\bullet$ Weighted sampling: assuming sampling access to each individual constraint matrix $A_{1},\ldots,A_{\tau}$, we propose a procedure that gives a good approximation of $A=A_{1}+\cdots+A_{\tau}$. $\bullet$ Symmetric approximation: we propose a sampling procedure that gives the \emph{spectral decomposition} of a low-rank Hermitian matrix $A$. To the best of our knowledge, this is the first sampling-based algorithm for spectral decomposition, as previous works only give singular values and vectors.