pwc-archive/papers-with-abstracts huggingface dataset:
Multi-scale Cross-form Pyramid Network for Stereo Matching
Authors: Zhidong Zhu, Mingyi He, Yuchao Dai, Zhibo Rao, Bo Li
Date: 2019-04-25 00:00:00
Tasks: 3D Feature Matching, 3D Scene Reconstruction, Autonomous Driving, Form, Stereo Matching, Stereo Matching Hand

Stereo matching plays an indispensable part in autonomous driving, robotics and 3D scene reconstruction. We propose a novel deep learning architecture, which called CFP-Net, a Cross-Form Pyramid stereo matching network for regressing disparity from a rectified pair of stereo images. The network consists of three modules: Multi-Scale 2D local feature extraction module, Cross-form spatial pyramid module and Multi-Scale 3D Feature Matching and Fusion module. The Multi-Scale 2D local feature extraction module can extract enough multi-scale features. The Cross-form spatial pyramid module aggregates the context information in different scales and locations to form a cost volume. Moreover, it is proved to be more effective than SPP and ASPP in ill-posed regions. The Multi-Scale 3D feature matching and fusion module is proved to regularize the cost volume using two parallel 3D deconvolution structure with two different receptive fields. Our proposed method has been evaluated on the Scene Flow and KITTI datasets. It achieves state-of-the-art performance on the KITTI 2012 and 2015 benchmarks.
Deep Iterative Reconstruction for Phase Retrieval
Authors: Çağatay Işıl, Figen S. Oktem, Aykut Koç
Date: 2019-04-25 00:00:00
Tasks: Denoising, Retrieval

Classical phase retrieval problem is the recovery of a constrained image from the magnitude of its Fourier transform. Although there are several well-known phase retrieval algorithms including the hybrid input-output (HIO) method, the reconstruction performance is generally sensitive to initialization and measurement noise. Recently, deep neural networks (DNNs) have been shown to provide state-of-the-art performance in solving several inverse problems such as denoising, deconvolution, and superresolution. In this work, we develop a phase retrieval algorithm that utilizes two DNNs together with the model-based HIO method. First, a DNN is trained to remove the HIO artifacts and is used iteratively with the HIO method to improve the reconstructions. After this iterative phase, a second DNN is trained to remove the remaining artifacts. Numerical results demonstrate the effectiveness of ourapproach, which has little additional computational cost compared to the HIO method. Our approach not only achieves state-of-the-art reconstruction performance but also is more robust to different initialization and noise levels.
Combining Anatomical and Functional Networks for Neuropathology Identification: A Case Study on Autism Spectrum Disorder
Authors: Sarah Itani, Dorina Thanou
Date: 2019-04-25 00:00:00
Tasks: General Classification

While the prevalence of Autism Spectrum Disorder (ASD) is increasing, research continues in an effort to identify common etiological and pathophysiological bases. In this regard, modern machine learning and network science pave the way for a better understanding of the neuropathology and the development of diagnosis aid systems. The present work addresses the classification of neurotypical and ASD subjects by combining knowledge about both the structure and the functional activity of the brain. In particular, we model the brain structure as a graph, and the resting-state functional MRI (rs-fMRI) signals as values that live on the nodes of that graph. We then borrow tools from the emerging field of Graph Signal Processing (GSP) to build features related to the frequency content of these signals. In order to make these features highly discriminative, we apply an extension of the Fukunaga-Koontz transform. Finally, we use these new markers to train a decision tree, an interpretable classification scheme, which results in a final diagnosis aid model. Interestingly, the resulting decision tree outperforms state-of-the-art methods on the publicly available Autism Brain Imaging Data Exchange (ABIDE) collection. Moreover, the analysis of the predictive markers reveals the influence of the frontal and temporal lobes in the diagnosis of the disorder, which is in line with previous findings in the literature of neuroscience. Our results indicate that exploiting jointly structural and functional information of the brain can reveal important information about the complexity of the neuropathology.
Reducing Anomaly Detection in Images to Detection in Noise
Authors: Axel Davy, Thibaud Ehret, Jean-Michel Morel, Mauricio Delbracio
Date: 2019-04-25 00:00:00
Tasks: Anomaly Detection

Anomaly detectors address the difficult problem of detecting automatically
exceptions in an arbitrary background image. Detection methods have been
proposed by the thousands because each problem requires a different background
model. By analyzing the existing approaches, we show that the problem can be
reduced to detecting anomalies in residual images (extracted from the target
image) in which noise and anomalies prevail. Hence, the general and impossible
background modeling problem is replaced by simpler noise modeling, and allows
the calculation of rigorous thresholds based on the a contrario detection
theory. Our approach is therefore unsupervised and works on arbitrary images.
LADN: Local Adversarial Disentangling Network for Facial Makeup and De-Makeup
Authors: Qiao Gu, Guanzhi Wang, Mang Tik Chiu, Yu-Wing Tai, Chi-Keung Tang
Date: 2019-04-25 00:00:00
Proceeding: ICCV 2019 10
Tasks: Style Transfer

We propose a local adversarial disentangling network (LADN) for facial makeup and de-makeup. Central to our method are multiple and overlapping local adversarial discriminators in a content-style disentangling network for achieving local detail transfer between facial images, with the use of asymmetric loss functions for dramatic makeup styles with high-frequency details. Existing techniques do not demonstrate or fail to transfer high-frequency details in a global adversarial setting, or train a single local discriminator only to ensure image structure consistency and thus work only for relatively simple styles. Unlike others, our proposed local adversarial discriminators can distinguish whether the generated local image details are consistent with the corresponding regions in the given reference image in cross-image style transfer in an unsupervised setting. Incorporating these technical contributions, we achieve not only state-of-the-art results on conventional styles but also novel results involving complex and dramatic styles with high-frequency details covering large areas across multiple facial features. A carefully designed dataset of unpaired before and after makeup images is released.
Discrete Optimal Graph Clustering
Authors: Yudong Han, Lei Zhu, Zhiyong Cheng, Jingjing Li, Xiaobai Liu
Date: 2019-04-25 00:00:00
Tasks: Clustering, Graph Clustering, graph construction

Graph based clustering is one of the major clustering methods. Most of it
work in three separate steps: similarity graph construction, clustering label
relaxing and label discretization with k-means. Such common practice has three
disadvantages: 1) the predefined similarity graph is often fixed and may not be
optimal for the subsequent clustering. 2) the relaxing process of cluster
labels may cause significant information loss. 3) label discretization may
deviate from the real clustering result since k-means is sensitive to the
initialization of cluster centroids. To tackle these problems, in this paper,
we propose an effective discrete optimal graph clustering (DOGC) framework. A
structured similarity graph that is theoretically optimal for clustering
performance is adaptively learned with a guidance of reasonable rank
constraint. Besides, to avoid the information loss, we explicitly enforce a
discrete transformation on the intermediate continuous label, which derives a
tractable optimization problem with discrete solution. Further, to compensate
the unreliability of the learned labels and enhance the clustering accuracy, we
design an adaptive robust module that learns prediction function for the unseen
data based on the learned discrete cluster labels. Finally, an iterative
optimization strategy guaranteed with convergence is developed to directly
solve the clustering results. Extensive experiments conducted on both real and
synthetic datasets demonstrate the superiority of our proposed methods compared
with several state-of-the-art clustering approaches.
On guiding video object segmentation
Authors: Diego Ortego, Kevin McGuinness, Juan C. SanMiguel, Eric Arazo, José M. Martínez, Noel E. O'Connor
Date: 2019-04-25 00:00:00
Tasks: Foreground Segmentation, Object, Optical Flow Estimation, Segmentation, Semantic Segmentation, Video Object Segmentation, Video Semantic Segmentation

This paper presents a novel approach for segmenting moving objects in
unconstrained environments using guided convolutional neural networks. This
guiding process relies on foreground masks from independent algorithms (i.e.
state-of-the-art algorithms) to implement an attention mechanism that
incorporates the spatial location of foreground and background to compute their
separated representations. Our approach initially extracts two kinds of
features for each frame using colour and optical flow information. Such
features are combined following a multiplicative scheme to benefit from their
complementarity. These unified colour and motion features are later processed
to obtain the separated foreground and background representations. Then, both
independent representations are concatenated and decoded to perform foreground
segmentation. Experiments conducted on the challenging DAVIS 2016 dataset
demonstrate that our guided representations not only outperform non-guided, but
also recent and top-performing video object segmentation algorithms.
Pointing Novel Objects in Image Captioning
Authors: Yehao Li, Ting Yao, Yingwei Pan, Hongyang Chao, Tao Mei
Date: 2019-04-25 00:00:00
Proceeding: CVPR 2019 6
Tasks: Decoder, Image Captioning, Object, Object Recognition, Sentence

Image captioning has received significant attention with remarkable
improvements in recent advances. Nevertheless, images in the wild encapsulate
rich knowledge and cannot be sufficiently described with models built on
image-caption pairs containing only in-domain objects. In this paper, we
propose to address the problem by augmenting standard deep captioning
architectures with object learners. Specifically, we present Long Short-Term
Memory with Pointing (LSTM-P) --- a new architecture that facilitates
vocabulary expansion and produces novel objects via pointing mechanism.
Technically, object learners are initially pre-trained on available object
recognition data. Pointing in LSTM-P then balances the probability between
generating a word through LSTM and copying a word from the recognized objects
at each time step in decoder stage. Furthermore, our captioning encourages
global coverage of objects in the sentence. Extensive experiments are conducted
on both held-out COCO image captioning and ImageNet datasets for describing
novel objects, and superior results are reported when comparing to
state-of-the-art approaches. More remarkably, we obtain an average of 60.9% in
F1 score on held-out COCO~dataset.
Exploring Object Relation in Mean Teacher for Cross-Domain Detection
Authors: Qi Cai, Yingwei Pan, Chong-Wah Ngo, Xinmei Tian, Ling-Yu Duan, Ting Yao
Date: 2019-04-25 00:00:00
Proceeding: CVPR 2019 6
Tasks: Domain Adaptation, Relation, Unsupervised Domain Adaptation

Rendering synthetic data (e.g., 3D CAD-rendered images) to generate annotations for learning deep models in vision tasks has attracted increasing attention in recent years. However, simply applying the models learnt on synthetic images may lead to high generalization error on real images due to domain shift. To address this issue, recent progress in cross-domain recognition has featured the Mean Teacher, which directly simulates unsupervised domain adaptation as semi-supervised learning. The domain gap is thus naturally bridged with consistency regularization in a teacher-student scheme. In this work, we advance this Mean Teacher paradigm to be applicable for cross-domain detection. Specifically, we present Mean Teacher with Object Relations (MTOR) that novelly remolds Mean Teacher under the backbone of Faster R-CNN by integrating the object relations into the measure of consistency cost between teacher and student modules. Technically, MTOR firstly learns relational graphs that capture similarities between pairs of regions for teacher and student respectively. The whole architecture is then optimized with three consistency regularizations: 1) region-level consistency to align the region-level predictions between teacher and student, 2) inter-graph consistency for matching the graph structures between teacher and student, and 3) intra-graph consistency to enhance the similarity between regions of same class within the graph of student. Extensive experiments are conducted on the transfers across Cityscapes, Foggy Cityscapes, and SIM10k, and superior results are reported when comparing to state-of-the-art approaches. More remarkably, we obtain a new record of single model: 22.8% of mAP on Syn2Real detection dataset.
NeuroPod: a real-time neuromorphic spiking CPG applied to robotics
Authors: Daniel Gutierrez-Galan, Juan Pedro Dominguez-Morales, Fernando Perez-Pena, Alejandro Linares-Barranco
Date: 2019-04-25 00:00:00

Initially, robots were developed with the aim of making our life easier,
carrying out repetitive or dangerous tasks for humans. Although they were able
to perform these tasks, the latest generation of robots are being designed to
take a step further, by performing more complex tasks that have been carried
out by smart animals or humans up to date. To this end, inspiration needs to be
taken from biological examples. For instance, insects are able to optimally
solve complex environment navigation problems, and many researchers have
started to mimic how these insects behave. Recent interest in neuromorphic
engineering has motivated us to present a real-time, neuromorphic, spike-based
Central Pattern Generator of application in neurorobotics, using an
arthropod-like robot. A Spiking Neural Network was designed and implemented on
SpiNNaker. The network models a complex, online-change capable Central Pattern
Generator which generates three gaits for a hexapod robot locomotion.
Reconfigurable hardware was used to manage both the motors of the robot and the
real-time communication interface with the Spiking Neural Networks. Real-time
measurements confirm the simulation results, and locomotion tests show that
NeuroPod can perform the gaits without any balance loss or added delay.
Unsupervised Label Noise Modeling and Loss Correction
Authors: Eric Arazo, Diego Ortego, Paul Albert, Noel E. O'Connor, Kevin McGuinness
Date: 2019-04-25 00:00:00
Tasks: Image Classification

Despite being robust to small amounts of label noise, convolutional neural networks trained with stochastic gradient methods have been shown to easily fit random labels. When there are a mixture of correct and mislabelled targets, networks tend to fit the former before the latter. This suggests using a suitable two-component mixture model as an unsupervised generative model of sample loss values during training to allow online estimation of the probability that a sample is mislabelled. Specifically, we propose a beta mixture to estimate this probability and correct the loss by relying on the network prediction (the so-called bootstrapping loss). We further adapt mixup augmentation to drive our approach a step further. Experiments on CIFAR-10/100 and TinyImageNet demonstrate a robustness to label noise that substantially outperforms recent state-of-the-art. Source code is available at https://git.io/fjsvE
Adaptive Collaborative Similarity Learning for Unsupervised Multi-view Feature Selection
Authors: Xiao Dong, Lei Zhu, Xuemeng Song, Jingjing Li, Zhiyong Cheng
Date: 2019-04-25 00:00:00
Tasks: feature selection

In this paper, we investigate the research problem of unsupervised multi-view
feature selection. Conventional solutions first simply combine multiple
pre-constructed view-specific similarity structures into a collaborative
similarity structure, and then perform the subsequent feature selection. These
two processes are separate and independent. The collaborative similarity
structure remains fixed during feature selection. Further, the simple
undirected view combination may adversely reduce the reliability of the
ultimate similarity structure for feature selection, as the view-specific
similarity structures generally involve noises and outlying entries. To
alleviate these problems, we propose an adaptive collaborative similarity
learning (ACSL) for multi-view feature selection. We propose to dynamically
learn the collaborative similarity structure, and further integrate it with the
ultimate feature selection into a unified framework. Moreover, a reasonable
rank constraint is devised to adaptively learn an ideal collaborative
similarity structure with proper similarity combination weights and desirable
neighbor assignment, both of which could positively facilitate the feature
selection. An effective solution guaranteed with the proved convergence is
derived to iteratively tackle the formulated optimization problem. Experiments
demonstrate the superiority of the proposed approach.
Transferrable Prototypical Networks for Unsupervised Domain Adaptation
Authors: Yingwei Pan, Ting Yao, Yehao Li, Yu Wang, Chong-Wah Ngo, Tao Mei
Date: 2019-04-25 00:00:00
Proceeding: CVPR 2019 6
Tasks: Domain Adaptation, Pseudo Label, Unsupervised Domain Adaptation

In this paper, we introduce a new idea for unsupervised domain adaptation via
a remold of Prototypical Networks, which learn an embedding space and perform
classification via a remold of the distances to the prototype of each class.
Specifically, we present Transferrable Prototypical Networks (TPN) for
adaptation such that the prototypes for each class in source and target domains
are close in the embedding space and the score distributions predicted by
prototypes separately on source and target data are similar. Technically, TPN
initially matches each target example to the nearest prototype in the source
domain and assigns an example a "pseudo" label. The prototype of each class
could then be computed on source-only, target-only and source-target data,
respectively. The optimization of TPN is end-to-end trained by jointly
minimizing the distance across the prototypes on three types of data and
KL-divergence of score distributions output by each pair of the prototypes.
Extensive experiments are conducted on the transfers across MNIST, USPS and
SVHN datasets, and superior results are reported when comparing to
state-of-the-art approaches. More remarkably, we obtain an accuracy of 80.4% of
single model on VisDA 2017 dataset.
Towards Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-based Convolutional Encoders
Authors: Matteo Manica, Ali Oskooei, Jannis Born, Vigneshwari Subramanian, Julio Sáez-Rodríguez, María Rodríguez Martínez
Date: 2019-04-25 00:00:00
Tasks: Drug Design, Prediction, Sensitivity

In line with recent advances in neural drug design and sensitivity prediction, we propose a novel architecture for interpretable prediction of anticancer compound sensitivity using a multimodal attention-based convolutional encoder. Our model is based on the three key pillars of drug sensitivity: compounds' structure in the form of a SMILES sequence, gene expression profiles of tumors and prior knowledge on intracellular interactions from protein-protein interaction networks. We demonstrate that our multiscale convolutional attention-based (MCA) encoder significantly outperforms a baseline model trained on Morgan fingerprints, a selection of encoders based on SMILES as well as previously reported state of the art for multimodal drug sensitivity prediction (R2 = 0.86 and RMSE = 0.89). Moreover, the explainability of our approach is demonstrated by a thorough analysis of the attention weights. We show that the attended genes significantly enrich apoptotic processes and that the drug attention is strongly correlated with a standard chemical structure similarity index. Finally, we report a case study of two receptor tyrosine kinase (RTK) inhibitors acting on a leukemia cell line, showcasing the ability of the model to focus on informative genes and submolecular regions of the two compounds. The demonstrated generalizability and the interpretability of our model testify its potential for in-silico prediction of anticancer compound efficacy on unseen cancer cells, positioning it as a valid solution for the development of personalized therapies as well as for the evaluation of candidate compounds in de novo drug design.
Exploring Auxiliary Context: Discrete Semantic Transfer Hashing for Scalable Image Retrieval
Authors: Lei Zhu, Zi Huang, Zhihui Li, Liang Xie, Heng Tao Shen
Date: 2019-04-25 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval

Unsupervised hashing can desirably support scalable content-based image
retrieval (SCBIR) for its appealing advantages of semantic label independence,
memory and search efficiency. However, the learned hash codes are embedded with
limited discriminative semantics due to the intrinsic limitation of image
representation. To address the problem, in this paper, we propose a novel
hashing approach, dubbed as \emph{Discrete Semantic Transfer Hashing} (DSTH).
The key idea is to \emph{directly} augment the semantics of discrete image hash
codes by exploring auxiliary contextual modalities. To this end, a unified
hashing framework is formulated to simultaneously preserve visual similarities
of images and perform semantic transfer from contextual modalities. Further, to
guarantee direct semantic transfer and avoid information loss, we explicitly
impose the discrete constraint, bit--uncorrelation constraint and bit-balance
constraint on hash codes. A novel and effective discrete optimization method
based on augmented Lagrangian multiplier is developed to iteratively solve the
optimization problem. The whole learning process has linear computation
complexity and desirable scalability. Experiments on three benchmark datasets
demonstrate the superiority of DSTH compared with several state-of-the-art
approaches.
Reviewing Data Access Patterns and Computational Redundancy for Machine Learning Algorithms
Authors: Imen Chakroun, Tom Vander Aa, Tom Ashby
Date: 2019-04-25 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning (ML) is probably the first and foremost used technique to deal with the size and complexity of the new generation of data. In this paper, we analyze one of the means to increase the performances of ML algorithms which is exploiting data locality. Data locality and access patterns are often at the heart of performance issues in computing systems due to the use of certain hardware techniques to improve performance. Altering the access patterns to increase locality can dramatically increase performance of a given algorithm. Besides, repeated data access can be seen as redundancy in data movement. Similarly, there can also be redundancy in the repetition of calculations. This work also identifies some of the opportunities for avoiding these redundancies by directly reusing computation results. We document the possibilities of such reuse in some selected machine learning algorithms and give initial indicative results from our first experiments on data access improvement and algorithm redesign.