pwc-archive/papers-with-abstracts huggingface dataset:
Certainty Driven Consistency Loss on Multi-Teacher Networks for Semi-Supervised Learning
Authors: Lu Liu, Robby T. Tan
Date: 2019-01-17 00:00:00

One of the successful approaches in semi-supervised learning is based on the consistency regularization. Typically, a student model is trained to be consistent with teacher prediction for the inputs under different perturbations. To be successful, the prediction targets given by teacher should have good quality, otherwise the student can be misled by teacher. Unfortunately, existing methods do not assess the quality of the teacher targets. In this paper, we propose a novel Certainty-driven Consistency Loss (CCL) that exploits the predictive uncertainty in the consistency loss to let the student dynamically learn from reliable targets. Specifically, we propose two approaches, i.e. Filtering CCL and Temperature CCL to either filter out uncertain predictions or pay less attention on them in the consistency regularization. We further introduce a novel decoupled framework to encourage model difference. Experimental results on SVHN, CIFAR-10, and CIFAR-100 demonstrate the advantages of our method over a few existing methods.
Background subtraction on depth videos with convolutional neural networks
Authors: Xueying Wang, Lei Liu, Guangli Li, Xiao Dong, Peng Zhao, Xiaobing Feng
Date: 2019-01-17 00:00:00
Tasks: Anomaly Detection, Object Tracking

Background subtraction is a significant component of computer vision systems.
It is widely used in video surveillance, object tracking, anomaly detection,
etc. A new data source for background subtraction appeared as the emergence of
low-cost depth sensors like Microsof t Kinect, Asus Xtion PRO, etc. In this
paper, we propose a background subtraction approach on depth videos, which is
based on convolutional neural networks (CNNs), called BGSNet-D (BackGround
Subtraction neural Networks for Depth videos). The method can be used in color
unavailable scenarios like poor lighting situations, and can also be applied to
combine with existing RGB background subtraction methods. A preprocessing
strategy is designed to reduce the influences incurred by noise from depth
sensors. The experimental results on the SBM-RGBD dataset show that the
proposed method outperforms existing methods on depth data.
Image Enhancement Network Trained by Using HDR images
Authors: Yuma Kinoshita, Hitoshi Kiya
Date: 2019-01-17 00:00:00
Tasks: Image Enhancement

In this paper, a novel image enhancement network is proposed, where HDR
images are used for generating training data for our network. Most of
conventional image enhancement methods, including Retinex based methods, do not
take into account restoring lost pixel values caused by clipping and
quantizing. In addition, recently proposed CNN based methods still have a
limited scope of application or a limited performance, due to network
architectures. In contrast, the proposed method have a higher performance and a
simpler network architecture than existing CNN based methods. Moreover, the
proposed method enables us to restore lost pixel values. Experimental results
show that the proposed method can provides higher-quality images than
conventional image enhancement methods including a CNN based method, in terms
of TMQI and NIQE.
Multiple Sclerosis Lesion Synthesis in MRI using an encoder-decoder U-NET
Authors: Mostafa Salem, Sergi Valverde, Mariano Cabezas, Deborah Pareto, Arnau Oliver, Joaquim Salvi, Àlex Rovira, Xavier Lladó
Date: 2019-01-17 00:00:00
Tasks: Data Augmentation, Decoder, Lesion Detection

In this paper, we propose generating synthetic multiple sclerosis (MS)
lesions on MRI images with the final aim to improve the performance of
supervised machine learning algorithms, therefore avoiding the problem of the
lack of available ground truth. We propose a two-input two-output fully
convolutional neural network model for MS lesion synthesis in MRI images. The
lesion information is encoded as discrete binary intensity level masks passed
to the model and stacked with the input images. The model is trained end-to-end
without the need for manually annotating the lesions in the training set. We
then perform the generation of synthetic lesions on healthy images via
registration of patient images, which are subsequently used for data
augmentation to increase the performance for supervised MS lesion detection
algorithms. Our pipeline is evaluated on MS patient data from an in-house
clinical dataset and the public ISBI2015 challenge dataset. The evaluation is
based on measuring the similarities between the real and the synthetic images
as well as in terms of lesion detection performance by segmenting both the
original and synthetic images individually using a state-of-the-art
segmentation framework. We also demonstrate the usage of synthetic MS lesions
generated on healthy images as data augmentation. We analyze a scenario of
limited training data (one-image training) to demonstrate the effect of the
data augmentation on both datasets. Our results significantly show the
effectiveness of the usage of synthetic MS lesion images. For the ISBI2015
challenge, our one-image model trained using only a single image plus the
synthetic data augmentation strategy showed a performance similar to that of
other CNN methods that were fully trained using the entire training set,
yielding a comparable human expert rater performance
A Temporal Attentive Approach for Video-Based Pedestrian Attribute Recognition
Authors: Zhiyuan Chen, Annan Li, Yunhong Wang
Date: 2019-01-17 00:00:00
Tasks: Attribute, Pedestrian Attribute Recognition

In this paper, we first tackle the problem of pedestrian attribute recognition by video-based approach. The challenge mainly lies in spatial and temporal modeling and how to integrating them for effective and dynamic pedestrian representation. To solve this problem, a novel multi-task model based on the conventional neural network and temporal attention strategy is proposed. Since publicly available dataset is rare, two new large-scale video datasets with expanded attribute definition are presented, on which the effectiveness of both video-based pedestrian attribute recognition methods and the proposed new network architecture is well demonstrated. The two datasets are published on http://irip.buaa.edu.cn/mars_duke_attributes/index.html.
SAFE: Scale Aware Feature Encoder for Scene Text Recognition
Authors: Wei Liu, Chaofeng Chen, Kwan-Yee K. Wong
Date: 2019-01-17 00:00:00
Tasks: Scene Text Recognition

In this paper, we address the problem of having characters with different
scales in scene text recognition. We propose a novel scale aware feature
encoder (SAFE) that is designed specifically for encoding characters with
different scales. SAFE is composed of a multi-scale convolutional encoder and a
scale attention network. The multi-scale convolutional encoder targets at
extracting character features under multiple scales, and the scale attention
network is responsible for selecting features from the most relevant scale(s).
SAFE has two main advantages over the traditional single-CNN encoder used in
current state-of-the-art text recognizers. First, it explicitly tackles the
scale problem by extracting scale-invariant features from the characters. This
allows the recognizer to put more effort in handling other challenges in scene
text recognition, like those caused by view distortion and poor image quality.
Second, it can transfer the learning of feature encoding across different
character scales. This is particularly important when the training set has a
very unbalanced distribution of character scales, as training with such a
dataset will make the encoder biased towards extracting features from the
predominant scale. To evaluate the effectiveness of SAFE, we design a simple
text recognizer named scale-spatial attention network (S-SAN) that employs SAFE
as its feature encoder, and carry out experiments on six public benchmarks.
Experimental results demonstrate that S-SAN can achieve state-of-the-art (or,
in some cases, extremely competitive) performance without any post-processing.
Visual enhancement of Cone-beam CT by use of CycleGAN
Authors: S. Kida, S. Kaji, K. Nawa, T. Imae, T. Nakamoto, S. Ozaki, T. Ohta, Y. Nozawa, K. Nakagawa
Date: 2019-01-17 00:00:00

Cone-beam computed tomography (CBCT) offers advantages over conventional fan-beam CT in that it requires a shorter time and less exposure to obtain images. CBCT has found a wide variety of applications in patient positioning for image-guided radiation therapy, extracting radiomic information for designing patient-specific treatment, and computing fractional dose distributions for adaptive radiation therapy. However, CBCT images suffer from low soft-tissue contrast, noise, and artifacts compared to conventional fan-beam CT images. Therefore, it is essential to improve the image quality of CBCT. In this paper, we propose a synthetic approach to translate CBCT images with deep neural networks. Our method requires only unpaired and unaligned CBCT images and planning fan-beam CT (PlanCT) images for training. Once trained, 3D reconstructed CBCT images can be directly translated to high-quality PlanCT-like images. We demonstrate the effectiveness of our method with images obtained from 24 prostate patients, and we provide a statistical and visual comparison. The image quality of the translated images shows substantial improvement in voxel values, spatial uniformity, and artifact suppression compared to those of the original CBCT. The anatomical structures of the original CBCT images were also well preserved in the translated images. Our method enables more accurate adaptive radiation therapy, and opens up new applications for CBCT that hinge on high-quality images.
Ensemble Feature for Person Re-Identification
Authors: Jiabao Wang, Yang Li, Zhuang Miao
Date: 2019-01-17 00:00:00
Tasks: Person Re-Identification

In person re-identification (re-ID), the key task is feature representation,
which is used to compute distance or similarity in prediction. Person re-ID
achieves great improvement when deep learning methods are introduced to tackle
this problem. The features extracted by convolutional neural networks (CNN) are
more effective and discriminative than the hand-crafted features. However, deep
feature extracted by a single CNN network is not robust enough in testing
stage. To improve the ability of feature representation, we propose a new
ensemble network (EnsembleNet) by dividing a single network into multiple
end-to-end branches. The ensemble feature is obtained by concatenating each of
the branch features to represent a person. EnsembleNet is designed based on
ResNet-50 and its backbone shares most of the parameters for saving computation
and memory cost. Experimental results show that our EnsembleNet achieves the
state-of-the-art performance on the public Market1501, DukeMTMC-reID and CUHK03
person re-ID benchmarks.
Monocular Outdoor Semantic Mapping with a Multi-task Network
Authors: Yucai Bai, Lei Fan, Ziyu Pan, Long Chen
Date: 2019-01-17 00:00:00
Tasks: 3D Reconstruction, Autonomous Driving, Depth Estimation, Depth Prediction, Prediction

In many robotic applications, especially for the autonomous driving, understanding the semantic information and the geometric structure of surroundings are both essential. Semantic 3D maps, as a carrier of the environmental knowledge, are then intensively studied for their abilities and applications. However, it is still challenging to produce a dense outdoor semantic map from a monocular image stream. Motivated by this target, in this paper, we propose a method for large-scale 3D reconstruction from consecutive monocular images. First, with the correlation of underlying information between depth and semantic prediction, a novel multi-task Convolutional Neural Network (CNN) is designed for joint prediction. Given a single image, the network learns low-level information with a shared encoder and separately predicts with decoders containing additional Atrous Spatial Pyramid Pooling (ASPP) layers and the residual connection which merits disparities and semantic mutually. To overcome the inconsistency of monocular depth prediction for reconstruction, post-processing steps with the superpixelization and the effective 3D representation approach are obtained to give the final semantic map. Experiments are compared with other methods on both semantic labeling and depth prediction. We also qualitatively demonstrate the map reconstructed from large-scale, difficult monocular image sequences to prove the effectiveness and superiority.
No reference image quality assessment metric based on regional mutual information among images
Authors: Vinay Kumar, Vivek Singh Bawa, Rahul Upadhyay
Date: 2019-01-17 00:00:00
Tasks: General Classification, Image Quality Assessment, No-Reference Image Quality Assessment

With the inclusion of camera in daily life, an automatic no reference image quality evaluation index is required for automatic classification of images. The present manuscripts proposes a new No Reference Regional Mutual Information based technique for evaluating the quality of an image. We use regional mutual information on subsets of the complete image. Proposed technique is tested on four benchmark natural image databases, and one benchmark synthetic database. A comparative analysis with classical and state-of-art methods indicate superiority of the present technique for high quality images and comparable for other images of the respective databases.
UltraCompression: Framework for High Density Compression of Ultrasound Volumes using Physics Modeling Deep Neural Networks
Authors: Debarghya China, Francis Tom, Sumanth Nandamuri, Aupendu Kar, Mukundhan Srinivasan, Pabitra Mitra, Debdoot Sheet
Date: 2019-01-17 00:00:00
Tasks: Image Compression, Image Segmentation, Semantic Segmentation

Ultrasound image compression by preserving speckle-based key information is a
challenging task. In this paper, we introduce an ultrasound image compression
framework with the ability to retain realism of speckle appearance despite
achieving very high-density compression factors. The compressor employs a
tissue segmentation method, transmitting segments along with transducer
frequency, number of samples and image size as essential information required
for decompression. The decompressor is based on a convolutional network trained
to generate patho-realistic ultrasound images which convey essential
information pertinent to tissue pathology visible in the images. We demonstrate
generalizability of the building blocks using two variants to build the
compressor. We have evaluated the quality of decompressed images using
distortion losses as well as perception loss and compared it with other off the
shelf solutions. The proposed method achieves a compression ratio of $725:1$
while preserving the statistical distribution of speckles. This enables image
segmentation on decompressed images to achieve dice score of $0.89 \pm 0.11$,
which evidently is not so accurately achievable when images are compressed with
current standards like JPEG, JPEG 2000, WebP and BPG. We envision this frame
work to serve as a roadmap for speckle image compression standards.
EAT-NAS: Elastic Architecture Transfer for Accelerating Large-scale Neural Architecture Search
Authors: Jiemin Fang, Yukang Chen, Xinbang Zhang, Qian Zhang, Chang Huang, Gaofeng Meng, Wenyu Liu, Xinggang Wang
Date: 2019-01-17 00:00:00
Tasks: Neural Architecture Search

Neural architecture search (NAS) methods have been proposed to release human
experts from tedious architecture engineering. However, most current methods
are constrained in small-scale search due to the issue of computational
resources. Meanwhile, directly applying architectures searched on small
datasets to large datasets often bears no performance guarantee. This
limitation impedes the wide use of NAS on large-scale tasks. To overcome this
obstacle, we propose an elastic architecture transfer mechanism for
accelerating large-scale neural architecture search (EAT-NAS). In our
implementations, architectures are first searched on a small dataset, e.g.,
CIFAR-10. The best one is chosen as the basic architecture. The search process
on the large dataset, e.g., ImageNet, is initialized with the basic
architecture as the seed. The large-scale search process is accelerated with
the help of the basic architecture. What we propose is not only a NAS method
but a mechanism for architecture-level transfer.
  In our experiments, we obtain two final models EATNet-A and EATNet-B that
achieve competitive accuracies, 74.7% and 74.2% on ImageNet, respectively,
which also surpass the models searched from scratch on ImageNet under the same
settings. For the computational cost, EAT-NAS takes only less than 5 days on 8
TITAN X GPUs, which is significantly less than the computational consumption of
the state-of-the-art large-scale NAS methods.
LiSHT: Non-Parametric Linearly Scaled Hyperbolic Tangent Activation Function for Neural Networks
Authors: Swalpa Kumar Roy, Suvojit Manna, Shiv Ram Dubey, Bidyut Baran Chaudhuri
Date: 2019-01-01 00:00:00
Tasks: Classification, image-classification, Image Classification

The activation function in neural network introduces the non-linearity required to deal with the complex tasks. Several activation/non-linearity functions are developed for deep learning models. However, most of the existing activation functions suffer due to the dying gradient problem and non-utilization of the large negative input values. In this paper, we propose a Linearly Scaled Hyperbolic Tangent (LiSHT) for Neural Networks (NNs) by scaling the Tanh linearly. The proposed LiSHT is non-parametric and tackles the dying gradient problem. We perform the experiments on benchmark datasets of different type, such as vector data, image data and natural language data. We observe the superior performance using Multi-layer Perceptron (MLP), Residual Network (ResNet) and Long-short term memory (LSTM) for data classification, image classification and tweets classification tasks, respectively. The accuracy on CIFAR100 dataset using ResNet model with LiSHT is improved by 9.48, 3.40, 3.16, 4.26, and 1.17\% as compared to Tanh, ReLU, PReLU, LReLU, and Swish, respectively. We also show the qualitative results using loss landscape, weight distribution and activations maps in support of the proposed activation function.
A Performance Comparison of Loss Functions for Deep Face Recognition
Authors: Yash Srivastava, Vaishnav Murali, Shiv Ram Dubey
Date: 2019-01-01 00:00:00
Tasks: Face Recognition

Face recognition is one of the most widely publicized feature in the devices today and hence represents an important problem that should be studied with the utmost priority. As per the recent trends, the Convolutional Neural Network (CNN) based approaches are highly successful in many tasks of Computer Vision including face recognition. The loss function is used on the top of CNN to judge the goodness of any network. In this paper, we present a performance comparison of different loss functions such as Cross-Entropy, Angular Softmax, Additive-Margin Softmax, ArcFace and Marginal Loss for face recognition. The experiments are conducted with two CNN architectures namely, ResNet and MobileNet. Two widely used face datasets namely, CASIA-Webface and MS-Celeb-1M are used for the training and benchmark Labeled Faces in the Wild (LFW) face dataset is used for the testing.
Foreground-aware Image Inpainting
Authors: Wei Xiong, Jiahui Yu, Zhe Lin, Jimei Yang, Xin Lu, Connelly Barnes, Jiebo Luo
Date: 2019-01-17 00:00:00
Proceeding: CVPR 2019 6
Tasks: Disentanglement, Image Inpainting

Existing image inpainting methods typically fill holes by borrowing
information from surrounding pixels. They often produce unsatisfactory results
when the holes overlap with or touch foreground objects due to lack of
information about the actual extent of foreground and background regions within
the holes. These scenarios, however, are very important in practice, especially
for applications such as the removal of distracting objects. To address the
problem, we propose a foreground-aware image inpainting system that explicitly
disentangles structure inference and content completion. Specifically, our
model learns to predict the foreground contour first, and then inpaints the
missing region using the predicted contour as guidance. We show that by such
disentanglement, the contour completion model predicts reasonable contours of
objects, and further substantially improves the performance of image
inpainting. Experiments show that our method significantly outperforms existing
methods and achieves superior inpainting results on challenging cases with
complex compositions.
Guided Curriculum Model Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation
Authors: Christos Sakaridis, Dengxin Dai, Luc van Gool
Date: 2019-01-17 00:00:00
Proceeding: ICCV 2019 10
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, Style Transfer

Most progress in semantic segmentation reports on daytime images taken under favorable illumination conditions. We instead address the problem of semantic segmentation of nighttime images and improve the state-of-the-art, by adapting daytime models to nighttime without using nighttime annotations. Moreover, we design a new evaluation framework to address the substantial uncertainty of semantics in nighttime images. Our central contributions are: 1) a curriculum framework to gradually adapt semantic segmentation models from day to night via labeled synthetic images and unlabeled real images, both for progressively darker times of day, which exploits cross-time-of-day correspondences for the real images to guide the inference of their labels; 2) a novel uncertainty-aware annotation and evaluation framework and metric for semantic segmentation, designed for adverse conditions and including image regions beyond human recognition capability in the evaluation in a principled fashion; 3) the Dark Zurich dataset, which comprises 2416 unlabeled nighttime and 2920 unlabeled twilight images with correspondences to their daytime counterparts plus a set of 151 nighttime images with fine pixel-level annotations created with our protocol, which serves as a first benchmark to perform our novel evaluation. Experiments show that our guided curriculum adaptation significantly outperforms state-of-the-art methods on real nighttime sets both for standard metrics and our uncertainty-aware metric. Furthermore, our uncertainty-aware evaluation reveals that selective invalidation of predictions can lead to better results on data with ambiguous content such as our nighttime benchmark and profit safety-oriented applications which involve invalid inputs.