pwc-archive/papers-with-abstracts huggingface dataset:
An Efficient Approach for Cell Segmentation in Phase Contrast Microscopy Images
Authors: Lin Zhang
Date: 2019-03-31 00:00:00
Tasks: Cell Segmentation

In this paper, we propose a new model to segment cells in phase contrast
microscopy images. Cell images collected from the similar scenario share a
similar background. Inspired by this, we separate cells from the background in
images by formulating the problem as a low-rank and structured sparse matrix
decomposition problem. Then, we propose the inverse diffraction pattern
filtering method to further segment individual cells in the images. This is a
deconvolution process that has a much lower computational complexity when
compared to the other restoration methods. Experiments demonstrate the
effectiveness of the proposed model when it is compared with recent works.
Fast and Full-Resolution Light Field Deblurring using a Deep Neural Network
Authors: Jonathan Samuel Lumentut, Tae Hyun Kim, Ravi Ramamoorthi, In Kyu Park
Date: 2019-03-31 00:00:00
Tasks: 16k, Deblurring

Restoring a sharp light field image from its blurry input has become
essential due to the increasing popularity of parallax-based image processing.
State-of-the-art blind light field deblurring methods suffer from several
issues such as slow processing, reduced spatial size, and a limited motion blur
model. In this work, we address these challenging problems by generating a
complex blurry light field dataset and proposing a learning-based deblurring
approach. In particular, we model the full 6-degree of freedom (6-DOF) light
field camera motion, which is used to create the blurry dataset using a
combination of real light fields captured with a Lytro Illum camera, and
synthetic light field renderings of 3D scenes. Furthermore, we propose a light
field deblurring network that is built with the capability of large receptive
fields. We also introduce a simple strategy of angular sampling to train on the
large-scale blurry light field effectively. We evaluate our method through both
quantitative and qualitative measurements and demonstrate superior performance
compared to the state-of-the-art method with a massive speedup in execution
time. Our method is about 16K times faster than Srinivasan et. al. [22] and can
deblur a full-resolution light field in less than 2 seconds.
Pedestrian re-identification based on Tree branch network with local and global learning
Authors: Hui Li, Meng Yang, Zhihui Lai, Wei-Shi Zheng, Zitong Yu
Date: 2019-03-31 00:00:00
Tasks: Person Re-Identification

Deep part-based methods in recent literature have revealed the great
potential of learning local part-level representation for pedestrian image in
the task of person re-identification. However, global features that capture
discriminative holistic information of human body are usually ignored or not
well exploited. This motivates us to investigate joint learning global and
local features from pedestrian images. Specifically, in this work, we propose a
novel framework termed tree branch network (TBN) for person re-identification.
Given a pedestrain image, the feature maps generated by the backbone CNN, are
partitioned recursively into several pieces, each of which is followed by a
bottleneck structure that learns finer-grained features for each level in the
hierarchical tree-like framework. In this way, representations are learned in a
coarse-to-fine manner and finally assembled to produce more discriminative
image descriptions. Experimental results demonstrate the effectiveness of the
global and local feature learning method in the proposed TBN framework. We also
show significant improvement in performance over state-of-the-art methods on
three public benchmarks: Market-1501, CUHK-03 and DukeMTMC.
PyramidBox++: High Performance Detector for Finding Tiny Face
Authors: Zhihang Li, Xu Tang, Junyu Han, Jingtuo Liu, Ran He
Date: 2019-03-31 00:00:00
Tasks: Data Augmentation, Face Detection, Vocal Bursts Intensity Prediction

With the rapid development of deep convolutional neural network, face
detection has made great progress in recent years. WIDER FACE dataset, as a
main benchmark, contributes greatly to this area. A large amount of methods
have been put forward where PyramidBox designs an effective data augmentation
strategy (Data-anchor-sampling) and context-based module for face detector. In
this report, we improve each part to further boost the performance, including
Balanced-data-anchor-sampling, Dual-PyramidAnchors and Dense Context Module.
Specifically, Balanced-data-anchor-sampling obtains more uniform sampling of
faces with different sizes. Dual-PyramidAnchors facilitate feature learning by
introducing progressive anchor loss. Dense Context Module with dense connection
not only enlarges receptive filed, but also passes information efficiently.
Integrating these techniques, PyramidBox++ is constructed and achieves
state-of-the-art performance in hard set.
Multi-vision Attention Networks for On-line Red Jujube Grading
Authors: Xiaoye Sun, Liyan Ma, Gongyan Li
Date: 2019-03-31 00:00:00
Tasks: Classification, General Classification

To solve the red jujube classification problem, this paper designs a
convolutional neural network model with low computational cost and high
classification accuracy. The architecture of the model is inspired by the
multi-visual mechanism of the organism and DenseNet. To further improve our
model, we add the attention mechanism of SE-Net. We also construct a dataset
which contains 23,735 red jujube images captured by a jujube grading system.
According to the appearance of the jujube and the characteristics of the
grading system, the dataset is divided into four classes: invalid, rotten,
wizened and normal. The numerical experiments show that the classification
accuracy of our model reaches to 91.89%, which is comparable to DenseNet-121,
InceptionV3, InceptionV4, and Inception-ResNet v2. However, our model has
real-time performance.
Road Scene Understanding by Occupancy Grid Learning from Sparse Radar Clusters using Semantic Segmentation
Authors: Liat Sless, Gilad Cohen, Bat El Shlomo, Shaul Oron
Date: 2019-03-31 00:00:00
Tasks: Autonomous Driving, road scene understanding, Scene Understanding, Semantic Segmentation

Occupancy grid mapping is an important component in road scene understanding for autonomous driving. It encapsulates information of the drivable area, road obstacles and enables safe autonomous driving. Radars are an emerging sensor in autonomous vehicle vision, becoming more widely used due to their long range sensing, low cost, and robustness to severe weather conditions. Despite recent advances in deep learning technology, occupancy grid mapping from radar data is still mostly done using classical filtering approaches.In this work, we propose learning the inverse sensor model used for occupancy grid mapping from clustered radar data. This is done in a data driven approach that leverages computer vision techniques. This task is very challenging due to data sparsity and noise characteristics of the radar sensor. The problem is formulated as a semantic segmentation task and we show how it can be learned using lidar data for generating ground truth. We show both qualitatively and quantitatively that our learned occupancy net outperforms classic methods by a large margin using the recently released NuScenes real-world driving data.
Single Path One-Shot Neural Architecture Search with Uniform Sampling
Authors: Zichao Guo, Xiangyu Zhang, Haoyuan Mu, Wen Heng, Zechun Liu, Yichen Wei, Jian Sun
Date: 2019-03-31 00:00:00
Proceeding: ECCV 2020 8
Tasks: Neural Architecture Search, Quantization

We revisit the one-shot Neural Architecture Search (NAS) paradigm and analyze its advantages over existing NAS approaches. Existing one-shot method, however, is hard to train and not yet effective on large scale datasets like ImageNet. This work propose a Single Path One-Shot model to address the challenge in the training. Our central idea is to construct a simplified supernet, where all architectures are single paths so that weight co-adaption problem is alleviated. Training is performed by uniform path sampling. All architectures (and their weights) are trained fully and equally. Comprehensive experiments verify that our approach is flexible and effective. It is easy to train and fast to search. It effortlessly supports complex search spaces (e.g., building blocks, channel, mixed-precision quantization) and different search constraints (e.g., FLOPs, latency). It is thus convenient to use for various needs. It achieves start-of-the-art performance on the large dataset ImageNet.
Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Model
Authors: Jianrui Cai, Hui Zeng, Hongwei Yong, Zisheng Cao, Lei Zhang
Date: 2019-04-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Image Registration, Image Super-Resolution, Super-Resolution

Most of the existing learning-based single image superresolution (SISR)
methods are trained and evaluated on simulated datasets, where the
low-resolution (LR) images are generated by applying a simple and uniform
degradation (i.e., bicubic downsampling) to their high-resolution (HR)
counterparts. However, the degradations in real-world LR images are far more
complicated. As a consequence, the SISR models trained on simulated data become
less effective when applied to practical scenarios. In this paper, we build a
real-world super-resolution (RealSR) dataset where paired LR-HR images on the
same scene are captured by adjusting the focal length of a digital camera. An
image registration algorithm is developed to progressively align the image
pairs at different resolutions. Considering that the degradation kernels are
naturally non-uniform in our dataset, we present a Laplacian pyramid based
kernel prediction network (LP-KPN), which efficiently learns per-pixel kernels
to recover the HR image. Our extensive experiments demonstrate that SISR models
trained on our RealSR dataset deliver better visual quality with sharper edges
and finer textures on real-world scenes than those trained on simulated
datasets. Though our RealSR dataset is built by using only two cameras (Canon
5D3 and Nikon D810), the trained model generalizes well to other camera devices
such as Sony a7II and mobile phones.
PIRM2018 Challenge on Spectral Image Super-Resolution: Dataset and Study
Authors: Mehrdad Shoeiby, Antonio Robles-Kelly, Ran Wei, Radu Timofte
Date: 2019-04-01 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

This paper introduces a newly collected and novel dataset (StereoMSI) for
example-based single and colour-guided spectral image super-resolution. The
dataset was first released and promoted during the PIRM2018 spectral image
super-resolution challenge. To the best of our knowledge, the dataset is the
first of its kind, comprising 350 registered colour-spectral image pairs. The
dataset has been used for the two tracks of the challenge and, for each of
these, we have provided a split into training, validation and testing. This
arrangement is a result of the challenge structure and phases, with the first
track focusing on example-based spectral image super-resolution and the second
one aiming at exploiting the registered stereo colour imagery to improve the
resolution of the spectral images. Each of the tracks and splits has been
selected to be consistent across a number of image quality metrics. The dataset
is quite general in nature and can be used for a wide variety of applications
in addition to the development of spectral image super-resolution methods.
Weakly Supervised Object Detection with Segmentation Collaboration
Authors: Xiaoyan Li, Meina Kan, Shiguang Shan, Xilin Chen
Date: 2019-04-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: General Classification, image-classification, Image Classification, Multiple Instance Learning, Object, object-detection, Object Detection, Segmentation, Weakly Supervised Object Detection

Weakly supervised object detection aims at learning precise object detectors,
given image category labels. In recent prevailing works, this problem is
generally formulated as a multiple instance learning module guided by an image
classification loss. The object bounding box is assumed to be the one
contributing most to the classification among all proposals. However, the
region contributing most is also likely to be a crucial part or the supporting
context of an object. To obtain a more accurate detector, in this work we
propose a novel end-to-end weakly supervised detection approach, where a newly
introduced generative adversarial segmentation module interacts with the
conventional detection module in a collaborative loop. The collaboration
mechanism takes full advantages of the complementary interpretations of the
weakly supervised localization task, namely detection and segmentation tasks,
forming a more comprehensive solution. Consequently, our method obtains more
precise object bounding boxes, rather than parts or irrelevant surroundings.
Expectedly, the proposed method achieves an accuracy of 51.0% on the PASCAL VOC
2007 dataset, outperforming the state-of-the-arts and demonstrating its
superiority for weakly supervised object detection.
Defogging Kinect: Simultaneous Estimation of Object Region and Depth in Foggy Scenes
Authors: Yuki Fujimura, Motoharu Sonogashira, Masaaki Iiyama
Date: 2019-04-01 00:00:00
Tasks: 3D Reconstruction, Depth Estimation, Object

Three-dimensional (3D) reconstruction and scene depth estimation from
2-dimensional (2D) images are major tasks in computer vision. However, using
conventional 3D reconstruction techniques gets challenging in participating
media such as murky water, fog, or smoke. We have developed a method that uses
a time-of-flight (ToF) camera to estimate an object region and depth in
participating media simultaneously. The scattering component is saturated, so
it does not depend on the scene depth, and received signals bouncing off
distant points are negligible due to light attenuation in the participating
media, so the observation of such a point contains only a scattering component.
These phenomena enable us to estimate the scattering component in an object
region from a background that only contains the scattering component. The
problem is formulated as robust estimation where the object region is regarded
as outliers, and it enables the simultaneous estimation of an object region and
depth on the basis of an iteratively reweighted least squares (IRLS)
optimization scheme. We demonstrate the effectiveness of the proposed method
using captured images from a Kinect v2 in real foggy scenes and evaluate the
applicability with synthesized data.
Palmprint image registration using convolutional neural networks and Hough transform
Authors: Mohsen Ahmadi, Hossein Soleimani
Date: 2019-04-01 00:00:00
Tasks: Image Registration

Minutia-based palmprint recognition systems has got lots of interest in last
two decades. Due to the large number of minutiae in a palmprint, approximately
1000 minutiae, the matching process is time consuming which makes it
unpractical for real time applications. One way to address this issue is
aligning all palmprint images to a reference image and bringing them to a same
coordinate system. Bringing all palmprint images to a same coordinate system,
results in fewer computations during minutia matching. In this paper, using
convolutional neural network (CNN) and generalized Hough transform (GHT), we
propose a new method to register palmprint images accurately. This method,
finds the corresponding rotation and displacement (in both x and y direction)
between the palmprint and a reference image. Exact palmprint registration can
enhance the speed and the accuracy of matching process. Proposed method is
capable of distinguishing between left and right palmprint automatically which
helps to speed up the matching process. Furthermore, designed structure of CNN
in registration stage, gives us the segmented palmprint image from background
which is a pre-processing step for minutia extraction. The proposed
registration method followed by minutia-cylinder code (MCC) matching algorithm
has been evaluated on the THUPALMLAB database, and the results show the
superiority of our algorithm over most of the state-of-the-art algorithms.
ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data
Authors: Foivos I. Diakogiannis, François Waldner, Peter Caccetta, Chen Wu
Date: 2019-04-01 00:00:00
Tasks: Scene Parsing, Scene Understanding, Segmentation, Semantic Segmentation

Scene understanding of high resolution aerial images is of great importance for the task of automated monitoring in various remote sensing applications. Due to the large within-class and small between-class variance in pixel values of objects of interest, this remains a challenging task. In recent years, deep convolutional neural networks have started being used in remote sensing applications and demonstrate state of the art performance for pixel level classification of objects. \textcolor{black}{Here we propose a reliable framework for performant results for the task of semantic segmentation of monotemporal very high resolution aerial images. Our framework consists of a novel deep learning architecture, ResUNet-a, and a novel loss function based on the Dice loss. ResUNet-a uses a UNet encoder/decoder backbone, in combination with residual connections, atrous convolutions, pyramid scene parsing pooling and multi-tasking inference. ResUNet-a infers sequentially the boundary of the objects, the distance transform of the segmentation mask, the segmentation mask and a colored reconstruction of the input. Each of the tasks is conditioned on the inference of the previous ones, thus establishing a conditioned relationship between the various tasks, as this is described through the architecture's computation graph. We analyse the performance of several flavours of the Generalized Dice loss for semantic segmentation, and we introduce a novel variant loss function for semantic segmentation of objects that has excellent convergence properties and behaves well even under the presence of highly imbalanced classes.} The performance of our modeling framework is evaluated on the ISPRS 2D Potsdam dataset. Results show state-of-the-art performance with an average F1 score of 92.9\% over all classes for our best model.
Learning Combinatorial Embedding Networks for Deep Graph Matching
Authors: Runzhong Wang, Junchi Yan, Xiaokang Yang
Date: 2019-04-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Graph Embedding, Graph Matching

Graph matching refers to finding node correspondence between graphs, such that the corresponding node and edge's affinity can be maximized. In addition with its NP-completeness nature, another important challenge is effective modeling of the node-wise and structure-wise affinity across graphs and the resulting objective, to guide the matching procedure effectively finding the true matching against noises. To this end, this paper devises an end-to-end differentiable deep network pipeline to learn the affinity for graph matching. It involves a supervised permutation loss regarding with node correspondence to capture the combinatorial nature for graph matching. Meanwhile deep graph embedding models are adopted to parameterize both intra-graph and cross-graph affinity functions, instead of the traditional shallow and simple parametric forms e.g. a Gaussian kernel. The embedding can also effectively capture the higher-order structure beyond second-order edges. The permutation loss model is agnostic to the number of nodes, and the embedding model is shared among nodes such that the network allows for varying numbers of nodes in graphs for training and inference. Moreover, our network is class-agnostic with some generalization capability across different categories. All these features are welcomed for real-world applications. Experiments show its superiority against state-of-the-art graph matching learning methods.
Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networks
Authors: Woo-Jeoung Nam, Shir Gur, Jaesik Choi, Lior Wolf, Seong-Whan Lee
Date: 2019-04-01 00:00:00

As Deep Neural Networks (DNNs) have demonstrated superhuman performance in a variety of fields, there is an increasing interest in understanding the complex internal mechanisms of DNNs. In this paper, we propose Relative Attributing Propagation (RAP), which decomposes the output predictions of DNNs with a new perspective of separating the relevant (positive) and irrelevant (negative) attributions according to the relative influence between the layers. The relevance of each neuron is identified with respect to its degree of contribution, separated into positive and negative, while preserving the conservation rule. Considering the relevance assigned to neurons in terms of relative priority, RAP allows each neuron to be assigned with a bi-polar importance score concerning the output: from highly relevant to highly irrelevant. Therefore, our method makes it possible to interpret DNNs with much clearer and attentive visualizations of the separated attributions than the conventional explaining methods. To verify that the attributions propagated by RAP correctly account for each meaning, we utilize the evaluation metrics: (i) Outside-inside relevance ratio, (ii) Segmentation mIOU and (iii) Region perturbation. In all experiments and metrics, we present a sizable gap in comparison to the existing literature. Our source code is available in \url{https://github.com/wjNam/Relative_Attributing_Propagation}.
Video Object Segmentation using Space-Time Memory Networks
Authors: Seoung Wug Oh, Joon-Young Lee, Ning Xu, Seon Joo Kim
Date: 2019-04-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Interactive Video Object Segmentation, Object, One-shot visual object segmentation, Semantic Segmentation, Semi-Supervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation

We propose a novel solution for semi-supervised video object segmentation. By the nature of the problem, available cues (e.g. video frame(s) with object masks) become richer with the intermediate predictions. However, the existing methods are unable to fully exploit this rich source of information. We resolve the issue by leveraging memory networks and learn to read relevant information from all available sources. In our framework, the past frames with object masks form an external memory, and the current frame as the query is segmented using the mask information in the memory. Specifically, the query and the memory are densely matched in the feature space, covering all the space-time pixel locations in a feed-forward fashion. Contrast to the previous approaches, the abundant use of the guidance information allows us to better handle the challenges such as appearance changes and occlussions. We validate our method on the latest benchmark sets and achieved the state-of-the-art performance (overall score of 79.4 on Youtube-VOS val set, J of 88.7 and 79.2 on DAVIS 2016/2017 val set respectively) while having a fast runtime (0.16 second/frame on DAVIS 2016 val set).