pwc-archive/papers-with-abstracts huggingface dataset:
Deep learning for image segmentation: veritable or overhyped?
Authors: Zhenzhou Wang
Date: 2019-04-16 00:00:00
Tasks: Deep Learning, General Classification, image-classification, Image Classification, Image Segmentation, Segmentation, Semantic Segmentation

Deep learning has achieved great success as a powerful classification tool and also made great progress in sematic segmentation. As a result, many researchers also believe that deep learning is the most powerful tool for pixel level image segmentation. Could deep learning achieve the same pixel level accuracy as traditional image segmentation techniques by mapping the features of the object into a non-linear function? This paper gives a short survey of the accuracies achieved by deep learning so far in image classification and image segmentation. Compared to the high accuracies achieved by deep learning in classifying limited categories in international vision challenges, the image segmentation accuracies achieved by deep learning in the same challenges are only about eighty percent. On the contrary, the image segmentation accuracies achieved in international biomedical challenges are close to ninty five percent. Why the difference is so big? Since the accuracies of the competitors methods are only evaluated based on their submitted results instead of reproducing the results by submitting the source codes or the software, are the achieved accuracies verifiable or overhyped? We are going to find it out by analyzing the working principle of deep learning. Finally, we compared the accuracies of state of the art deep learning methods with a threshold selection method quantitatively. Experimental results showed that the threshold selection method could achieve significantly higher accuracy than deep learning methods in image segmentation.
MultiNet++: Multi-Stream Feature Aggregation and Geometric Loss Strategy for Multi-Task Learning
Authors: Sumanth Chennupati, Ganesh Sistu, Senthil Yogamani, Samir A Rawashdeh
Date: 2019-04-15 00:00:00
Tasks: Autonomous Driving, Multi-Task Learning

Multi-task learning is commonly used in autonomous driving for solving
various visual perception tasks. It offers significant benefits in terms of
both performance and computational complexity. Current work on multi-task
learning networks focus on processing a single input image and there is no
known implementation of multi-task learning handling a sequence of images. In
this work, we propose a multi-stream multi-task network to take advantage of
using feature representations from preceding frames in a video sequence for
joint learning of segmentation, depth, and motion. The weights of the current
and previous encoder are shared so that features computed in the previous frame
can be leveraged without additional computation. In addition, we propose to use
the geometric mean of task losses as a better alternative to the weighted
average of task losses. The proposed loss function facilitates better handling
of the difference in convergence rates of different tasks. Experimental results
on KITTI, Cityscapes and SYNTHIA datasets demonstrate that the proposed
strategies outperform various existing multi-task learning solutions.
Tightly Coupled 3D Lidar Inertial Odometry and Mapping
Authors: Haoyang Ye, Yuying Chen, Ming Liu
Date: 2019-04-15 00:00:00
Tasks: Motion Estimation, Sensor Fusion

Ego-motion estimation is a fundamental requirement for most mobile robotic
applications. By sensor fusion, we can compensate the deficiencies of
stand-alone sensors and provide more reliable estimations. We introduce a
tightly coupled lidar-IMU fusion method in this paper. By jointly minimizing
the cost derived from lidar and IMU measurements, the lidar-IMU odometry (LIO)
can perform well with acceptable drift after long-term experiment, even in
challenging cases where the lidar measurements can be degraded. Besides, to
obtain more reliable estimations of the lidar poses, a rotation-constrained
refinement algorithm (LIO-mapping) is proposed to further align the lidar poses
with the global map. The experiment results demonstrate that the proposed
method can estimate the poses of the sensor pair at the IMU update rate with
high precision, even under fast motion conditions or with insufficient
features.
Co-regularized Multi-view Sparse Reconstruction Embedding for Dimension Reduction
Authors: Huibing Wang, Jinjia Peng, Xianping Fu
Date: 2019-04-01 00:00:00
Tasks: Dimensionality Reduction, Document Classification, Face Recognition, Image Retrieval, Retrieval

With the development of information technology, we have witnessed an age of
data explosion which produces a large variety of data filled with redundant
information. Because dimension reduction is an essential tool which embeds
high-dimensional data into a lower-dimensional subspace to avoid redundant
information, it has attracted interests from researchers all over the world.
However, facing with features from multiple views, it's difficult for most
dimension reduction methods to fully comprehended multi-view features and
integrate compatible and complementary information from these features to
construct low-dimensional subspace directly. Furthermore, most multi-view
dimension reduction methods cannot handle features from nonlinear spaces with
high dimensions. Therefore, how to construct a multi-view dimension reduction
methods which can deal with multi-view features from high-dimensional nonlinear
space is of vital importance but challenging. In order to address this problem,
we proposed a novel method named Co-regularized Multi-view Sparse
Reconstruction Embedding (CMSRE) in this paper. By exploiting correlations of
sparse reconstruction from multiple views, CMSRE is able to learn local sparse
structures of nonlinear manifolds from multiple views and constructs
significative low-dimensional representations for them. Due to the proposed
co-regularized scheme, correlations of sparse reconstructions from multiple
views are preserved by CMSRE as much as possible. Furthermore, sparse
representation produces more meaningful correlations between features from each
single view, which helps CMSRE to gain better performances. Various evaluations
based on the applications of document classification, face recognition and
image retrieval can demonstrate the effectiveness of the proposed approach on
multi-view dimension reduction.
Few-Shot Learning with Localization in Realistic Settings
Authors: Davis Wertheimer, Bharath Hariharan
Date: 2019-04-09 00:00:00
Proceeding: CVPR 2019 6
Tasks: Few-Shot Learning, Meta-Learning

Traditional recognition methods typically require large, artificially-balanced training classes, while few-shot learning methods are tested on artificially small ones. In contrast to both extremes, real world recognition problems exhibit heavy-tailed class distributions, with cluttered scenes and a mix of coarse and fine-grained class distinctions. We show that prior methods designed for few-shot learning do not work out of the box in these challenging conditions, based on a new "meta-iNat" benchmark. We introduce three parameter-free improvements: (a) better training procedures based on adapting cross-validation to meta-learning, (b) novel architectures that localize objects using limited bounding box annotations before classification, and (c) simple parameter-free expansions of the feature space based on bilinear pooling. Together, these improvements double the accuracy of state-of-the-art models on meta-iNat while generalizing to prior benchmarks, complex neural architectures, and settings with substantial domain shift.
QANet -- Quality Assurance Network for Image Segmentation
Authors: Assaf Arbelle, Eliav Elul, Tammy Riklin Raviv
Date: 2019-04-09 00:00:00
Tasks: Cell Segmentation, Image Segmentation, Instance Segmentation, Segmentation, Semantic Segmentation

We introduce a novel Deep Learning framework, which quantitatively estimates image segmentation quality without the need for human inspection or labeling. We refer to this method as a Quality Assurance Network -- QANet. Specifically, given an image and a `proposed' corresponding segmentation, obtained by any method including manual annotation, the QANet solves a regression problem in order to estimate a predefined quality measure with respect to the unknown ground truth. The QANet is by no means yet another segmentation method. Instead, it performs a multi-level, multi-feature comparison of an image-segmentation pair based on a unique network architecture, called the RibCage. To demonstrate the strength of the QANet, we addressed the evaluation of instance segmentation using two different datasets from different domains, namely, high throughput live cell microscopy images from the Cell Segmentation Benchmark and natural images of plants from the Leaf Segmentation Challenge. While synthesized segmentations were used to train the QANet, it was tested on segmentations obtained by publicly available methods that participated in the different challenges. We show that the QANet accurately estimates the scores of the evaluated segmentations with respect to the hidden ground truth, as published by the challenges' organizers. The code is available at: TBD.
Deep Localization of Mixed Image Tampering Techniques
Authors: Robin Elizabeth Yancey
Date: 2019-04-07 00:00:00
Tasks: Fraud Detection, object-detection, Object Detection, Region Proposal

With technological advances leading to an increase in mechanisms for image tampering, fraud detection methods must continue to be upgraded to match their sophistication. One problem with current methods is that they require prior knowledge of the method of forgery in order to determine which features to extract from the image to localize the region of interest. When a machine learning algorithm is used to learn different types of tampering from a large set of various image types, with a large enough database we can easily classify which images are tampered. However, we still are left with the question of which features to train on, and how to localize the manipulation. In this work, deep learning for object detection is adapted to tampering detection to solve these two problems, while fusing features from multiple classic techniques for improved accuracy. A Multi-stream version of the Faster RCNN network will be employed with the second stream having an input of the element-wise sum of the ELA and BAG error maps to provide even higher accuracy than a single stream alone.
Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage Dataset
Authors: Martin Mundt, Sagnik Majumder, Sreenivas Murali, Panagiotis Panetsos, Visvanathan Ramesh
Date: 2019-04-02 00:00:00
Proceeding: CVPR 2019 6
Tasks: General Classification, Meta-Learning, Neural Architecture Search, Reinforcement Learning, Reinforcement Learning (RL)

Recognition of defects in concrete infrastructure, especially in bridges, is
a costly and time consuming crucial first step in the assessment of the
structural integrity. Large variation in appearance of the concrete material,
changing illumination and weather conditions, a variety of possible surface
markings as well as the possibility for different types of defects to overlap,
make it a challenging real-world task. In this work we introduce the novel
COncrete DEfect BRidge IMage dataset (CODEBRIM) for multi-target classification
of five commonly appearing concrete defects. We investigate and compare two
reinforcement learning based meta-learning approaches, MetaQNN and efficient
neural architecture search, to find suitable convolutional neural network
architectures for this challenging multi-class multi-target task. We show that
learned architectures have fewer overall parameters in addition to yielding
better multi-target accuracy in comparison to popular neural architectures from
the literature evaluated in the context of our application.
Machine Vision Guided 3D Medical Image Compression for Efficient Transmission and Accurate Segmentation in the Clouds
Authors: Zihao Liu, Xiaowei Xu, Tao Liu, Qi Liu, Yanzhi Wang, Yiyu Shi, Wujie Wen, Meiping Huang, Haiyun Yuan, Jian Zhuang
Date: 2019-04-09 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Compression, Image Segmentation, Medical Image Analysis, Medical Image Segmentation, Segmentation, Semantic Segmentation

Cloud based medical image analysis has become popular recently due to the
high computation complexities of various deep neural network (DNN) based
frameworks and the increasingly large volume of medical images that need to be
processed. It has been demonstrated that for medical images the transmission
from local to clouds is much more expensive than the computation in the clouds
itself. Towards this, 3D image compression techniques have been widely applied
to reduce the data traffic. However, most of the existing image compression
techniques are developed around human vision, i.e., they are designed to
minimize distortions that can be perceived by human eyes. In this paper we will
use deep learning based medical image segmentation as a vehicle and demonstrate
that interestingly, machine and human view the compression quality differently.
Medical images compressed with good quality w.r.t. human vision may result in
inferior segmentation accuracy. We then design a machine vision oriented 3D
image compression framework tailored for segmentation using DNNs. Our method
automatically extracts and retains image features that are most important to
the segmentation. Comprehensive experiments on widely adopted segmentation
frameworks with HVSMR 2016 challenge dataset show that our method can achieve
significantly higher segmentation accuracy at the same compression rate, or
much better compression rate under the same segmentation accuracy, when
compared with the existing JPEG 2000 method. To the best of the authors'
knowledge, this is the first machine vision guided medical image compression
framework for segmentation in the clouds.
Tensor Sparse PCA and Face Recognition: A Novel Approach
Authors: Loc Hoang Tran, Linh Hoang Tran
Date: 2019-04-12 00:00:00
Tasks: Classification, Face Recognition, General Classification, regression

Face recognition is the important field in machine learning and pattern recognition research area. It has a lot of applications in military, finance, public security, to name a few. In this paper, the combination of the tensor sparse PCA with the nearest-neighbor method (and with the kernel ridge regression method) will be proposed and applied to the face dataset. Experimental results show that the combination of the tensor sparse PCA with any classification system does not always reach the best accuracy performance measures. However, the accuracy of the combination of the sparse PCA method and one specific classification system is always better than the accuracy of the combination of the PCA method and one specific classification system and is always better than the accuracy of the classification system itself.
An In-Depth Study on Open-Set Camera Model Identification
Authors: Pedro Ribeiro Mendes Júnior, Luca Bondi, Paolo Bestagini, Stefano Tubaro, Anderson Rocha
Date: 2019-04-11 00:00:00
Tasks: Open Set Learning

Camera model identification refers to the problem of linking a picture to the camera model used to shoot it. As this might be an enabling factor in different forensic applications to single out possible suspects (e.g., detecting the author of child abuse or terrorist propaganda material), many accurate camera model attribution methods have been developed in the literature. One of their main drawbacks, however, is the typical closed-set assumption of the problem. This means that an investigated photograph is always assigned to one camera model within a set of known ones present during investigation, i.e., training time, and the fact that the picture can come from a completely unrelated camera model during actual testing is usually ignored. Under realistic conditions, it is not possible to assume that every picture under analysis belongs to one of the available camera models. To deal with this issue, in this paper, we present the first in-depth study on the possibility of solving the camera model identification problem in open-set scenarios. Given a photograph, we aim at detecting whether it comes from one of the known camera models of interest or from an unknown one. We compare different feature extraction algorithms and classifiers specially targeting open-set recognition. We also evaluate possible open-set training protocols that can be applied along with any open-set classifier, observing that a simple of those alternatives obtains best results. Thorough testing on independent datasets shows that it is possible to leverage a recently proposed convolutional neural network as feature extractor paired with a properly trained open-set classifier aiming at solving the open-set camera model attribution problem even to small-scale image patches, improving over state-of-the-art available solutions.
Exploring Uncertainty Measures for Image-Caption Embedding-and-Retrieval Task
Authors: Kenta Hama, Takashi Matsubara, Kuniaki Uehara, Jianfei Cai
Date: 2019-04-09 00:00:00
Tasks: General Classification, regression, Retrieval

With the wide development of black-box machine learning algorithms,
particularly deep neural network (DNN), the practical demand for the
reliability assessment is rapidly rising. On the basis of the concept that
`Bayesian deep learning knows what it does not know,' the uncertainty of DNN
outputs has been investigated as a reliability measure for the classification
and regression tasks. However, in the image-caption retrieval task, well-known
samples are not always easy-to-retrieve samples. This study investigates two
aspects of image-caption embedding-and-retrieval systems. On one hand, we
quantify feature uncertainty by considering image-caption embedding as a
regression task, and use it for model averaging, which can improve the
retrieval performance. On the other hand, we further quantify posterior
uncertainty by considering the retrieval as a classification task, and use it
as a reliability measure, which can greatly improve the retrieval performance
by rejecting uncertain queries. The consistent performance of two uncertainty
measures is observed with different datasets (MS COCO and Flickr30k), different
deep learning architectures (dropout and batch normalization), and different
similarity functions.
On the computability of conditional probability
Authors: Nathanael L. Ackerman, Cameron E. Freer, Daniel M. Roy
Date: 2010-05-17 00:00:00

As inductive inference and machine learning methods in computer science see continued success, researchers are aiming to describe ever more complex probabilistic models and inference algorithms. It is natural to ask whether there is a universal computational procedure for probabilistic inference. We investigate the computability of conditional probability, a fundamental notion in probability theory and a cornerstone of Bayesian statistics. We show that there are computable joint distributions with noncomputable conditional distributions, ruling out the prospect of general inference algorithms, even inefficient ones. Specifically, we construct a pair of computable random variables in the unit interval such that the conditional distribution of the first variable given the second encodes the halting problem. Nevertheless, probabilistic inference is possible in many common modeling settings, and we prove several results giving broadly applicable conditions under which conditional distributions are computable. In particular, conditional distributions become computable when measurements are corrupted by independent computable noise with a sufficiently smooth bounded density.
Performance Evalution of 3D Keypoint Detectors and Descriptors for Plants Health Classification
Authors: Shiva Azimi, Brejesh lall, Tapan K. Gandhi
Date: 2019-04-02 00:00:00
Tasks: General Classification

Plant Phenomics based on imaging based techniques can be used to monitor the
health and the diseases of plants and crops. The use of 3D data for plant
phenomics is a recent phenomenon. However, since 3D point cloud contains more
information than plant images, in this paper, we compare the performance of
different keypoint detectors and local feature descriptors combinations for the
plant growth stage and it's growth condition classification based on 3D point
clouds of the plants. We have also implemented a modified form of 3D SIFT
descriptor, that is invariant to rotation and is computationally less intense
than most of the 3D SIFT descriptors reported in the existing literature. The
performance is evaluated in terms of the classification accuracy and the
results are presented in terms of accuracy tables. We find the ISS-SHOT and the
SIFT-SIFT combinations consistently perform better and Fisher Vector (FV) is a
better encoder than Vector of Linearly Aggregated (VLAD) for such applications.
It can serve as a better modality.
Learning 2D to 3D Lifting for Object Detection in 3D for Autonomous Vehicles
Authors: Siddharth Srivastava, Frederic Jurie, Gaurav Sharma
Date: 2019-03-27 00:00:00
Tasks: 3D Object Detection, 3D Object Detection From Monocular Images, Autonomous Driving, Autonomous Navigation, Autonomous Vehicles, Monocular 3D Object Localization, object-detection, Object Detection

We address the problem of 3D object detection from 2D monocular images in autonomous driving scenarios. We propose to lift the 2D images to 3D representations using learned neural networks and leverage existing networks working directly on 3D data to perform 3D object detection and localization. We show that, with carefully designed training mechanism and automatically selected minimally noisy data, such a method is not only feasible, but gives higher results than many methods working on actual 3D inputs acquired from physical sensors. On the challenging KITTI benchmark, we show that our 2D to 3D lifted method outperforms many recent competitive 3D networks while significantly outperforming previous state-of-the-art for 3D detection from monocular images. We also show that a late fusion of the output of the network trained on generated 3D images, with that trained on real 3D images, improves performance. We find the results very interesting and argue that such a method could serve as a highly reliable backup in case of malfunction of expensive 3D sensors, if not potentially making them redundant, at least in the case of low human injury risk autonomous navigation scenarios like warehouse automation.