pwc-archive/papers-with-abstracts huggingface dataset:
Teacher Supervises Students How to Learn From Partially Labeled Images for Facial Landmark Detection
Authors: Xuanyi Dong, Yi Yang
Date: 2019-08-06 00:00:00
Proceeding: ICCV 2019 10
Tasks: Facial Landmark Detection

Facial landmark detection aims to localize the anatomically defined points of human faces. In this paper, we study facial landmark detection from partially labeled facial images. A typical approach is to (1) train a detector on the labeled images; (2) generate new training samples using this detector's prediction as pseudo labels of unlabeled images; (3) retrain the detector on the labeled samples and partial pseudo labeled samples. In this way, the detector can learn from both labeled and unlabeled data to become robust. In this paper, we propose an interaction mechanism between a teacher and two students to generate more reliable pseudo labels for unlabeled data, which are beneficial to semi-supervised facial landmark detection. Specifically, the two students are instantiated as dual detectors. The teacher learns to judge the quality of the pseudo labels generated by the students and filter out unqualified samples before the retraining stage. In this way, the student detectors get feedback from their teacher and are retrained by premium data generated by itself. Since the two students are trained by different samples, a combination of their predictions will be more robust as the final prediction compared to either prediction. Extensive experiments on 300-W and AFLW benchmarks show that the interactions between teacher and students contribute to better utilization of the unlabeled data and achieves state-of-the-art performance.
Addressing Data Bias Problems for Chest X-ray Image Report Generation
Authors: Philipp Harzig, Yan-Ying Chen, Francine Chen, Rainer Lienhart
Date: 2019-08-06 00:00:00
Tasks: Medical Report Generation, Sentence

Automatic medical report generation from chest X-ray images is one possibility for assisting doctors to reduce their workload. However, the different patterns and data distribution of normal and abnormal cases can bias machine learning models. Previous attempts did not focus on isolating the generation of the abnormal and normal sentences in order to increase the variability of generated paragraphs. To address this, we propose to separate abnormal and normal sentence generation by using two different word LSTMs in a hierarchical LSTM model. We conduct an analysis on the distinctiveness of generated sentences compared to the BLEU score, which increases when less distinct reports are generated. We hope our findings will help to encourage the development of new metrics to better verify methods of automatic medical report generation.
Semi-Supervised Adversarial Monocular Depth Estimation
Authors: Rongrong Ji, Ke Li, Yan Wang, Xiaoshuai Sun, Feng Guo, Xiaowei Guo, Yongjian Wu, Feiyue Huang, Jiebo Luo
Date: 2019-08-06 00:00:00
Tasks: Depth Estimation, Monocular Depth Estimation

In this paper, we address the problem of monocular depth estimation when only a limited number of training image-depth pairs are available. To achieve a high regression accuracy, the state-of-the-art estimation methods rely on CNNs trained with a large number of image-depth pairs, which are prohibitively costly or even infeasible to acquire. Aiming to break the curse of such expensive data collections, we propose a semi-supervised adversarial learning framework that only utilizes a small number of image-depth pairs in conjunction with a large number of easily-available monocular images to achieve high performance. In particular, we use one generator to regress the depth and two discriminators to evaluate the predicted depth , i.e., one inspects the image-depth pair while the other inspects the depth channel alone. These two discriminators provide their feedbacks to the generator as the loss to generate more realistic and accurate depth predictions. Experiments show that the proposed approach can (1) improve most state-of-the-art models on the NYUD v2 dataset by effectively leveraging additional unlabeled data sources; (2) reach state-of-the-art accuracy when the training set is small, e.g., on the Make3D dataset; (3) adapt well to an unseen new dataset (Make3D in our case) after training on an annotated dataset (KITTI in our case).
Neural Blind Deconvolution Using Deep Priors
Authors: Dongwei Ren, Kai Zhang, Qilong Wang, QinGhua Hu, WangMeng Zuo
Date: 2019-08-06 00:00:00
Proceeding: CVPR 2020 6
Tasks: Deblurring, Self-Supervised Learning

Blind deconvolution is a classical yet challenging low-level vision problem with many real-world applications. Traditional maximum a posterior (MAP) based methods rely heavily on fixed and handcrafted priors that certainly are insufficient in characterizing clean images and blur kernels, and usually adopt specially designed alternating minimization to avoid trivial solution. In contrast, existing deep motion deblurring networks learn from massive training images the mapping to clean image or blur kernel, but are limited in handling various complex and large size blur kernels. To connect MAP and deep models, we in this paper present two generative networks for respectively modeling the deep priors of clean image and blur kernel, and propose an unconstrained neural optimization solution to blind deconvolution. In particular, we adopt an asymmetric Autoencoder with skip connections for generating latent clean image, and a fully-connected network (FCN) for generating blur kernel. Moreover, the SoftMax nonlinearity is applied to the output layer of FCN to meet the non-negative and equality constraints. The process of neural optimization can be explained as a kind of "zero-shot" self-supervised learning of the generative networks, and thus our proposed method is dubbed SelfDeblur. Experimental results show that our SelfDeblur can achieve notable quantitative gains as well as more visually plausible deblurring results in comparison to state-of-the-art blind deconvolution methods on benchmark datasets and real-world blurry images. The source code is available at https://github.com/csdwren/SelfDeblur
Learning Aberrance Repressed Correlation Filters for Real-Time UAV Tracking
Authors: Ziyuan Huang, Changhong Fu, Yiming Li, Fuling Lin, Peng Lu
Date: 2019-08-06 00:00:00
Proceeding: ICCV 2019 10
Tasks: Object Tracking

Traditional framework of discriminative correlation filters (DCF) is often subject to undesired boundary effects. Several approaches to enlarge search regions have been already proposed in the past years to make up for this shortcoming. However, with excessive background information, more background noises are also introduced and the discriminative filter is prone to learn from the ambiance rather than the object. This situation, along with appearance changes of objects caused by full/partial occlusion, illumination variation, and other reasons has made it more likely to have aberrances in the detection process, which could substantially degrade the credibility of its result. Therefore, in this work, a novel approach to repress the aberrances happening during the detection process is proposed, i.e., aberrance repressed correlation filter (ARCF). By enforcing restriction to the rate of alteration in response maps generated in the detection phase, the ARCF tracker can evidently suppress aberrances and is thus more robust and accurate to track objects. Considerable experiments are conducted on different UAV datasets to perform object tracking from an aerial view, i.e., UAV123, UAVDT, and DTB70, with 243 challenging image sequences containing over 90K frames to verify the performance of the ARCF tracker and it has proven itself to have outperformed other 20 state-of-the-art trackers based on DCF and deep-based frameworks with sufficient speed for real-time applications.
Learning Cross-Modal Deep Representations for Multi-Modal MR Image Segmentation
Authors: Cheng Li, Hui Sun, Zaiyi Liu, Meiyun Wang, Hairong Zheng, Shan-Shan Wang
Date: 2019-08-06 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Multi-modal magnetic resonance imaging (MRI) is essential in clinics for comprehensive diagnosis and surgical planning. Nevertheless, the segmentation of multi-modal MR images tends to be time-consuming and challenging. Convolutional neural network (CNN)-based multi-modal MR image analysis commonly proceeds with multiple down-sampling streams fused at one or several layers. Although inspiring performance has been achieved, the feature fusion is usually conducted through simple summation or concatenation without optimization. In this work, we propose a supervised image fusion method to selectively fuse the useful information from different modalities and suppress the respective noise signals. Specifically, an attention block is introduced as guidance for the information selection. From the different modalities, one modality that contributes most to the results is selected as the master modality, which supervises the information selection of the other assistant modalities. The effectiveness of the proposed method is confirmed through breast mass segmentation in MR images of two modalities and better segmentation results are achieved compared to the state-of-the-art methods.
Model-based Convolutional De-Aliasing Network Learning for Parallel MR Imaging
Authors: Yanxia Chen, Taohui Xiao, Cheng Li, Qiegen Liu, Shan-Shan Wang
Date: 2019-08-06 00:00:00
Tasks: De-aliasing, Sensitivity

Parallel imaging has been an essential technique to accelerate MR imaging. Nevertheless, the acceleration rate is still limited due to the ill-condition and challenges associated with the undersampled reconstruction. In this paper, we propose a model-based convolutional de-aliasing network with adaptive parameter learning to achieve accurate reconstruction from multi-coil undersampled k-space data. Three main contributions have been made: a de-aliasing reconstruction model was proposed to accelerate parallel MR imaging with deep learning exploring both spatial redundancy and multi-coil correlations; a split Bregman iteration algorithm was developed to solve the model efficiently; and unlike most existing parallel imaging methods which rely on the accuracy of the estimated multi-coil sensitivity, the proposed method can perform parallel reconstruction from undersampled data without explicit sensitivity calculation. Evaluations were conducted on \emph{in vivo} brain dataset with a variety of undersampling patterns and different acceleration factors. Our results demonstrated that this method could achieve superior performance in both quantitative and qualitative analysis, compared to three state-of-the-art methods.
Winnowing: Local Algorithms for Document Fingerprinting
Authors: Saul Schleimer MSCS University of Illinois, Chicago saul@math.uic.edu, Daniel S. Wilkerson Computer Science Division UC Berkeley dsw@cs.berkeley.edu, Alex Aiken Computer Science Division UC Berkeley aiken@cs.berkeley.edu
Date: 2003-06-09 00:00:00
Proceeding: Conference 2003 6

Digital content is for copying: quotation, revision, plagiarism, and file sharing all create copies. Document fingerprinting is concerned with accurately identifying copying, including small partial copies, within large sets of documents. We introduce the class of local document fingerprinting algorithms, which seems to capture an essential property of any fingerprinting technique guaranteed to detect copies. We prove a novel lower bound on the performance of any local algorithm. We also develop winnowing, an efficient local fingerprinting algorithm, and show that winnowing’s performance is within 33% of the lower bound. Finally, we also give experimental results on Web data, and report experience with MOSS, a widely-used plagiarism detection service.
Don't go chasing artificial waterfalls: Simulating cascading failures in the power grid and the impact of artificial line-limit methods on results
Authors: Jonathan Bourne, Aidan O'Sullivan, Elsa Arcaute
Date: 2019-07-30 00:00:00

Research into cascading failures in power-transmission networks requires detailed data on the capacity of individual transmission lines. However, these data are often unavailable to researchers. As a result, line limits are often modelled by assuming they are proportional to some average load. Little research exists, however, to support this assumption as being realistic. In this paper, we analyse the proportional-loading (PL) approach and compare it to two linear models that use voltage and initial power flow as variables. In conducting this modelling, we test the ability of artificial line limits to model true line limits, the damage done during an attack and the order in which edges are lost. we also test how accurately these methods rank the relative performance of different attack strategies. We find that the linear models are the top-performing method or close to the top in all tests. In comparison, the tolerance value that produces the best PL limits changes depending on the test. The PL approach was a particularly poor fit when the line tolerance was less than two, which is the most commonly used value range in cascading-failure research. We also find indications that the accuracy of modelling line limits does not indicate how well a model will represent grid collapse. In addition, we find evidence that the network's topology can be used to estimate the system's true mean loading. The findings of this paper provide an understanding of the weaknesses of the PL approach and offer an alternative method of line-limit modelling.
Zero watermarking scheme for citygml
Authors: Dayou, Jiang; Jongweon, Kim
Date: 2018-10-30 00:00:00
Proceeding: Journal of Theoretical and Applied Information Technology 2018 10
Tasks: Translation

City Geography Markup Language (CityGML) is developed as an open data model, which is based on the XML format for the storage and exchange of virtual 3D city models. CityGML defines the basic entities, attributes, and relations of a 3D city model. With respect to the cost-effective sustainable maintenance of 3D city models, it allows the reuse of the same data in different application fields. However, the copyright protection in suchlike virtual 3D city filed is not popular. Therefore, we present a zero watermarking scheme based on the global geometry property of the point cloud for CityGML. Firstly, the paper analyzes the data structure of CityGML and discusses the feasibility data for the watermark. Secondly, extracts the point and topology information from CityGML. Finally, generates the zero-watermarking by using the global geometry property from vertex norm. We conduct several experiments on CityGML models with translation, rotation, uniform scaling, vertex re-ordering, simplification, smoothing, and noise attacks. Experiments show the scheme can satisfy the requests for copyright protection of CityGML models.
A Blind Watermarking Method for CityGML
Authors: Dayou, Jiang; Jongweon, Kim
Date: 2018-07-20 00:00:00
Proceeding: IADIS International Conference Computer Graphics, Visualization, Computer Vision and Image Processing 2018 (part of MCCSIS 2018 2018 7
Tasks: Translation

We propose a blind watermarking method for CityGML in this paper. Firstly, we parse the CityGML text file and select the virtual 3D city model for watermarking. Secondly, we transform the model by using geometric invariance. Then, we transform the Cartesian coordinate to the Spherical coordinate to obtain the sphere radii of each vertex. Finally, we insert the watermark by altering the DCT coefficients of the sphere radii segment. We conduct several experiments using different parameters to explore the watermark payload. The method can embed 64 bits watermark into a CityGML model. In addition, it shows good robustness against adding noise, rotation, uniform scaling, translation, and vertices reordering attacks. However, it still has drawbacks under cropping and simplification attacks.
Digital Watermarking Model for CityGML
Authors: Jongweon, Kim
Date: 2017-10-01 00:00:00
Proceeding: The Journal of MacroTrends in Technology and Innovation 2017 10
Tasks: Autonomous Navigation, model

The geospatial information is getting more important as autonomous navigation technology is applied to moving objects such as automobiles and drones. In addition, examples of using spatial information in virtual reality augmented reality, and games are increasing. CityGML is an international standard for effectively representing 3D geospatial information. As more applications using geospatial information appear, the controversy over copyright infringement is also increasing. Despite the controversy over copyright infringement, CityGML still does not study the technology for copyright protection. In this paper, we propose a digital watermarking model for copyright protection of CityGML and analyze the technical requirements of the model.
Artwork painting identification method for panorama based on adaptive rectilinear projection and optimized ASIFT
Authors: Dayou, Jiang; Jongweon, Kim
Date: 2019-07-28 00:00:00
Proceeding: Multimedia Tools and Applications 2019 7
Tasks: Image Enhancement

In the paper, the authors present an artwork painting identification method for panorama based on adaptive rectilinear projection and optimized ASIFT (Affine Scale-Invariant Feature Transform). Firstly, the authors use the panorama dataset to train the artwork painting detection network to obtain the location information of artwork paintings. Secondly, the authors use the adaptive rectilinear projection to map the artwork painting into a square image with a fixed size. Then the authors use the image enhancement method to improve the image quality. Finally, the authors use the optimized ASIFT for features extraction and image matching. Several contrast experiments were conducted on the artwork paintings panorama dataset for artwork paintings identification. The results show that the proposed method can achieve 96% identification accuracy on average for the whole test artwork paintings panorama dataset. The proposed adaptive rectilinear based-method can improve at least 20% of the recognition accuracy. The proposed optimized ASIFT can improve at least 30% of the identification accuracy than SIFT. The authors also study other factors such as the size of the original artwork image, the image matching threshold, whether using image enhancement or not. The results show the size of the original artwork has little influence on the artwork identification in the panorama. The image matching threshold with 2.0 is better than 3.0. Furthermore, using the image enhancement method can improve about 2% of the identification accuracy.
Artwork Recognition for Panorama Images Based on Optimized ASIFT and Cubic Projection
Authors: Dayou, Jiang; Jongweon, Kim
Date: 2018-02-01 00:00:00
Proceeding: International Journal of Machine Learning and Computing 2018 2
Tasks: Object Recognition

Few studies have been published on object recognition for panorama images. To prevent the infringement of artworks in 360-degree images, we put forward an efficient method for artworks recognition inside 360-degree images in this paper. To start with, we employed the improved cubic projection to transform the distorted panorama image. Then, we used the optimized Affine Invariant Feature Transform (ASIFT) algorithm for extracting local features of the transformed image. Finally, the feature point matching is based on one-to-one mapping constraint. The overall performance of the method is investigated on panorama dataset and the experimental results are compared with other well-known local feature extraction methods and original panorama image. The experimental results show that using the proposed method can improve around 30% of the accuracy for relatively higher distorted panorama images and reduce the computing time.
Video searching and fingerprint detection by using the image query and PlaceNet-based shot boundary detection method
Authors: Dayou, Jiang; Jongweon, Kim
Date: 2018-09-26 00:00:00
Proceeding: Applied Sciences 2018 9
Tasks: Boundary Detection

This work presents a novel shot boundary detection (SBD) method based on the Place-centric deep network (PlaceNet), with the aim of using video shots and image queries for video searching (VS) and fingerprint detection. The SBD method has three stages. In the first stage, we employed Local Binary Pattern-Singular Value Decomposition (LBP-SVD) features for candidate shot boundaries selection. In the second stage, we used the PlaceNet to select the shot boundary by semantic labels. In the third stage, we used the Scale-Invariant Feature Transform (SIFT) descriptor to eliminate falsely detected boundaries. The experimental results show that our SBD method is effective on a series of SBD datasets. In addition, video searching experiments are conducted by using one query image instead of video sequences. The results under several image transitions by using shot fingerprints have shown good precision.
Texture image retrieval using DTCWT-SVD and local binary pattern features
Authors: Dayou, Jiang; Jongweon, Kim
Date: 2017-12-01 00:00:00
Proceeding: Journal of Information Processing Systems 2017 12
Tasks: Image Retrieval, Retrieval, Texture Image Retrieval

The combination texture feature extraction approach for texture image retrieval is proposed in this paper. Two kinds of low-level texture features were combined in the approach. One of them was extracted from singular value decomposition (SVD) based dual-tree complex wavelet transform (DTCWT) coefficients, and the other one was extracted from multi-scale local binary patterns (LBPs). The fusion features of SVD based multi-directional wavelet features and multi-scale LBP features have short dimensions of the feature vector. The comparing experiments are conducted on Brodatz and Vistex datasets. According to the experimental results, the proposed method has a relatively better performance in aspect of retrieval accuracy and time complexity upon the existing methods.
Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics
Authors: Mark Weber, Giacomo Domeniconi, Jie Chen, Daniel Karl I. Weidele, Claudio Bellei, Tom Robinson, Charles E. Leiserson
Date: 2019-07-31 00:00:00
Tasks: Binary Classification, Time Series Analysis

Anti-money laundering (AML) regulations play a critical role in safeguarding financial systems, but bear high costs for institutions and drive financial exclusion for those on the socioeconomic and international margins. The advent of cryptocurrency has introduced an intriguing paradox: pseudonymity allows criminals to hide in plain sight, but open data gives more power to investigators and enables the crowdsourcing of forensic analysis. Meanwhile advances in learning algorithms show great promise for the AML toolkit. In this workshop tutorial, we motivate the opportunity to reconcile the cause of safety with that of financial inclusion. We contribute the Elliptic Data Set, a time series graph of over 200K Bitcoin transactions (nodes), 234K directed payment flows (edges), and 166 node features, including ones based on non-public data; to our knowledge, this is the largest labelled transaction data set publicly available in any cryptocurrency. We share results from a binary classification task predicting illicit transactions using variations of Logistic Regression (LR), Random Forest (RF), Multilayer Perceptrons (MLP), and Graph Convolutional Networks (GCN), with GCN being of special interest as an emergent new method for capturing relational information. The results show the superiority of Random Forest (RF), but also invite algorithmic work to combine the respective powers of RF and graph methods. Lastly, we consider visualization for analysis and explainability, which is difficult given the size and dynamism of real-world transaction graphs, and we offer a simple prototype capable of navigating the graph and observing model performance on illicit activity over time. With this tutorial and data set, we hope to a) invite feedback in support of our ongoing inquiry, and b) inspire others to work on this societally important challenge.