pwc-archive/papers-with-abstracts huggingface dataset:
No-reference Screen Content Image Quality Assessment with Unsupervised Domain Adaptation
Authors: Baoliang Chen, Haoliang Li, Hongfei Fan, Shiqi Wang
Date: 2020-08-19 00:00:00
Tasks: Diversity, Domain Adaptation, Image Quality Assessment, Learning-To-Rank, Philosophy, Unsupervised Domain Adaptation

In this paper, we quest the capability of transferring the quality of natural scene images to the images that are not acquired by optical cameras (e.g., screen content images, SCIs), rooted in the widely accepted view that the human visual system has adapted and evolved through the perception of natural environment. Here, we develop the first unsupervised domain adaptation based no reference quality assessment method for SCIs, leveraging rich subjective ratings of the natural images (NIs). In general, it is a non-trivial task to directly transfer the quality prediction model from NIs to a new type of content (i.e., SCIs) that holds dramatically different statistical characteristics. Inspired by the transferability of pair-wise relationship, the proposed quality measure operates based on the philosophy of improving the transferability and discriminability simultaneously. In particular, we introduce three types of losses which complementarily and explicitly regularize the feature space of ranking in a progressive manner. Regarding feature discriminatory capability enhancement, we propose a center based loss to rectify the classifier and improve its prediction capability not only for source domain (NI) but also the target domain (SCI). For feature discrepancy minimization, the maximum mean discrepancy (MMD) is imposed on the extracted ranking features of NIs and SCIs. Furthermore, to further enhance the feature diversity, we introduce the correlation penalization between different feature dimensions, leading to the features with lower rank and higher diversity. Experiments show that our method can achieve higher performance on different source-target settings based on a light-weight convolution neural network. The proposed method also sheds light on learning quality assessment measures for unseen application-specific content without the cumbersome and costing subjective evaluations.
Physically-Constrained Transfer Learning through Shared Abundance Space for Hyperspectral Image Classification
Authors: Ying Qu, Razieh Kaviani Baghbaderani, Wei Li, Lianru Gao, Hairong Qi
Date: 2020-08-19 00:00:00
Tasks: General Classification, Hyperspectral Image Classification, image-classification, Image Classification, Transfer Learning

Hyperspectral image (HSI) classification is one of the most active research topics and has achieved promising results boosted by the recent development of deep learning. However, most state-of-the-art approaches tend to perform poorly when the training and testing images are on different domains, e.g., source domain and target domain, respectively, due to the spectral variability caused by different acquisition conditions. Transfer learning-based methods address this problem by pre-training in the source domain and fine-tuning on the target domain. Nonetheless, a considerable amount of data on the target domain has to be labeled and non-negligible computational resources are required to retrain the whole network. In this paper, we propose a new transfer learning scheme to bridge the gap between the source and target domains by projecting the HSI data from the source and target domains into a shared abundance space based on their own physical characteristics. In this way, the domain discrepancy would be largely reduced such that the model trained on the source domain could be applied on the target domain without extra efforts for data labeling or network retraining. The proposed method is referred to as physically-constrained transfer learning through shared abundance space (PCTL-SAS). Extensive experimental results demonstrate the superiority of the proposed method as compared to the state-of-the-art. The success of this endeavor would largely facilitate the deployment of HSI classification for real-world sensing scenarios.
Every Pixel Matters: Center-aware Feature Alignment for Domain Adaptive Object Detector
Authors: Cheng-Chun Hsu, Yi-Hsuan Tsai, Yen-Yu Lin, Ming-Hsuan Yang
Date: 2020-08-19 00:00:00
Proceeding: ECCV 2020 8
Tasks: Domain Adaptation

A domain adaptive object detector aims to adapt itself to unseen domains that may contain variations of object appearance, viewpoints or backgrounds. Most existing methods adopt feature alignment either on the image level or instance level. However, image-level alignment on global features may tangle foreground/background pixels at the same time, while instance-level alignment using proposals may suffer from the background noise. Different from existing solutions, we propose a domain adaptation framework that accounts for each pixel via predicting pixel-wise objectness and centerness. Specifically, the proposed method carries out center-aware alignment by paying more attention to foreground pixels, hence achieving better adaptation across domains. We demonstrate our method on numerous adaptation settings with extensive experimental results and show favorable performance against existing state-of-the-art algorithms.
LIRA: Lifelong Image Restoration from Unknown Blended Distortions
Authors: Jianzhao Liu, Jianxin Lin, Xin Li, Wei Zhou, Sen Liu, Zhibo Chen
Date: 2020-08-19 00:00:00
Proceeding: ECCV 2020 8
Tasks: Image Restoration, SSIM

Most existing image restoration networks are designed in a disposable way and catastrophically forget previously learned distortions when trained on a new distortion removal task. To alleviate this problem, we raise the novel lifelong image restoration problem for blended distortions. We first design a base fork-join model in which multiple pre-trained expert models specializing in individual distortion removal task work cooperatively and adaptively to handle blended distortions. When the input is degraded by a new distortion, inspired by adult neurogenesis in human memory system, we develop a neural growing strategy where the previously trained model can incorporate a new expert branch and continually accumulate new knowledge without interfering with learned knowledge. Experimental results show that the proposed approach can not only achieve state-of-the-art performance on blended distortions removal tasks in both PSNR/SSIM metrics, but also maintain old expertise while learning new restoration tasks.
Spatio-temporal relationships between rainfall and convective clouds during Indian Monsoon through a discrete lens
Authors: Arjun Sharma, Adway Mitra, Vishal Vasan, Rama Govindarajan
Date: 2020-08-19 00:00:00

The Indian monsoon, a multi-variable process causing heavy rains during June-September every year, is very heterogeneous in space and time. We study the relationship between rainfall and Outgoing Longwave Radiation (OLR, convective cloud cover) for monsoon between 2004-2010. To identify, classify and visualize spatial patterns of rainfall and OLR we use a discrete and spatio-temporally coherent representation of the data, created using a statistical model based on Markov Random Field. Our approach clusters the days with similar spatial distributions of rainfall and OLR into a small number of spatial patterns. We find that eight daily spatial patterns each in rainfall and OLR, and seven joint patterns of rainfall and OLR, describe over 90\% of all days. Through these patterns, we find that OLR generally has a strong negative correlation with precipitation, but with significant spatial variations. In particular, peninsular India (except west coast) is under significant convective cloud cover over a majority of days but remains rainless. We also find that much of the monsoon rainfall co-occurs with low OLR, but some amount of rainfall in Eastern and North-western India in June occurs on OLR days, presumably from shallow clouds. To study day-to-day variations of both quantities, we identify spatial patterns in the temporal gradients computed from the observations. We find that changes in convective cloud activity across India most commonly occur due to the establishment of a north-south OLR gradient which persists for 1-2 days and shifts the convective cloud cover from light to deep or vice versa. Such changes are also accompanied by changes in the spatial distribution of precipitation. The present work thus provides a highly reduced description of the complex spatial patterns and their day-to-day variations, and could form a useful tool for future simplified descriptions of this process.
DONet: Dual Objective Networks for Skin Lesion Segmentation
Authors: Yaxiong Wang, Yunchao Wei, Xueming Qian, Li Zhu, Yi Yang
Date: 2020-08-19 00:00:00
Tasks: Lesion Segmentation, Segmentation, Semantic Segmentation, Skin Lesion Segmentation

Skin lesion segmentation is a crucial step in the computer-aided diagnosis of dermoscopic images. In the last few years, deep learning based semantic segmentation methods have significantly advanced the skin lesion segmentation results. However, the current performance is still unsatisfactory due to some challenging factors such as large variety of lesion scale and ambiguous difference between lesion region and background. In this paper, we propose a simple yet effective framework, named Dual Objective Networks (DONet), to improve the skin lesion segmentation. Our DONet adopts two symmetric decoders to produce different predictions for approaching different objectives. Concretely, the two objectives are actually defined by different loss functions. In this way, the two decoders are encouraged to produce differentiated probability maps to match different optimization targets, resulting in complementary predictions accordingly. The complementary information learned by these two objectives are further aggregated together to make the final prediction, by which the uncertainty existing in segmentation maps can be significantly alleviated. Besides, to address the challenge of large variety of lesion scales and shapes in dermoscopic images, we additionally propose a recurrent context encoding module (RCEM) to model the complex correlation among skin lesions, where the features with different scale contexts are efficiently integrated to form a more robust representation. Extensive experiments on two popular benchmarks well demonstrate the effectiveness of the proposed DONet. In particular, our DONet achieves 0.881 and 0.931 dice score on ISIC 2018 and $\text{PH}^2$, respectively. Code will be made public available.
Improving Blind Spot Denoising for Microscopy
Authors: Anna S. Goncharova, Alf Honigmann, Florian Jug, Alexander Krull
Date: 2020-08-19 00:00:00
Tasks: Denoising

Many microscopy applications are limited by the total amount of usable light and are consequently challenged by the resulting levels of noise in the acquired images. This problem is often addressed via (supervised) deep learning based denoising. Recently, by making assumptions about the noise statistics, self-supervised methods have emerged. Such methods are trained directly on the images that are to be denoised and do not require additional paired training data. While achieving remarkable results, self-supervised methods can produce high-frequency artifacts and achieve inferior results compared to supervised approaches. Here we present a novel way to improve the quality of self-supervised denoising. Considering that light microscopy images are usually diffraction-limited, we propose to include this knowledge in the denoising process. We assume the clean image to be the result of a convolution with a point spread function (PSF) and explicitly include this operation at the end of our neural network. As a consequence, we are able to eliminate high-frequency artifacts and achieve self-supervised results that are very close to the ones achieved with traditional supervised methods.
Blur-Attention: A boosting mechanism for non-uniform blurred image restoration
Authors: Xiaoguang Li, Feifan Yang, Kin Man Lam, Li Zhuo, Jiafeng Li
Date: 2020-08-19 00:00:00
Tasks: Deblurring, Image Restoration, SSIM

Dynamic scene deblurring is a challenging problem in computer vision. It is difficult to accurately estimate the spatially varying blur kernel by traditional methods. Data-driven-based methods usually employ kernel-free end-to-end mapping schemes, which are apt to overlook the kernel estimation. To address this issue, we propose a blur-attention module to dynamically capture the spatially varying features of non-uniform blurred images. The module consists of a DenseBlock unit and a spatial attention unit with multi-pooling feature fusion, which can effectively extract complex spatially varying blur features. We design a multi-level residual connection structure to connect multiple blur-attention modules to form a blur-attention network. By introducing the blur-attention network into a conditional generation adversarial framework, we propose an end-to-end blind motion deblurring method, namely Blur-Attention-GAN (BAG), for a single image. Our method can adaptively select the weights of the extracted features according to the spatially varying blur features, and dynamically restore the images. Experimental results show that the deblurring capability of our method achieved outstanding objective performance in terms of PSNR, SSIM, and subjective visual quality. Furthermore, by visualizing the features extracted by the blur-attention module, comprehensive discussions are provided on its effectiveness.
Victim or Perpetrator? Analysis of Violent Characters Portrayals from Movie Scripts
Authors: Victor R. Martinez, Krishna Somandepalli, Karan Singla, Anil Ramanakrishna, Yalda T. Uhls, Shrikanth Narayanan
Date: 2020-08-19 00:00:00

Violent content in the media can influence viewers' perception of the society. For example, frequent depictions of certain demographics as victims or perpetrators of violence can shape stereotyped attitudes. We propose that computational methods can aid in the large-scale analysis of violence in movies. The method we develop characterizes aspects of violent content solely from the language used in the scripts. Thus, our method is applicable to a movie in the earlier stages of content creation even before it is produced. This is complementary to previous works which rely on audio or video post production. In this work, we identify stereotypes in character roles (i.e., victim, perpetrator and narrator) based on the demographics of the actor casted for that role. Our results highlight two significant differences in the frequency of portrayals as well as the demographics of the interaction between victims and perpetrators : (1) female characters appear more often as victims, and (2) perpetrators are more likely to be White if the victim is Black or Latino. To date, we are the first to show that language used in movie scripts is a strong indicator of violent content, and that there are systematic portrayals of certain demographics as victims and perpetrators in a large dataset. This offers novel computational tools to assist in creating awareness of representations in storytelling
FinChat: Corpus and evaluation setup for Finnish chat conversations on everyday topics
Authors: Katri Leino, Juho Leinonen, Mittul Singh, Sami Virpioja, Mikko Kurimo
Date: 2020-08-19 00:00:00
Tasks: Chatbot, Retrieval

Creating open-domain chatbots requires large amounts of conversational data and related benchmark tasks to evaluate them. Standardized evaluation tasks are crucial for creating automatic evaluation metrics for model development; otherwise, comparing the models would require resource-expensive human evaluation. While chatbot challenges have recently managed to provide a plethora of such resources for English, resources in other languages are not yet available. In this work, we provide a starting point for Finnish open-domain chatbot research. We describe our collection efforts to create the Finnish chat conversation corpus FinChat, which is made available publicly. FinChat includes unscripted conversations on seven topics from people of different ages. Using this corpus, we also construct a retrieval-based evaluation task for Finnish chatbot development. We observe that off-the-shelf chatbot models trained on conversational corpora do not perform better than chance at choosing the right answer based on automatic metrics, while humans can do the same task almost perfectly. Similarly, in a human evaluation, responses to questions from the evaluation set generated by the chatbots are predominantly marked as incoherent. Thus, FinChat provides a challenging evaluation set, meant to encourage chatbot development in Finnish.
BabelEnconding at SemEval-2020 Task 3: Contextual Similarity as a Combination of Multilingualism and Language Models
Authors: Lucas R. C. Pessutto, Tiago de Melo, Viviane P. Moreira, Altigran da Silva
Date: 2020-08-19 00:00:00
Proceeding: SEMEVAL 2020
Tasks: Translation, Word Similarity

This paper describes the system submitted by our team (BabelEnconding) to SemEval-2020 Task 3: Predicting the Graded Effect of Context in Word Similarity. We propose an approach that relies on translation and multilingual language models in order to compute the contextual similarity between pairs of words. Our hypothesis is that evidence from additional languages can leverage the correlation with the human generated scores. BabelEnconding was applied to both subtasks and ranked among the top-3 in six out of eight task/language combinations and was the highest scoring system three times.
UoB at SemEval-2020 Task 12: Boosting BERT with Corpus Level Information
Authors: Wah Meng Lim, Harish Tayyar Madabushi
Date: 2020-08-19 00:00:00
Proceeding: SEMEVAL 2020
Tasks: Abuse Detection, Language Modeling, Language Modelling, Sentence

Pre-trained language model word representation, such as BERT, have been extremely successful in several Natural Language Processing tasks significantly improving on the state-of-the-art. This can largely be attributed to their ability to better capture semantic information contained within a sentence. Several tasks, however, can benefit from information available at a corpus level, such as Term Frequency-Inverse Document Frequency (TF-IDF). In this work we test the effectiveness of integrating this information with BERT on the task of identifying abuse on social media and show that integrating this information with BERT does indeed significantly improve performance. We participate in Sub-Task A (abuse detection) wherein we achieve a score within two points of the top performing team and in Sub-Task B (target detection) wherein we are ranked 4 of the 44 participating teams.
Multiple profiles sensor-based monitoring and anomaly detection
Authors: Chen Zhang, Hao Yan, Seungho Lee, Jianjun Shi
Date: 2018-10-31 00:00:00
Proceeding: JOURNAL OF QUALITY TECHNOLOGY 2018 10
Tasks: Anomaly Detection

Generally, in an advanced manufacturing system hundreds of sensors are deployed to measure key
process variables in real time. Thus it is desirable to develop methodologies to use real-time sensor
data for on-line system condition monitoring and anomaly detection. However, there are several challenges in developing an effective process monitoring system: (i) data streams generated by multiple
sensors are high-dimensional profiles; (ii) sensor signals are affected by noise due to system-inherent
variations; (iii) signals of different sensors have cluster-wise features; and (iv) an anomaly may cause
only sparse changes of sensor signals. To address these challenges, this article presents a real-time
multiple profiles sensor-based process monitoring system, which includes the following modules: (i)
preprocessing sensor signals to remove inherent variations and conduct profile alignments, (ii) using
multichannel functional principal component analysis (MFPCA)–based methods to extract sensor features by considering cluster-wise between-sensor correlations, and (iii) constructing a monitoring
scheme with the top-R strategy based on the extracted features, which has scalable detection power
for different fault patterns. Finally, we implement and demonstrate the proposed framework using
data from a real manufacturing system.
Large-Scale Study of Perceptual Video Quality
Authors: Zeina Sinno, Alan C. Bovik
Date: 2018-03-05 00:00:00
Tasks: Video Quality Assessment

The great variations of videographic skills, camera designs, compression and processing protocols, and displays lead to an enormous variety of video impairments. Current no-reference (NR) video quality models are unable to handle this diversity of distortions. This is true in part because available video quality assessment databases contain very limited content, fixed resolutions, were captured using a small number of camera devices by a few videographers and have been subjected to a modest number of distortions. As such, these databases fail to adequately represent real world videos, which contain very different kinds of content obtained under highly diverse imaging conditions and are subject to authentic, often commingled distortions that are impossible to simulate. As a result, NR video quality predictors tested on real-world video data often perform poorly. Towards advancing NR video quality prediction, we constructed a large-scale video quality assessment database containing 585 videos of unique content, captured by a large number of users, with wide ranges of levels of complex, authentic distortions. We collected a large number of subjective video quality scores via crowdsourcing. A total of 4776 unique participants took part in the study, yielding more than 205000 opinion scores, resulting in an average of 240 recorded human opinions per video. We demonstrate the value of the new resource, which we call the LIVE Video Quality Challenge Database (LIVE-VQC), by conducting a comparison of leading NR video quality predictors on it. This study is the largest video quality assessment study ever conducted along several key dimensions: number of unique contents, capture devices, distortion types and combinations of distortions, study participants, and recorded subjective scores. The database is available for download on this link: http://live.ece.utexas.edu/research/LIVEVQC/index.html .
A learning model to detect maliciousness of portable executable using integrated feature set
Authors: Ajit Kumar, K.S. Kuppusamy, G. Aghila
Date: 2017-01-31 00:00:00
Proceeding: journal 2017 1
Tasks: BIG-bench Machine Learning, General Classification, Malware Detection

Malware is one of the top most obstructions for expansion and growth of digital acceptance among the users. Both enterprises and common users are struggling to get protected from the malware in the cyberspace, which emphasizes the importance of developing efficient methods of malware detection. In this work, we propose a machine learning based solution to classify a sample as benign or malware with high accuracy and low computation overhead. An integrated feature set has been amalgamated as a combination of portable executable header fields raw value and derived values. Various machine-learning algorithms such as Decision Tree, Random Forest, kNN, Logistic Regression, Linear Discriminant Analysis and Naive Bayes were adopted in the classification of malware. Using existing raw feature set and the proposed integrated feature set we compared performance of each classifier. The empirical evidence indicates 98.4% classification accuracy in the 10-fold cross validation for the proposed integrated feature set. In the experiments conducted on the novel test data set the accuracy was observed as 89.23% for the integrated feature set which is 15% improvement on accuracy achieved with raw-feature set alone. Classification accuracy with only top N features (N = 5, 10, 15, 20, 25) are also experimented and it was observed that with only top 15 features 98% and 97% accuracy can be achieved on integrated and raw feature respectively.