pwc-archive/papers-with-abstracts huggingface dataset:
Detail-Preserving Transformer for Light Field Image Super-Resolution
Authors: Shunzhou Wang, Tianfei Zhou, Yao Lu, Huijun Di
Date: 2022-01-02 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Recently, numerous algorithms have been developed to tackle the problem of light field super-resolution (LFSR), i.e., super-resolving low-resolution light fields to gain high-resolution views. Despite delivering encouraging results, these approaches are all convolution-based, and are naturally weak in global relation modeling of sub-aperture images necessarily to characterize the inherent structure of light fields. In this paper, we put forth a novel formulation built upon Transformers, by treating LFSR as a sequence-to-sequence reconstruction task. In particular, our model regards sub-aperture images of each vertical or horizontal angular view as a sequence, and establishes long-range geometric dependencies within each sequence via a spatial-angular locally-enhanced self-attention layer, which maintains the locality of each sub-aperture image as well. Additionally, to better recover image details, we propose a detail-preserving Transformer (termed as DPT), by leveraging gradient maps of light field to guide the sequence learning. DPT consists of two branches, with each associated with a Transformer for learning from an original or gradient image sequence. The two branches are finally fused to obtain comprehensive feature representations for reconstruction. Evaluations are conducted on a number of light field datasets, including real-world scenes and synthetic data. The proposed method achieves superior performance comparing with other state-of-the-art schemes. Our code is publicly available at: https://github.com/BITszwang/DPT.
Riemannian Nearest-Regularized Subspace Classification for Polarimetric SAR images
Authors: Junfei Shi, Haiyan Jin
Date: 2022-01-02 00:00:00
Tasks: Classification, image-classification, Image Classification, Representation Learning

As a representation learning method, nearest regularized subspace(NRS) algorithm is an effective tool to obtain both accuracy and speed for PolSAR image classification. However, existing NRS methods use the polarimetric feature vector but the PolSAR original covariance matrix(known as Hermitian positive definite(HPD)matrix) as the input. Without considering the matrix structure, existing NRS-based methods cannot learn correlation among channels. How to utilize the original covariance matrix to NRS method is a key problem. To address this limit, a Riemannian NRS method is proposed, which consider the HPD matrices endow in the Riemannian space. Firstly, to utilize the PolSAR original data, a Riemannian NRS method(RNRS) is proposed by constructing HPD dictionary and HPD distance metric. Secondly, a new Tikhonov regularization term is designed to reduce the differences within the same class. Finally, the optimal method is developed and the first-order derivation is inferred. During the experimental test, only T matrix is used in the proposed method, while multiple of features are utilized for compared methods. Experimental results demonstrate the proposed method can outperform the state-of-art algorithms even using less features.
MissMarple : A Novel Socio-inspired Feature-transfer Learning Deep Network for Image Splicing Detection
Authors: Angelina L. Gokhale, Dhanya Pramod, Sudeep D. Thepade, Ravi Kulkarni
Date: 2021-12-15 00:00:00
Tasks: Deep Learning, Transfer Learning

In this paper we propose a novel socio-inspired convolutional neural network (CNN) deep learning model for image splicing detection. Based on the premise that learning from the detection of coarsely spliced image regions can improve the detection of visually imperceptible finely spliced image forgeries, the proposed model referred to as, MissMarple, is a twin CNN network involving feature-transfer learning. Results obtained from training and testing the proposed model using the benchmark datasets like Columbia splicing, WildWeb, DSO1 and a proposed dataset titled AbhAS consisting of realistic splicing forgeries revealed improvement in detection accuracy over the existing deep learning models.
V-LinkNet: Learning Contextual Inpainting Across Latent Space of Generative Adversarial Network
Authors: Jireh Jam, Connah Kendrick, Vincent Drouard, Kevin Walker, Moi Hoon Yap
Date: 2022-01-02 00:00:00
Tasks: Generative Adversarial Network, Image Inpainting

Image inpainting is a key technique in image processing task to predict the missing regions and generate realistic images. Given the advancement of existing generative inpainting models with feature extraction, propagation and reconstruction capabilities, there is lack of high-quality feature extraction and transfer mechanisms in deeper layers to tackle persistent aberrations on the generated inpainted regions. Our method, V-LinkNet, develops high-level feature transference to deep level textural context of inpainted regions our work, proposes a novel technique of combining encoders learning through a recursive residual transition layer (RSTL). The RSTL layer easily adapts dual encoders by increasing the unique semantic information through direct communication. By collaborating the dual encoders structure with contextualised feature representation loss function, our system gains the ability to inpaint with high-level features. To reduce biases from random mask-image pairing, we introduce a standard protocol with paired mask-image on the testing set of CelebA-HQ, Paris Street View and Places2 datasets. Our results show V-LinkNet performed better on CelebA-HQ and Paris Street View using this standard protocol. We will share the standard protocol and our codes with the research community upon acceptance of this paper.
Does a Face Mask Protect my Privacy?: Deep Learning to Predict Protected Attributes from Masked Face Images
Authors: Sachith Seneviratne, Nuran Kasthuriarachchi, Sanka Rasnayaka, Danula Hettiachchi, Ridwan Shariffdeen
Date: 2021-12-15 00:00:00

Contactless and efficient systems are implemented rapidly to advocate preventive methods in the fight against the COVID-19 pandemic. Despite the positive benefits of such systems, there is potential for exploitation by invading user privacy. In this work, we analyse the privacy invasiveness of face biometric systems by predicting privacy-sensitive soft-biometrics using masked face images. We train and apply a CNN based on the ResNet-50 architecture with 20,003 synthetic masked images and measure the privacy invasiveness. Despite the popular belief of the privacy benefits of wearing a mask among people, we show that there is no significant difference to privacy invasiveness when a mask is worn. In our experiments we were able to accurately predict sex (94.7%),race (83.1%) and age (MAE 6.21 and RMSE 8.33) from masked face images. Our proposed approach can serve as a baseline utility to evaluate the privacy-invasiveness of artificial intelligence systems that make use of privacy-sensitive information. We open-source all contributions for re-producibility and broader use by the research community.
Recurrent Feature Propagation and Edge Skip-Connections for Automatic Abdominal Organ Segmentation
Authors: Zefan Yang, Di Lin, Dong Ni, Yi Wang
Date: 2022-01-02 00:00:00
Tasks: Computed Tomography (CT), Decoder, Organ Segmentation, Segmentation, Semantic Segmentation

Automatic segmentation of abdominal organs in computed tomography (CT) images can support radiation therapy and image-guided surgery workflows. Developing of such automatic solutions remains challenging mainly owing to complex organ interactions and blurry boundaries in CT images. To address these issues, we focus on effective spatial context modeling and explicit edge segmentation priors. Accordingly, we propose a 3D network with four main components trained end-to-end including shared encoder, edge detector, decoder with edge skip-connections (ESCs) and recurrent feature propagation head (RFP-Head). To capture wide-range spatial dependencies, the RFP-Head propagates and harvests local features through directed acyclic graphs (DAGs) formulated with recurrent connections in an efficient slice-wise manner, with regard to spatial arrangement of image units. To leverage edge information, the edge detector learns edge prior knowledge specifically tuned for semantic segmentation by exploiting intermediate features from the encoder with the edge supervision. The ESCs then aggregate the edge knowledge with multi-level decoder features to learn a hierarchy of discriminative features explicitly modeling complementarity between organs' interiors and edges for segmentation. We conduct extensive experiments on two challenging abdominal CT datasets with eight annotated organs. Experimental results show that the proposed network outperforms several state-of-the-art models, especially for the segmentation of small and complicated structures (gallbladder, esophagus, stomach, pancreas and duodenum). The code will be publicly available.
Utilizing XAI technique to improve autoencoder based model for computer network anomaly detection with shapley additive explanation(SHAP)
Authors: Khushnaseeb Roshan, Aasim Zafar
Date: 2021-12-14 00:00:00
Tasks: Anomaly Detection, Explainable artificial intelligence, Explainable Artificial Intelligence (XAI), feature selection, Fraud Detection, Intrusion Detection

Machine learning (ML) and Deep Learning (DL) methods are being adopted rapidly, especially in computer network security, such as fraud detection, network anomaly detection, intrusion detection, and much more. However, the lack of transparency of ML and DL based models is a major obstacle to their implementation and criticized due to its black-box nature, even with such tremendous results. Explainable Artificial Intelligence (XAI) is a promising area that can improve the trustworthiness of these models by giving explanations and interpreting its output. If the internal working of the ML and DL based models is understandable, then it can further help to improve its performance. The objective of this paper is to show that how XAI can be used to interpret the results of the DL model, the autoencoder in this case. And, based on the interpretation, we improved its performance for computer network anomaly detection. The kernel SHAP method, which is based on the shapley values, is used as a novel feature selection technique. This method is used to identify only those features that are actually causing the anomalous behaviour of the set of attack/anomaly instances. Later, these feature sets are used to train and validate the autoencoder but on benign data only. Finally, the built SHAP_Model outperformed the other two models proposed based on the feature selection method. This whole experiment is conducted on the subset of the latest CICIDS2017 network dataset. The overall accuracy and AUC of SHAP_Model is 94% and 0.969, respectively.
Subspace modeling for fast and high-sensitivity X-ray chemical imaging
Authors: Jizhou Li, Bin Chen, Guibin Zan, Guannan Qian, Piero Pianetta, Yijin Liu
Date: 2022-01-01 00:00:00
Tasks: Denoising, Sensitivity, Vocal Bursts Intensity Prediction

Resolving morphological chemical phase transformations at the nanoscale is of vital importance to many scientific and industrial applications across various disciplines. The TXM-XANES imaging technique, by combining full field transmission X-ray microscopy (TXM) and X-ray absorption near edge structure (XANES), has been an emerging tool which operates by acquiring a series of microscopy images with multi-energy X-rays and fitting to obtain the chemical map. Its capability, however, is limited by the poor signal-to-noise ratios due to the system errors and low exposure illuminations for fast acquisition. In this work, by exploiting the intrinsic properties and subspace modeling of the TXM-XANES imaging data, we introduce a simple and robust denoising approach to improve the image quality, which enables fast and high-sensitivity chemical imaging. Extensive experiments on both synthetic and real datasets demonstrate the superior performance of the proposed method.
SporeAgent: Reinforced Scene-level Plausibility for Object Pose Refinement
Authors: Dominik Bauer, Timothy Patten, Markus Vincze
Date: 2022-01-01 00:00:00
Tasks: Object

Observational noise, inaccurate segmentation and ambiguity due to symmetry and occlusion lead to inaccurate object pose estimates. While depth- and RGB-based pose refinement approaches increase the accuracy of the resulting pose estimates, they are susceptible to ambiguity in the observation as they consider visual alignment. We propose to leverage the fact that we often observe static, rigid scenes. Thus, the objects therein need to be under physically plausible poses. We show that considering plausibility reduces ambiguity and, in consequence, allows poses to be more accurately predicted in cluttered environments. To this end, we extend a recent RL-based registration approach towards iterative refinement of object poses. Experiments on the LINEMOD and YCB-VIDEO datasets demonstrate the state-of-the-art performance of our depth-based refinement approach.
Deep Learning Applications for Lung Cancer Diagnosis: A systematic review
Authors: Hesamoddin Hosseini, Reza Monsefi, Shabnam Shadroo
Date: 2022-01-01 00:00:00
Tasks: Articles, Deep Learning, Lung Cancer Diagnosis

Lung cancer has been one of the most prevalent disease in recent years. According to the research of this field, more than 200,000 cases are identified each year in the US. Uncontrolled multiplication and growth of the lung cells result in malignant tumour formation. Recently, deep learning algorithms, especially Convolutional Neural Networks (CNN), have become a superior way to automatically diagnose disease. The purpose of this article is to review different models that lead to different accuracy and sensitivity in the diagnosis of early-stage lung cancer and to help physicians and researchers in this field. The main purpose of this work is to identify the challenges that exist in lung cancer based on deep learning. The survey is systematically written that combines regular mapping and literature review to review 32 conference and journal articles in the field from 2016 to 2021. After analysing and reviewing the articles, the questions raised in the articles are being answered. This research is superior to other review articles in this field due to the complete review of relevant articles and systematic write up.
Turath-150K: Image Database of Arab Heritage
Authors: Dani Kiyasseh, Rasheed el-Bouri
Date: 2022-01-01 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, image-classification, Image Classification

Large-scale image databases remain largely biased towards objects and activities encountered in a select few cultures. This absence of culturally-diverse images, which we refer to as the hidden tail, limits the applicability of pre-trained neural networks and inadvertently excludes researchers from under-represented regions. To begin remedying this issue, we curate Turath-150K, a database of images of the Arab world that reflect objects, activities, and scenarios commonly found there. In the process, we introduce three benchmark databases, Turath Standard, Art, and UNESCO, specialised subsets of the Turath dataset. After demonstrating the limitations of existing networks pre-trained on ImageNet when deployed on such benchmarks, we train and evaluate several networks on the task of image classification. As a consequence of Turath, we hope to engage machine learning researchers in under-represented regions, and to inspire the release of additional culture-focused databases. The database can be accessed here: danikiyasseh.github.io/Turath.
Generation of Synthetic Rat Brain MRI scans with a 3D Enhanced Alpha-GAN
Authors: André Ferreira, Ricardo Magalhães, Sébastien Mériaux, Victor Alves
Date: 2021-12-27 00:00:00
Tasks: Data Augmentation, Ethics

Translational brain research using Magnetic Resonance Imaging (MRI) is becoming increasingly popular as animal models are an essential part of scientific studies and more ultra-high-field scanners are becoming available. Some disadvantages of MRI are the availability of MRI scanners and the time required for a full scanning session (it usually takes over 30 minutes). Privacy laws and the 3Rs ethics rule also make it difficult to create large datasets for training deep learning models. Generative Adversarial Networks (GANs) can perform data augmentation with higher quality than other techniques. In this work, the alpha-GAN architecture is used to test its ability to produce realistic 3D MRI scans of the rat brain. As far as the authors are aware, this is the first time that a GAN-based approach has been used for data augmentation in preclinical data. The generated scans are evaluated using various qualitative and quantitative metrics. A Turing test conducted by 4 experts has shown that the generated scans can trick almost any expert. The generated scans were also used to evaluate their impact on the performance of an existing deep learning model developed for segmenting the rat brain into white matter, grey matter and cerebrospinal fluid. The models were compared using the Dice score. The best results for whole brain and white matter segmentation were obtained when 174 real scans and 348 synthetic scans were used, with improvements of 0.0172 and 0.0129, respectively. Using 174 real scans and 87 synthetic scans resulted in improvements of 0.0038 and 0.0764 for grey matter and CSF segmentation, respectively. Thus, by using the proposed new normalisation layer and loss functions, it was possible to improve the realism of the generated rat MRI scans and it was shown that using the generated data improved the segmentation model more than using the conventional data augmentation.
Image Restoration using Feature-guidance
Authors: Maitreya Suin, Kuldeep Purohit, A. N. Rajagopalan
Date: 2022-01-01 00:00:00
Tasks: Image Restoration, Knowledge Distillation

Image restoration is the task of recovering a clean image from a degraded version. In most cases, the degradation is spatially varying, and it requires the restoration network to both localize and restore the affected regions. In this paper, we present a new approach suitable for handling the image-specific and spatially-varying nature of degradation in images affected by practically occurring artifacts such as blur, rain-streaks. We decompose the restoration task into two stages of degradation localization and degraded region-guided restoration, unlike existing methods which directly learn a mapping between the degraded and clean images. Our premise is to use the auxiliary task of degradation mask prediction to guide the restoration process. We demonstrate that the model trained for this auxiliary task contains vital region knowledge, which can be exploited to guide the restoration network's training using attentive knowledge distillation technique. Further, we propose mask-guided convolution and global context aggregation module that focuses solely on restoring the degraded regions. The proposed approach's effectiveness is demonstrated by achieving significant improvement over strong baselines.
Adaptive Image Inpainting
Authors: Maitreya Suin, Kuldeep Purohit, A. N. Rajagopalan
Date: 2022-01-01 00:00:00
Tasks: Image Inpainting

Image inpainting methods have shown significant improvements by using deep neural networks recently. However, many of these techniques often create distorted structures or blurry textures inconsistent with surrounding areas. The problem is rooted in the encoder layers' ineffectiveness in building a complete and faithful embedding of the missing regions. To address this problem, two-stage approaches deploy two separate networks for a coarse and fine estimate of the inpainted image. Some approaches utilize handcrafted features like edges or contours to guide the reconstruction process. These methods suffer from huge computational overheads owing to multiple generator networks, limited ability of handcrafted features, and sub-optimal utilization of the information present in the ground truth. Motivated by these observations, we propose a distillation based approach for inpainting, where we provide direct feature level supervision for the encoder layers in an adaptive manner. We deploy cross and self distillation techniques and discuss the need for a dedicated completion-block in encoder to achieve the distillation target. We conduct extensive evaluations on multiple datasets to validate our method.
Multi-view Subspace Adaptive Learning via Autoencoder and Attention
Authors: Jian-wei Liu, Hao-jie Xie, Run-kun Lu, Xiong-lin Luo
Date: 2022-01-01 00:00:00
Tasks: Clustering, MULTI-VIEW LEARNING

Multi-view learning can cover all features of data samples more comprehensively, so multi-view learning has attracted widespread attention. Traditional subspace clustering methods, such as sparse subspace clustering (SSC) and low-ranking subspace clustering (LRSC), cluster the affinity matrix for a single view, thus ignoring the problem of fusion between views. In our article, we propose a new Multiview Subspace Adaptive Learning based on Attention and Autoencoder (MSALAA). This method combines a deep autoencoder and a method for aligning the self-representations of various views in Multi-view Low-Rank Sparse Subspace Clustering (MLRSSC), which can not only increase the capability to non-linearity fitting, but also can meets the principles of consistency and complementarity of multi-view learning. We empirically observe significant improvement over existing baseline methods on six real-life datasets.
Dynamic Scene Video Deblurring using Non-Local Attention
Authors: Maitreya Suin, A. N. Rajagopalan
Date: 2022-01-01 00:00:00
Tasks: Deblurring, Video Deblurring

This paper tackles the challenging problem of video deblurring. Most of the existing works depend on implicit or explicit alignment for temporal information fusion which either increase the computational cost or result in suboptimal performance due to wrong alignment. In this study, we propose a factorized spatio-temporal attention to perform non-local operations across space and time to fully utilize the available information without depending on alignment. It shows superior performance compared to existing fusion techniques while being much efficient. Extensive experiments on multiple datasets demonstrate the superiority of our method.
Self-attention Multi-view Representation Learning with Diversity-promoting Complementarity
Authors: Jian-wei Liu, Xi-hao Ding, Run-kun Lu, Xionglin Luo
Date: 2022-01-01 00:00:00
Tasks: Diversity, MULTI-VIEW LEARNING, Representation Learning

Multi-view learning attempts to generate a model with a better performance by exploiting the consensus and/or complementarity among multi-view data. However, in terms of complementarity, most existing approaches only can find representations with single complementarity rather than complementary information with diversity. In this paper, to utilize both complementarity and consistency simultaneously, give free rein to the potential of deep learning in grasping diversity-promoting complementarity for multi-view representation learning, we propose a novel supervised multi-view representation learning algorithm, called Self-Attention Multi-View network with Diversity-Promoting Complementarity (SAMVDPC), which exploits the consistency by a group of encoders, uses self-attention to find complementary information entailing diversity. Extensive experiments conducted on eight real-world datasets have demonstrated the effectiveness of our proposed method, and show its superiority over several baseline methods, which only consider single complementary information.