pwc-archive/papers-with-abstracts huggingface dataset:
Development of Diabetic Foot Ulcer Datasets: An Overview
Authors: Moi Hoon Yap, Connah Kendrick, Neil D. Reeves, Manu Goyal, Joseph M. Pappachan, Bill Cassidy
Date: 2022-01-01 00:00:00

This paper provides conceptual foundation and procedures used in the development of diabetic foot ulcer datasets over the past decade, with a timeline to demonstrate progress. We conduct a survey on data capturing methods for foot photographs, an overview of research in developing private and public datasets, the related computer vision tasks (detection, segmentation and classification), the diabetic foot ulcer challenges and the future direction of the development of the datasets. We report the distribution of dataset users by country and year. Our aim is to share the technical challenges that we encountered together with good practices in dataset development, and provide motivation for other researchers to participate in data sharing in this domain.
Adaptive Single Image Deblurring
Authors: Maitreya Suin, Kuldeep Purohit, A. N. Rajagopalan
Date: 2022-01-01 00:00:00
Tasks: Deblurring, Image Deblurring, Single Image Deblurring

This paper tackles the problem of dynamic scene deblurring. Although end-to-end fully convolutional designs have recently advanced the state-of-the-art in non-uniform motion deblurring, their performance-complexity trade-off is still sub-optimal. Existing approaches achieve a large receptive field by a simple increment in the number of generic convolution layers, kernel-size, which comes with the burden of the increase in model size and inference speed. In this work, we propose an efficient pixel adaptive and feature attentive design for handling large blur variations within and across different images. We also propose an effective content-aware global-local filtering module that significantly improves the performance by considering not only the global dependencies of the pixel but also dynamically using the neighboring pixels. We use a patch hierarchical attentive architecture composed of the above module that implicitly discover the spatial variations in the blur present in the input image and in turn perform local and global modulation of intermediate features. Extensive qualitative and quantitative comparisons with prior art on deblurring benchmarks demonstrate the superiority of the proposed network.
SAFL: A Self-Attention Scene Text Recognizer with Focal Loss
Authors: Bao Hieu Tran, Thanh Le-Cong, Huu Manh Nguyen, Duc Anh Le, Thanh Hung Nguyen, Phi Le Nguyen
Date: 2022-01-01 00:00:00
Tasks: Optical Character Recognition, Optical Character Recognition (OCR), Scene Text Recognition

In the last decades, scene text recognition has gained worldwide attention from both the academic community and actual users due to its importance in a wide range of applications. Despite achievements in optical character recognition, scene text recognition remains challenging due to inherent problems such as distortions or irregular layout. Most of the existing approaches mainly leverage recurrence or convolution-based neural networks. However, while recurrent neural networks (RNNs) usually suffer from slow training speed due to sequential computation and encounter problems as vanishing gradient or bottleneck, CNN endures a trade-off between complexity and performance. In this paper, we introduce SAFL, a self-attention-based neural network model with the focal loss for scene text recognition, to overcome the limitation of the existing approaches. The use of focal loss instead of negative log-likelihood helps the model focus more on low-frequency samples training. Moreover, to deal with the distortions and irregular texts, we exploit Spatial TransformerNetwork (STN) to rectify text before passing to the recognition network. We perform experiments to compare the performance of the proposed model with seven benchmarks. The numerical results show that our model achieves the best performance.
Machine learning approaches for localized lockdown during COVID-19: a case study analysis
Authors: Sara Malvar, Julio Romano Meneghini
Date: 2022-01-03 00:00:00
Tasks: BIG-bench Machine Learning

At the end of 2019, the latest novel coronavirus Sars-CoV-2 emerged as a significant acute respiratory disease that has become a global pandemic. Countries like Brazil have had difficulty in dealing with the virus due to the high socioeconomic difference of states and municipalities. Therefore, this study presents a new approach using different machine learning and deep learning algorithms applied to Brazilian COVID-19 data. First, a clustering algorithm is used to identify counties with similar sociodemographic behavior, while Benford's law is used to check for data manipulation. Based on these results we are able to correctly model SARIMA models based on the clusters to predict new daily cases. The unsupervised machine learning techniques optimized the process of defining the parameters of the SARIMA model. This framework can also be useful to propose confinement scenarios during the so-called second wave. We have used the 645 counties from S\~ao Paulo state, the most populous state in Brazil. However, this methodology can be used in other states or countries. This paper demonstrates how different techniques of machine learning, deep learning, data mining and statistics can be used together to produce important results when dealing with pandemic data. Although the findings cannot be used exclusively to assess and influence policy decisions, they offer an alternative to the ineffective measures that have been used.
SurfGen: Adversarial 3D Shape Synthesis with Explicit Surface Discriminators
Authors: Andrew Luo, Tianqin Li, Wen-Hao Zhang, Tai Sing Lee
Date: 2022-01-01 00:00:00
Proceeding: ICCV 2021 10

Recent advances in deep generative models have led to immense progress in 3D shape synthesis. While existing models are able to synthesize shapes represented as voxels, point-clouds, or implicit functions, these methods only indirectly enforce the plausibility of the final 3D shape surface. Here we present a 3D shape synthesis framework (SurfGen) that directly applies adversarial training to the object surface. Our approach uses a differentiable spherical projection layer to capture and represent the explicit zero isosurface of an implicit 3D generator as functions defined on the unit sphere. By processing the spherical representation of 3D object surfaces with a spherical CNN in an adversarial setting, our generator can better learn the statistics of natural shape surfaces. We evaluate our model on large-scale shape datasets, and demonstrate that the end-to-end trained model is capable of generating high fidelity 3D shapes with diverse topology.
PCACE: A Statistical Approach to Ranking Neurons for CNN Interpretability
Authors: Sílvia Casacuberta, Esra Suel, Seth Flaxman
Date: 2021-12-31 00:00:00
Tasks: Air Pollution Prediction

In this paper we introduce a new problem within the growing literature of interpretability for convolution neural networks (CNNs). While previous work has focused on the question of how to visually interpret CNNs, we ask what it is that we care to interpret, that is, which layers and neurons are worth our attention? Due to the vast size of modern deep learning network architectures, automated, quantitative methods are needed to rank the relative importance of neurons so as to provide an answer to this question. We present a new statistical method for ranking the hidden neurons in any convolutional layer of a network. We define importance as the maximal correlation between the activation maps and the class score. We provide different ways in which this method can be used for visualization purposes with MNIST and ImageNet, and show a real-world application of our method to air pollution prediction with street-level images.
Quality-aware Part Models for Occluded Person Re-identification
Authors: Pengfei Wang, Changxing Ding, Zhiyin Shao, Zhibin Hong, Shengli Zhang, DaCheng Tao
Date: 2022-01-01 00:00:00
Tasks: Computational Efficiency, Occluded Person Re-Identification, Person Re-Identification

Occlusion poses a major challenge for person re-identification (ReID). Existing approaches typically rely on outside tools to infer visible body parts, which may be suboptimal in terms of both computational efficiency and ReID accuracy. In particular, they may fail when facing complex occlusions, such as those between pedestrians. Accordingly, in this paper, we propose a novel method named Quality-aware Part Models (QPM) for occlusion-robust ReID. First, we propose to jointly learn part features and predict part quality scores. As no quality annotation is available, we introduce a strategy that automatically assigns low scores to occluded body parts, thereby weakening the impact of occluded body parts on ReID results. Second, based on the predicted part quality scores, we propose a novel identity-aware spatial attention (ISA) module. In this module, a coarse identity-aware feature is utilized to highlight pixels of the target pedestrian, so as to handle the occlusion between pedestrians. Third, we design an adaptive and efficient approach for generating global features from common non-occluded regions with respect to each image pair. This design is crucial, but is often ignored by existing methods. QPM has three key advantages: 1) it does not rely on any outside tools in either the training or inference stages; 2) it handles occlusions caused by both objects and other pedestrians;3) it is highly computationally efficient. Experimental results on four popular databases for occluded ReID demonstrate that QPM consistently outperforms state-of-the-art methods by significant margins. The code of QPM will be released.
Towards a Domain Ontology for the Analysis of Ancient Fabrics The SILKNOW Project and the Case of European Silk Heritage
Authors: Marie Puren, Pierre Vernus
Date: 2021-12-31 00:00:00

In this article, we present the SILKNOW project (Silk heritage in the Knowledge Society: from punched card to Big Data, Deep Learning and visual/tangible simulations) (2018-2021). This project aimed to use Semantic Web technologies to give greater visibility to silk objects produced and consumed in Europe between the 15th and 19th centuries. Silk is a particularly important material in European history, and it has produced some exceptional objects of great historical interest. However, it is a threatened heritage that is little known to the general public. We show the interest of using Semantic Web technologies to give more visibility to such a heritage, by describing the results we have obtained. We present the methodology used to develop a knowledge graph, and in particular the different steps that were necessary to create the underlying data model, based on the CIDOC CRM or CIDOC Conceptual Reference Model. We also propose a CIDOC CRM-compatible extension to express the complex semantics of the creation and production process of ancient silk fabrics.
Using Graph-Aware Reinforcement Learning to Identify Winning Strategies in Diplomacy Games (Student Abstract)
Authors: Hansin Ahuja, Lynnette Hui Xian Ng, Kokil Jaidka
Date: 2021-12-31 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This abstract proposes an approach towards goal-oriented modeling of the detection and modeling complex social phenomena in multiparty discourse in an online political strategy game. We developed a two-tier approach that first encodes sociolinguistic behavior as linguistic features then use reinforcement learning to estimate the advantage afforded to any player. In the first tier, sociolinguistic behavior, such as Friendship and Reasoning, that speakers use to influence others are encoded as linguistic features to identify the persuasive strategies applied by each player in simultaneous two-party dialogues. In the second tier, a reinforcement learning approach is used to estimate a graph-aware reward function to quantify the advantage afforded to each player based on their standing in this multiparty setup. We apply this technique to the game Diplomacy, using a dataset comprising of over 15,000 messages exchanged between 78 users. Our graph-aware approach shows robust performance compared to a context-agnostic setup.
Robust Region Feature Synthesizer for Zero-Shot Object Detection
Authors: Peiliang Huang, Junwei Han, De Cheng, Dingwen Zhang
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Generalized Zero-Shot Object Detection, Object, object-detection, Object Detection, Zero-Shot Object Detection

Zero-shot object detection aims at incorporating class semantic vectors to realize the detection of (both seen and) unseen classes given an unconstrained test image. In this study, we reveal the core challenges in this research area: how to synthesize robust region features (for unseen objects) that are as intra-class diverse and inter-class separable as the real samples, so that strong unseen object detectors can be trained upon them. To address these challenges, we build a novel zero-shot object detection framework that contains an Intra-class Semantic Diverging component and an Inter-class Structure Preserving component. The former is used to realize the one-to-more mapping to obtain diverse visual features from each class semantic vector, preventing miss-classifying the real unseen objects as image backgrounds. While the latter is used to avoid the synthesized features too scattered to mix up the inter-class and foreground-background relationship. To demonstrate the effectiveness of the proposed approach, comprehensive experiments on PASCAL VOC, COCO, and DIOR datasets are conducted. Notably, our approach achieves the new state-of-the-art performance on PASCAL VOC and COCO and it is the first study to carry out zero-shot object detection in remote sensing imagery.
Deep Learning Based 3D Point Cloud Regression for Estimating Forest Biomass
Authors: Stefan Oehmcke, Lei LI, Katerina Trepekli, Jaime Revenga, Thomas Nord-Larsen, Fabian Gieseke, Christian Igel
Date: 2021-12-21 00:00:00
Tasks: Management, regression

Quantification of forest biomass stocks and their dynamics is important for implementing effective climate change mitigation measures. The knowledge is needed, e.g., for local forest management, studying the processes driving af-, re-, and deforestation, and can improve the accuracy of carbon-accounting. Remote sensing using airborne LiDAR can be used to perform these measurements of vegetation structure at large scale. We present deep learning systems for predicting wood volume, above-ground biomass (AGB), and subsequently above-ground carbon stocks directly from airborne LiDAR point clouds. We devise different neural network architectures for point cloud regression and evaluate them on remote sensing data of areas for which AGB estimates have been obtained from field measurements in the Danish national forest inventory. Our adaptation of Minkowski convolutional neural networks for regression gave the best results. The deep neural networks produced significantly more accurate wood volume, AGB, and carbon stock estimates compared to state-of-the-art approaches operating on basic statistics of the point clouds. In contrast to other methods, the proposed deep learning approach does not require a digital terrain model. We expect this finding to have a strong impact on LiDAR-based analyses of biomass dynamics.
Big Data is not the New Oil: Common Misconceptions about Population Data
Authors: Peter Christen, Rainer Schnell
Date: 2021-12-20 00:00:00
Tasks: Decision Making, Misconceptions, valid

Databases covering all individuals of a population are increasingly used for research and decision-making. The massive size of such databases is often mistaken as a guarantee for valid inferences. However, population data have characteristics that make them challenging to use. Various assumptions on population coverage and data quality are commonly made, including how such data were captured and what types of processing have been applied to them. Furthermore, the full potential of population data can often only be unlocked when such data are linked to other databases. Record linkage often implies subtle technical problems, which are easily missed. We discuss a diverse range of misconceptions relevant for anybody capturing, processing, linking, or analysing population data. Remarkably many of these misconceptions are due to the social nature of data collections and are therefore missed by purely technical accounts of data processing. Many of these misconceptions are also not well documented in scientific publications. We conclude with a set of recommendations for using population data.
Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB Images
Authors: Xiaoqiang Wang, Lei Zhu, Siliang Tang, Huazhu Fu, Ping Li, Fei Wu, Yi Yang, Yueting Zhuang
Date: 2022-01-01 00:00:00
Tasks: Depth Estimation, object-detection, Object Detection, RGB-D Salient Object Detection, Saliency Detection, Salient Object Detection

Training deep models for RGB-D salient object detection (SOD) often requires a large number of labeled RGB-D images. However, RGB-D data is not easily acquired, which limits the development of RGB-D SOD techniques. To alleviate this issue, we present a Dual-Semi RGB-D Salient Object Detection Network (DS-Net) to leverage unlabeled RGB images for boosting RGB-D saliency detection. We first devise a depth decoupling convolutional neural network (DDCNN), which contains a depth estimation branch and a saliency detection branch. The depth estimation branch is trained with RGB-D images and then used to estimate the pseudo depth maps for all unlabeled RGB images to form the paired data. The saliency detection branch is used to fuse the RGB feature and depth feature to predict the RGB-D saliency. Then, the whole DDCNN is assigned as the backbone in a teacher-student framework for semi-supervised learning. Moreover, we also introduce a consistency loss on the intermediate attention and saliency maps for the unlabeled data, as well as a supervised depth and saliency loss for labeled data. Experimental results on seven widely-used benchmark datasets demonstrate that our DDCNN outperforms state-of-the-art methods both quantitatively and qualitatively. We also demonstrate that our semi-supervised DS-Net can further improve the performance, even when using an RGB image with the pseudo depth map.
Adversarial Attack via Dual-Stage Network Erosion
Authors: Yexin Duan, Junhua Zou, Xingyu Zhou, Wu Zhang, Jin Zhang, Zhisong Pan
Date: 2022-01-01 00:00:00
Tasks: Adversarial Attack

Deep neural networks are vulnerable to adversarial examples, which can fool deep models by adding subtle perturbations. Although existing attacks have achieved promising results, it still leaves a long way to go for generating transferable adversarial examples under the black-box setting. To this end, this paper proposes to improve the transferability of adversarial examples, and applies dual-stage feature-level perturbations to an existing model to implicitly create a set of diverse models. Then these models are fused by the longitudinal ensemble during the iterations. The proposed method is termed Dual-Stage Network Erosion (DSNE). We conduct comprehensive experiments both on non-residual and residual networks, and obtain more transferable adversarial examples with the computational cost similar to the state-of-the-art method. In particular, for the residual networks, the transferability of the adversarial examples can be significantly improved by biasing the residual block information to the skip connections. Our work provides new insights into the architectural vulnerability of neural networks and presents new challenges to the robustness of neural networks.
Interpretable Knowledge Tracing: Simple and Efficient Student Modeling with Causal Relations
Authors: Sein Minn, Jill-Jenn Vie, Koh Takeuchi, Hisashi Kashima, Feida Zhu
Date: 2021-12-15 00:00:00
Tasks: Deep Learning, Diagnostic, Knowledge Tracing, Prediction, Skill Mastery

Intelligent Tutoring Systems have become critically important in future learning environments. Knowledge Tracing (KT) is a crucial part of that system. It is about inferring the skill mastery of students and predicting their performance to adjust the curriculum accordingly. Deep Learning-based KT models have shown significant predictive performance compared with traditional models. However, it is difficult to extract psychologically meaningful explanations from the tens of thousands of parameters in neural networks, that would relate to cognitive theory. There are several ways to achieve high accuracy in student performance prediction but diagnostic and prognostic reasoning is more critical in learning sciences. Since KT problem has few observable features (problem ID and student's correctness at each practice), we extract meaningful latent features from students' response data by using machine learning and data mining techniques. In this work, we present Interpretable Knowledge Tracing (IKT), a simple model that relies on three meaningful latent features: individual skill mastery, ability profile (learning transfer across skills), and problem difficulty. IKT's prediction of future student performance is made using a Tree-Augmented Naive Bayes Classifier (TAN), therefore its predictions are easier to explain than deep learning-based student models. IKT also shows better student performance prediction than deep learning-based student models without requiring a huge amount of parameters. We conduct ablation studies on each feature to examine their contribution to student performance prediction. Thus, IKT has great potential for providing adaptive and personalized instructions with causal reasoning in real-world educational systems.
SalyPath360: Saliency and Scanpath Prediction Framework for Omnidirectional Images
Authors: Mohamed Amine Kerkouri, Marouane Tliba, Aladine Chetouani, Mohamed Sayeh
Date: 2022-01-01 00:00:00
Tasks: Decoder, Prediction, Scanpath prediction

This paper introduces a new framework to predict visual attention of omnidirectional images. The key setup of our architecture is the simultaneous prediction of the saliency map and a corresponding scanpath for a given stimulus. The framework implements a fully encoder-decoder convolutional neural network augmented by an attention module to generate representative saliency maps. In addition, an auxiliary network is employed to generate probable viewport center fixation points through the SoftArgMax function. The latter allows to derive fixation points from feature maps. To take advantage of the scanpath prediction, an adaptive joint probability distribution model is then applied to construct the final unbiased saliency map by leveraging the encoder decoder-based saliency map and the scanpath-based saliency heatmap. The proposed framework was evaluated in terms of saliency and scanpath prediction, and the results were compared to state-of-the-art methods on Salient360! dataset. The results showed the relevance of our framework and the benefits of such architecture for further omnidirectional visual attention prediction tasks.
Performance Comparison of Deep Learning Architectures for Artifact Removal in Gastrointestinal Endoscopic Imaging
Authors: Taira Watanabe, Kensuke Tanioka, Satoru Hiwa, Tomoyuki Hiroyasu
Date: 2022-01-01 00:00:00

Endoscopic images typically contain several artifacts. The artifacts significantly impact image analysis result in computer-aided diagnosis. Convolutional neural networks (CNNs), a type of deep learning, can removes such artifacts. Various architectures have been proposed for the CNNs, and the accuracy of artifact removal varies depending on the choice of architecture. Therefore, it is necessary to determine the artifact removal accuracy, depending on the selected architecture. In this study, we focus on endoscopic surgical instruments as artifacts, and determine and discuss the artifact removal accuracy using seven different CNN architectures.