pwc-archive/papers-with-abstracts huggingface dataset:
UniToBrain dataset: a Brain Perfusion Dataset
Authors: Daniele Perlo, Enzo Tartaglione, Umberto Gava, Federico D'Agata, Edwin Benninck, Mauro Bergui
Date: 2022-08-01 00:00:00

The CT perfusion (CTP) is a medical exam for measuring the passage of a bolus of contrast solution through the brain on a pixel-by-pixel basis. The objective is to draw "perfusion maps" (namely cerebral blood volume, cerebral blood flow and time to peak) very rapidly for ischemic lesions, and to be able to distinguish between core and penumubra regions. A precise and quick diagnosis, in a context of ischemic stroke, can determine the fate of the brain tissues and guide the intervention and treatment in emergency conditions. In this work we present UniToBrain dataset, the very first open-source dataset for CTP. It comprises a cohort of more than a hundred of patients, and it is accompanied by patients metadata and ground truth maps obtained with state-of-the-art algorithms. We also propose a novel neural networks-based algorithm, using the European library ECVL and EDDL for the image processing and developing deep learning models respectively. The results obtained by the neural network models match the ground truth and open the road towards potential sub-sampling of the required number of CT maps, which impose heavy radiation doses to the patients.
Lung nodules segmentation from CT with DeepHealth toolkit
Authors: Hafiza Ayesha Hoor Chaudhry, Riccardo Renzulli, Daniele Perlo, Francesca Santinelli, Stefano Tibaldi, Carmen Cristiano, Marco Grosso, Attilio Fiandrotti, Maurizio Lucenteforte, Davide Cavagnino
Date: 2022-08-01 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

The accurate and consistent border segmentation plays an important role in the tumor volume estimation and its treatment in the field of Medical Image Segmentation. Globally, Lung cancer is one of the leading causes of death and the early detection of lung nodules is essential for the early cancer diagnosis and survival rate of patients. The goal of this study was to demonstrate the feasibility of Deephealth toolkit including PyECVL and PyEDDL libraries to precisely segment lung nodules. Experiments for lung nodules segmentation has been carried out on UniToChest using PyECVL and PyEDDL, for data pre-processing as well as neural network training. The results depict accurate segmentation of lung nodules across a wide diameter range and better accuracy over a traditional detection approach. The datasets and the code used in this paper are publicly available as a baseline reference.
Dress Well via Fashion Cognitive Learning
Authors: Kaicheng Pang, Xingxing Zou, Waikeung Wong
Date: 2022-08-01 00:00:00
Tasks: Graph Neural Network

Fashion compatibility models enable online retailers to easily obtain a large number of outfit compositions with good quality. However, effective fashion recommendation demands precise service for each customer with a deeper cognition of fashion. In this paper, we conduct the first study on fashion cognitive learning, which is fashion recommendations conditioned on personal physical information. To this end, we propose a Fashion Cognitive Network (FCN) to learn the relationships among visual-semantic embedding of outfit composition and appearance features of individuals. FCN contains two submodules, namely outfit encoder and Multi-label Graph Neural Network (ML-GCN). The outfit encoder uses a convolutional layer to encode an outfit into an outfit embedding. The latter module learns label classifiers via stacked GCN. We conducted extensive experiments on the newly collected O4U dataset, and the results provide strong qualitative and quantitative evidence that our framework outperforms alternative methods.
Multi-spectral Vehicle Re-identification with Cross-directional Consistency Network and a High-quality Benchmark
Authors: Aihua Zheng, Xianpeng Zhu, Zhiqi Ma, Chenglong Li, Jin Tang, Jixin Ma
Date: 2022-08-01 00:00:00
Tasks: Vehicle Re-Identification

To tackle the challenge of vehicle re-identification (Re-ID) in complex lighting environments and diverse scenes, multi-spectral sources like visible and infrared information are taken into consideration due to their excellent complementary advantages. However, multi-spectral vehicle Re-ID suffers cross-modality discrepancy caused by heterogeneous properties of different modalities as well as a big challenge of the diverse appearance with different views in each identity. Meanwhile, diverse environmental interference leads to heavy sample distributional discrepancy in each modality. In this work, we propose a novel cross-directional consistency network to simultaneously overcome the discrepancies from both modality and sample aspects. In particular, we design a new cross-directional center loss to pull the modality centers of each identity close to mitigate cross-modality discrepancy, while the sample centers of each identity close to alleviate the sample discrepancy. Such strategy can generate discriminative multi-spectral feature representations for vehicle Re-ID. In addition, we design an adaptive layer normalization unit to dynamically adjust individual feature distribution to handle distributional discrepancy of intra-modality features for robust learning. To provide a comprehensive evaluation platform, we create a high-quality RGB-NIR-TIR multi-spectral vehicle Re-ID benchmark (MSVR310), including 310 different vehicles from a broad range of viewpoints, time spans and environmental complexities. Comprehensive experiments on both created and public datasets demonstrate the effectiveness of the proposed approach comparing to the state-of-the-art methods.
XOOD: Extreme Value Based Out-Of-Distribution Detection For Image Classification
Authors: Frej Berglind, Haron Temam, Supratik Mukhopadhyay, Kamalika Das, Md Saiful Islam Sajol, Sricharan Kumar, Kumar Kallurupalli
Date: 2022-08-01 00:00:00
Tasks: image-classification, Image Classification, Out-of-Distribution Detection, Out of Distribution (OOD) Detection

Detecting out-of-distribution (OOD) data at inference time is crucial for many applications of machine learning. We present XOOD: a novel extreme value-based OOD detection framework for image classification that consists of two algorithms. The first, XOOD-M, is completely unsupervised, while the second XOOD-L is self-supervised. Both algorithms rely on the signals captured by the extreme values of the data in the activation layers of the neural network in order to distinguish between in-distribution and OOD instances. We show experimentally that both XOOD-M and XOOD-L outperform state-of-the-art OOD detection methods on many benchmark data sets in both efficiency and accuracy, reducing false-positive rate (FPR95) by 50%, while improving the inferencing time by an order of magnitude.
A Rotation Meanout Network with Invariance for Dermoscopy Image Classification and Retrieval
Authors: Yilan Zhang, Fengying Xie, Xuedong Song, Hangning Zhou, Yiguang Yang, Haopeng Zhang, Jie Liu
Date: 2022-08-01 00:00:00
Tasks: image-classification, Image Classification, Retrieval

The computer-aided diagnosis (CAD) system can provide a reference basis for the clinical diagnosis of skin diseases. Convolutional neural networks (CNNs) can not only extract visual elements such as colors and shapes but also semantic features. As such they have made great improvements in many tasks of dermoscopy images. The imaging of dermoscopy has no principal orientation, indicating that there are a large number of skin lesion rotations in the datasets. However, CNNs lack rotation invariance, which is bound to affect the robustness of CNNs against rotations. To tackle this issue, we propose a rotation meanout (RM) network to extract rotation-invariant features from dermoscopy images. In RM, each set of rotated feature maps corresponds to a set of outputs of the weight-sharing convolutions and they are fused using meanout strategy to obtain the final feature maps. Through theoretical derivation, the proposed RM network is rotation-equivariant and can extract rotation-invariant features when followed by the global average pooling (GAP) operation. The extracted rotation-invariant features can better represent the original data in classification and retrieval tasks for dermoscopy images. The RM is a general operation, which does not change the network structure or increase any parameter, and can be flexibly embedded in any part of CNNs. Extensive experiments are conducted on a dermoscopy image dataset. The results show our method outperforms other anti-rotation methods and achieves great improvements in dermoscopy image classification and retrieval tasks, indicating the potential of rotation invariance in the field of dermoscopy images.
Quality Evaluation of Arbitrary Style Transfer: Subjective Study and Objective Metric
Authors: Hangwei Chen, Feng Shao, Xiongli Chai, Yuese Gu, Qiuping Jiang, Xiangchao Meng, Yo-Sung Ho
Date: 2022-08-01 00:00:00
Tasks: Style Transfer

Arbitrary neural style transfer is a vital topic with great research value and wide industrial application, which strives to render the structure of one image using the style of another. Recent researches have devoted great efforts on the task of arbitrary style transfer (AST) for improving the stylization quality. However, there are very few explorations about the quality evaluation of AST images, even it can potentially guide the design of different algorithms. In this paper, we first construct a new AST images quality assessment database (AST-IQAD), which consists 150 content-style image pairs and the corresponding 1200 stylized images produced by eight typical AST algorithms. Then, a subjective study is conducted on our AST-IQAD database, which obtains the subjective rating scores of all stylized images on the three subjective evaluations, i.e., content preservation (CP), style resemblance (SR), and overall vision (OV). To quantitatively measure the quality of AST image, we propose a new sparse representation-based method, which computes the quality according to the sparse feature similarity. Experimental results on our AST-IQAD have demonstrated the superiority of the proposed method. The dataset and source code will be released at https://github.com/Hangwei-Chen/AST-IQAD-SRQE
Software Package for Automated Analysis of Lung Ultrasound Videos
Authors: Anito Anto, Linda Rose Jimson, Tanya Rose, Mohammed Jafrin, Mahesh Raveendranatha Panicker
Date: 2022-08-01 00:00:00
Tasks: Diagnostic

In the recent past with the rapid surge of COVID-19 infections, lung ultrasound has emerged as a fast and powerful diagnostic tool particularly for continuous and periodic monitoring of the lung. There have been many attempts towards severity classification, segmentation and detection of key landmarks in the lung. Leveraging the progress, an automated lung ultrasound video analysis package is presented in this work, which can provide summary of key frames in the video, flagging of the key frames with lung infection and options to automatically detect and segment the lung landmarks. The integrated package is implemented as an open-source web application and available in the link https://github.com/anitoanto/alus-package.
Improving Fine-Grained Visual Recognition in Low Data Regimes via Self-Boosting Attention Mechanism
Authors: Yangyang Shu, Baosheng Yu, HaiMing Xu, Lingqiao Liu
Date: 2022-08-01 00:00:00
Tasks: Fine-Grained Visual Recognition

The challenge of fine-grained visual recognition often lies in discovering the key discriminative regions. While such regions can be automatically identified from a large-scale labeled dataset, a similar method might become less effective when only a few annotations are available. In low data regimes, a network often struggles to choose the correct regions for recognition and tends to overfit spurious correlated patterns from the training data. To tackle this issue, this paper proposes the self-boosting attention mechanism, a novel method for regularizing the network to focus on the key regions shared across samples and classes. Specifically, the proposed method first generates an attention map for each training image, highlighting the discriminative part for identifying the ground-truth object category. Then the generated attention maps are used as pseudo-annotations. The network is enforced to fit them as an auxiliary task. We call this approach the self-boosting attention mechanism (SAM). We also develop a variant by using SAM to create multiple attention maps to pool convolutional maps in a style of bilinear pooling, dubbed SAM-Bilinear. Through extensive experimental studies, we show that both methods can significantly improve fine-grained visual recognition performance on low data regimes and can be incorporated into existing network architectures. The source code is publicly available at: https://github.com/GANPerf/SAM
Accurate Polygonal Mapping of Buildings in Satellite Imagery
Authors: Bowen Xu, Jiakun Xu, Nan Xue, Gui-Song Xia
Date: 2022-08-01 00:00:00

This paper studies the problem of polygonal mapping of buildings by tackling the issue of mask reversibility that leads to a notable performance gap between the predicted masks and polygons from the learning-based methods. We addressed such an issue by exploiting the hierarchical supervision (of bottom-level vertices, mid-level line segments and the high-level regional masks) and proposed a novel interaction mechanism of feature embedding sourced from different levels of supervision signals to obtain reversible building masks for polygonal mapping of buildings. As a result, we show that the learned reversible building masks take all the merits of the advances of deep convolutional neural networks for high-performing polygonal mapping of buildings. In the experiments, we evaluated our method on the two public benchmarks of AICrowd and Inria. On the AICrowd dataset, our proposed method obtains unanimous improvements on the metrics of AP, APboundary and PoLiS. For the Inria dataset, our proposed method also obtains very competitive results on the metrics of IoU and Accuracy. The models and source code are available at https://github.com/SarahwXU.
Breast Cancer Classification Based on Histopathological Images Using a Deep Learning Capsule Network
Authors: Hayder A. Khikani, Naira Elazab, Ahmed Elgarayhi, Mohammed Elmogy, Mohammed Sallah
Date: 2022-08-01 00:00:00
Tasks: Cancer Classification, Prognosis

Breast cancer is one of the most serious types of cancer that can occur in women. The automatic diagnosis of breast cancer by analyzing histological images (HIs) is important for patients and their prognosis. The classification of HIs provides clinicians with an accurate understanding of diseases and allows them to treat patients more efficiently. Deep learning (DL) approaches have been successfully employed in a variety of fields, particularly medical imaging, due to their capacity to extract features automatically. This study aims to classify different types of breast cancer using HIs. In this research, we present an enhanced capsule network that extracts multi-scale features using the Res2Net block and four additional convolutional layers. Furthermore, the proposed method has fewer parameters due to using small convolutional kernels and the Res2Net block. As a result, the new method outperforms the old ones since it automatically learns the best possible features. The testing results show that the model outperformed the previous DL methods.
An Enhanced Deep Learning Technique for Prostate Cancer Identification Based on MRI Scans
Authors: Hussein Hashem, Yasmin Alsakar, Ahmed Elgarayhi, Mohammed Elmogy, Mohammed Sallah
Date: 2022-08-01 00:00:00
Tasks: Deep Learning

Prostate cancer is the most dangerous cancer diagnosed in men worldwide. Prostate diagnosis has been affected by many factors, such as lesion complexity, observer visibility, and variability. Many techniques based on Magnetic Resonance Imaging (MRI) have been used for prostate cancer identification and classification in the last few decades. Developing these techniques is crucial and has a great medical effect because they improve the treatment benefits and the chance of patients' survival. A new technique that depends on MRI has been proposed to improve the diagnosis. This technique consists of two stages. First, the MRI images have been preprocessed to make the medical image more suitable for the detection step. Second, prostate cancer identification has been performed based on a pre-trained deep learning model, InceptionResNetV2, that has many advantages and achieves effective results. In this paper, the InceptionResNetV2 deep learning model used for this purpose has average accuracy equals to 89.20%, and the area under the curve (AUC) equals to 93.6%. The experimental results of this proposed new deep learning technique represent promising and effective results compared to other previous techniques.
CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation
Authors: Zhihao LI, Jianzhuang Liu, Zhensong Zhang, Songcen Xu, Youliang Yan
Date: 2022-08-01 00:00:00
Tasks: 3D human pose and shape estimation, 3D Human Pose Estimation, Human Detection, Human Mesh Recovery, Unsupervised 3D Human Pose Estimation

Top-down methods dominate the field of 3D human pose and shape estimation, because they are decoupled from human detection and allow researchers to focus on the core problem. However, cropping, their first step, discards the location information from the very beginning, which makes themselves unable to accurately predict the global rotation in the original camera coordinate system. To address this problem, we propose to Carry Location Information in Full Frames (CLIFF) into this task. Specifically, we feed more holistic features to CLIFF by concatenating the cropped-image feature with its bounding box information. We calculate the 2D reprojection loss with a broader view of the full frame, taking a projection process similar to that of the person projected in the image. Fed and supervised by global-location-aware information, CLIFF directly predicts the global rotation along with more accurate articulated poses. Besides, we propose a pseudo-ground-truth annotator based on CLIFF, which provides high-quality 3D annotations for in-the-wild 2D datasets and offers crucial full supervision for regression-based methods. Extensive experiments on popular benchmarks show that CLIFF outperforms prior arts by a significant margin, and reaches the first place on the AGORA leaderboard (the SMPL-Algorithms track). The code and data are available at https://github.com/huawei-noah/noah-research/tree/master/CLIFF.
AvatarGen: a 3D Generative Model for Animatable Human Avatars
Authors: Jianfeng Zhang, Zihang Jiang, Dingdong Yang, Hongyi Xu, Yichun Shi, Guoxian Song, Zhongcong Xu, Xinchao Wang, Jiashi Feng
Date: 2022-08-01 00:00:00
Tasks: 3D Human Reconstruction

Unsupervised generation of clothed virtual humans with various appearance and animatable poses is important for creating 3D human avatars and other AR/VR applications. Existing methods are either limited to rigid object modeling, or not generative and thus unable to synthesize high-quality virtual humans and animate them. In this work, we propose AvatarGen, the first method that enables not only non-rigid human generation with diverse appearance but also full control over poses and viewpoints, while only requiring 2D images for training. Specifically, it extends the recent 3D GANs to clothed human generation by utilizing a coarse human body model as a proxy to warp the observation space into a standard avatar under a canonical space. To model non-rigid dynamics, it introduces a deformation network to learn pose-dependent deformations in the canonical space. To improve geometry quality of the generated human avatars, it leverages signed distance field as geometric representation, which allows more direct regularization from the body model on the geometry learning. Benefiting from these designs, our method can generate animatable human avatars with high-quality appearance and geometry modeling, significantly outperforming previous 3D GANs. Furthermore, it is competent for many applications, e.g., single-view reconstruction, reanimation, and text-guided synthesis. Code and pre-trained model will be available.
Analysis of Semi-Supervised Methods for Facial Expression Recognition
Authors: Shuvendu Roy, Ali Etemad
Date: 2022-07-31 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER), Pseudo Label

Training deep neural networks for image recognition often requires large-scale human annotated data. To reduce the reliance of deep neural solutions on labeled data, state-of-the-art semi-supervised methods have been proposed in the literature. Nonetheless, the use of such semi-supervised methods has been quite rare in the field of facial expression recognition (FER). In this paper, we present a comprehensive study on recently proposed state-of-the-art semi-supervised learning methods in the context of FER. We conduct comparative study on eight semi-supervised learning methods, namely Pi-Model, Pseudo-label, Mean-Teacher, VAT, MixMatch, ReMixMatch, UDA, and FixMatch, on three FER datasets (FER13, RAF-DB, and AffectNet), when various amounts of labeled samples are used. We also compare the performance of these methods against fully-supervised training. Our study shows that when training existing semi-supervised methods on as little as 250 labeled samples per class can yield comparable performances to that of fully-supervised methods trained on the full labeled datasets. To facilitate further research in this area, we make our code publicly available at: https://github.com/ShuvenduRoy/SSL_FER
Is current research on adversarial robustness addressing the right problem?
Authors: Ali Borji
Date: 2022-07-31 00:00:00
Tasks: Adversarial Robustness

Short answer: Yes, Long answer: No! Indeed, research on adversarial robustness has led to invaluable insights helping us understand and explore different aspects of the problem. Many attacks and defenses have been proposed over the last couple of years. The problem, however, remains largely unsolved and poorly understood. Here, I argue that the current formulation of the problem serves short term goals, and needs to be revised for us to achieve bigger gains. Specifically, the bound on perturbation has created a somewhat contrived setting and needs to be relaxed. This has misled us to focus on model classes that are not expressive enough to begin with. Instead, inspired by human vision and the fact that we rely more on robust features such as shape, vertices, and foreground objects than non-robust features such as texture, efforts should be steered towards looking for significantly different classes of models. Maybe instead of narrowing down on imperceptible adversarial perturbations, we should attack a more general problem which is finding architectures that are simultaneously robust to perceptible perturbations, geometric transformations (e.g. rotation, scaling), image distortions (lighting, blur), and more (e.g. occlusion, shadow). Only then we may be able to solve the problem of adversarial vulnerability.