pwc-archive/papers-with-abstracts huggingface dataset:
Hybrid Skip: A Biologically Inspired Skip Connection for the UNet Architecture
Authors: Nikolaos Zioulis, Georgios Albanis, Petros Drakoulis, Federico Alvarez, Dimitrios Zarpalas, Petros Daras
Date: 2022-07-11 00:00:00
Tasks: Decoder, Depth Estimation

In this work we introduce a biologically inspired long-range skip connection for the UNet architecture that relies on the perceptual illusion of hybrid images, being images that simultaneously encode two images. The fusion of early encoder features with deeper decoder ones allows UNet models to produce finer-grained dense predictions. While proven in segmentation tasks, the network's benefits are down-weighted for dense regression tasks as these long-range skip connections additionally result in texture transfer artifacts. Specifically for depth estimation, this hurts smoothness and introduces false positive edges which are detrimental to the task due to the depth maps' piece-wise smooth nature. The proposed HybridSkip connections show improved performance in balancing the trade-off between edge preservation, and the minimization of texture transfer artifacts that hurt smoothness. This is achieved by the proper and balanced exchange of information that Hybrid-Skip connections offer between the high and low frequency, encoder and decoder features, respectively.
Automating Detection of Papilledema in Pediatric Fundus Images with Explainable Machine Learning
Authors: Kleanthis Avramidis, Mohammad Rostami, Melinda Chang, Shrikanth Narayanan
Date: 2022-07-10 00:00:00
Tasks: BIG-bench Machine Learning, Data Augmentation, Deep Learning, Diagnostic

Papilledema is an ophthalmic neurologic disorder in which increased intracranial pressure leads to swelling of the optic nerves. Undiagnosed papilledema in children may lead to blindness and may be a sign of life-threatening conditions, such as brain tumors. Robust and accurate clinical diagnosis of this syndrome can be facilitated by automated analysis of fundus images using deep learning, especially in the presence of challenges posed by pseudopapilledema that has similar fundus appearance but distinct clinical implications. We present a deep learning-based algorithm for the automatic detection of pediatric papilledema. Our approach is based on optic disc localization and detection of explainable papilledema indicators through data augmentation. Experiments on real-world clinical data demonstrate that our proposed method is effective with a diagnostic accuracy comparable to expert ophthalmologists.
Segmentation of Blood Vessels, Optic Disc Localization, Detection of Exudates and Diabetic Retinopathy Diagnosis from Digital Fundus Images
Authors: Soham Basu, Sayantan Mukherjee, Ankit Bhattacharya, Anindya Sen
Date: 2022-07-09 00:00:00
Tasks: Clustering, Contour Detection, Diabetic Retinopathy Detection, Retinal Vessel Segmentation, Segmentation, Template Matching

Diabetic Retinopathy (DR) is a complication of long-standing, unchecked diabetes and one of the leading causes of blindness in the world. This paper focuses on improved and robust methods to extract some of the features of DR, viz. Blood Vessels and Exudates. Blood vessels are segmented using multiple morphological and thresholding operations. For the segmentation of exudates, k-means clustering and contour detection on the original images are used. Extensive noise reduction is performed to remove false positives from the vessel segmentation algorithm's results. The localization of Optic Disc using k-means clustering and template matching is also performed. Lastly, this paper presents a Deep Convolutional Neural Network (DCNN) model with 14 Convolutional Layers and 2 Fully Connected Layers, for the automatic, binary diagnosis of DR. The vessel segmentation, optic disc localization and DCNN achieve accuracies of 95.93%, 98.77% and 75.73% respectively. The source code and pre-trained model are available https://github.com/Sohambasu07/DR_2021
Unsupervised Joint Image Transfer and Uncertainty Quantification Using Patch Invariant Networks
Authors: Christoph Angermann, Markus Haltmeier, Ahsan Raza Siyal
Date: 2022-07-09 00:00:00
Tasks: Data Augmentation, Uncertainty Quantification

Unsupervised image transfer enables intra- and inter-modality image translation in applications where a large amount of paired training data is not abundant. To ensure a structure-preserving mapping from the input to the target domain, existing methods for unpaired image transfer are commonly based on cycle-consistency, causing additional computational resources and instability due to the learning of an inverse mapping. This paper presents a novel method for uni-directional domain mapping that does not rely on any paired training data. A proper transfer is achieved by using a GAN architecture and a novel generator loss based on patch invariance. To be more specific, the generator outputs are evaluated and compared at different scales, also leading to an increased focus on high-frequency details as well as an implicit data augmentation. This novel patch loss also offers the possibility to accurately predict aleatoric uncertainty by modeling an input-dependent scale map for the patch residuals. The proposed method is comprehensively evaluated on three well-established medical databases. As compared to four state-of-the-art methods, we observe significantly higher accuracy on these datasets, indicating great potential of the proposed method for unpaired image transfer with uncertainty taken into account. Implementation of the proposed framework is released here: \url{https://github.com/anger-man/unsupervised-image-transfer-and-uq}.
Video Coding Using Learned Latent GAN Compression
Authors: Mustafa Shukor, Bharath Bhushan Damodaran, Xu Yao, Pierre Hellier
Date: 2022-07-09 00:00:00
Tasks: Video Compression

We propose in this paper a new paradigm for facial video compression. We leverage the generative capacity of GANs such as StyleGAN to represent and compress a video, including intra and inter compression. Each frame is inverted in the latent space of StyleGAN, from which the optimal compression is learned. To do so, a diffeomorphic latent representation is learned using a normalizing flows model, where an entropy model can be optimized for image coding. In addition, we propose a new perceptual loss that is more efficient than other counterparts. Finally, an entropy model for video inter coding with residual is also learned in the previously constructed latent representation. Our method (SGANC) is simple, faster to train, and achieves better results for image and video coding compared to state-of-the-art codecs such as VTM, AV1, and recent deep learning techniques. In particular, it drastically minimizes perceptual distortion at low bit rates.
Rank-Enhanced Low-Dimensional Convolution Set for Hyperspectral Image Denoising
Authors: Jinhui Hou, Zhiyu Zhu, Hui Liu, Junhui Hou
Date: 2022-07-09 00:00:00
Tasks: Denoising, Hyperspectral Image Denoising, Image Denoising

This paper tackles the challenging problem of hyperspectral (HS) image denoising. Unlike existing deep learning-based methods usually adopting complicated network architectures or empirically stacking off-the-shelf modules to pursue performance improvement, we focus on the efficient and effective feature extraction manner for capturing the high-dimensional characteristics of HS images. To be specific, based on the theoretical analysis that increasing the rank of the matrix formed by the unfolded convolutional kernels can promote feature diversity, we propose rank-enhanced low-dimensional convolution set (Re-ConvSet), which separately performs 1-D convolution along the three dimensions of an HS image side-by-side, and then aggregates the resulting spatial-spectral embeddings via a learnable compression layer. Re-ConvSet not only learns the diverse spatial-spectral features of HS images, but also reduces the parameters and complexity of the network. We then incorporate Re-ConvSet into the widely-used U-Net architecture to construct an HS image denoising method. Surprisingly, we observe such a concise framework outperforms the most recent method to a large extent in terms of quantitative metrics, visual results, and efficiency. We believe our work may shed light on deep learning-based HS image processing and analysis.
Multi-Attribute Attention Network for Interpretable Diagnosis of Thyroid Nodules in Ultrasound Images
Authors: Van T. Manh, Jianqiao Zhou, Xiaohong Jia, Zehui Lin, Wenwen Xu, Zihan Mei, Yijie Dong, Xin Yang, Ruobing Huang, Dong Ni
Date: 2022-07-09 00:00:00
Tasks: Attribute

Ultrasound (US) is the primary imaging technique for the diagnosis of thyroid cancer. However, accurate identification of nodule malignancy is a challenging task that can elude less-experienced clinicians. Recently, many computer-aided diagnosis (CAD) systems have been proposed to assist this process. However, most of them do not provide the reasoning of their classification process, which may jeopardize their credibility in practical use. To overcome this, we propose a novel deep learning framework called multi-attribute attention network (MAA-Net) that is designed to mimic the clinical diagnosis process. The proposed model learns to predict nodular attributes and infer their malignancy based on these clinically-relevant features. A multi-attention scheme is adopted to generate customized attention to improve each task and malignancy diagnosis. Furthermore, MAA-Net utilizes nodule delineations as nodules spatial prior guidance for the training rather than cropping the nodules with additional models or human interventions to prevent losing the context information. Validation experiments were performed on a large and challenging dataset containing 4554 patients. Results show that the proposed method outperformed other state-of-the-art methods and provides interpretable predictions that may better suit clinical needs.
COVID-19 Disease Identification on Chest-CT images using CNN and VGG16
Authors: Briskline Kiruba S, Petchiammal A, D. Murugan
Date: 2022-07-09 00:00:00
Tasks: Diagnostic

A newly identified coronavirus disease called COVID-19 mainly affects the human respiratory system. COVID-19 is an infectious disease caused by a virus originating in Wuhan, China, in December 2019. Early diagnosis is the primary challenge of health care providers. In the earlier stage, medical organizations were dazzled because there were no proper health aids or medicine to detect a COVID-19. A new diagnostic tool RT-PCR (Reverse Transcription Polymerase Chain Reaction), was introduced. It collects swab specimens from the patient's nose or throat, where the COVID-19 virus gathers. This method has some limitations related to accuracy and testing time. Medical experts suggest an alternative approach called CT (Computed Tomography) that can quickly diagnose the infected lung areas and identify the COVID-19 in an earlier stage. Using chest CT images, computer researchers developed several deep learning models identifying the COVID-19 disease. This study presents a Convolutional Neural Network (CNN) and VGG16-based model for automated COVID-19 identification on chest CT images. The experimental results using a public dataset of 14320 CT images showed a classification accuracy of 96.34% and 96.99% for CNN and VGG16, respectively.
BOSS: Bottom-up Cross-modal Semantic Composition with Hybrid Counterfactual Training for Robust Content-based Image Retrieval
Authors: Wenqiao Zhang, Jiannan Guo, Mengze Li, Haochen Shi, Shengyu Zhang, Juncheng Li, Siliang Tang, Yueting Zhuang
Date: 2022-07-09 00:00:00
Tasks: Content-Based Image Retrieval, counterfactual, Image Retrieval, Retrieval, Semantic Composition

Content-Based Image Retrieval (CIR) aims to search for a target image by concurrently comprehending the composition of an example image and a complementary text, which potentially impacts a wide variety of real-world applications, such as internet search and fashion retrieval. In this scenario, the input image serves as an intuitive context and background for the search, while the corresponding language expressly requests new traits on how specific characteristics of the query image should be modified in order to get the intended target image. This task is challenging since it necessitates learning and understanding the composite image-text representation by incorporating cross-granular semantic updates. In this paper, we tackle this task by a novel \underline{\textbf{B}}ottom-up cr\underline{\textbf{O}}ss-modal \underline{\textbf{S}}emantic compo\underline{\textbf{S}}ition (\textbf{BOSS}) with Hybrid Counterfactual Training framework, which sheds new light on the CIR task by studying it from two previously overlooked perspectives: \emph{implicitly bottom-up composition of visiolinguistic representation} and \emph{explicitly fine-grained correspondence of query-target construction}. On the one hand, we leverage the implicit interaction and composition of cross-modal embeddings from the bottom local characteristics to the top global semantics, preserving and transforming the visual representation conditioned on language semantics in several continuous steps for effective target image search. On the other hand, we devise a hybrid counterfactual training strategy that can reduce the model's ambiguity for similar queries.
Multi-view Attention for gestational age at birth prediction
Authors: Mathieu Leclercq, Martin Styner, Juan Carlos Prieto
Date: 2022-07-08 00:00:00
Tasks: Prediction, regression

We present our method for gestational age at birth prediction for the SLCN (surface learning for clinical neuroimaging) challenge. Our method is based on a multi-view shape analysis technique that captures 2D renderings of a 3D object from different viewpoints. We render the brain features on the surface of the sphere and then the 2D images are analyzed via 2D CNNs and an attention layer for the regression task. The regression task achieves a MAE of 1.637 +- 1.3 on the Native space and MAE of 1.38 +- 1.14 on the template space. The source code for this project is available in our github repository https://github.com/MathieuLeclercq/SLCN_challenge_UNC
FAIVConf: Face enhancement for AI-based Video Conference with Low Bit-rate
Authors: Zhengang Li, Sheng Lin, Shan Liu, Songnan Li, Xue Lin, Wei Wang, Wei Jiang
Date: 2022-07-08 00:00:00
Tasks: Face Generation, Face Swapping, Video Compression

Recently, high-quality video conferencing with fewer transmission bits has become a very hot and challenging problem. We propose FAIVConf, a specially designed video compression framework for video conferencing, based on the effective neural human face generation techniques. FAIVConf brings together several designs to improve the system robustness in real video conference scenarios: face-swapping to avoid artifacts in background animation; facial blurring to decrease transmission bit-rate and maintain the quality of extracted facial landmarks; and dynamic source update for face view interpolation to accommodate a large range of head poses. Our method achieves a significant bit-rate reduction in the video conference and gives much better visual quality under the same bit-rate compared with H.264 and H.265 coding schemes.
Hierarchical Average Precision Training for Pertinent Image Retrieval
Authors: Elias Ramzi, Nicolas Audebert, Nicolas Thome, Clément Rambour, Xavier Bitot
Date: 2022-07-05 00:00:00
Tasks: Image Retrieval, Metric Learning

Image Retrieval is commonly evaluated with Average Precision (AP) or Recall@k. Yet, those metrics, are limited to binary labels and do not take into account errors' severity. This paper introduces a new hierarchical AP training method for pertinent image retrieval (HAP-PIER). HAPPIER is based on a new H-AP metric, which leverages a concept hierarchy to refine AP by integrating errors' importance and better evaluate rankings. To train deep models with H-AP, we carefully study the problem's structure and design a smooth lower bound surrogate combined with a clustering loss that ensures consistent ordering. Extensive experiments on 6 datasets show that HAPPIER significantly outperforms state-of-the-art methods for hierarchical retrieval, while being on par with the latest approaches when evaluating fine-grained ranking performances. Finally, we show that HAPPIER leads to better organization of the embedding space, and prevents most severe failure cases of non-hierarchical methods. Our code is publicly available at: https://github.com/elias-ramzi/HAPPIER.
Computer Vision-Aided Reconfigurable Intelligent Surface-Based Beam Tracking: Prototyping and Experimental Results
Authors: Ming Ouyang, Yucong Wang, Feifei Gao, Shun Zhang, Puchu Li, Jian Ren
Date: 2022-07-11 00:00:00

In this paper, we propose a novel computer vision-based approach to aid Reconfigurable Intelligent Surface (RIS) for dynamic beam tracking and then implement the corresponding prototype verification system. A camera is attached at the RIS to obtain the visual information about the surrounding environment, with which RIS identifies the desired reflected beam direction and then adjusts the reflection coefficients according to the pre-designed codebook. Compared to the conventional approaches that utilize channel estimation or beam sweeping to obtain the reflection coefficients, the proposed one not only saves beam training overhead but also eliminates the requirement for extra feedback links. We build a 20-by-20 RIS running at 5.4 GHz and develop a high-speed control board to ensure the real-time refresh of the reflection coefficients. Meanwhile we implement an independent peer-to-peer communication system to simulate the communication between the base station and the user equipment. The vision-aided RIS prototype system is tested in two mobile scenarios: RIS works in near-field conditions as a passive array antenna of the base station; RIS works in far-field conditions to assist the communication between the base station and the user equipment. The experimental results show that RIS can quickly adjust the reflection coefficients for dynamic beam tracking with the help of visual information.
Cramér-Rao Bound Analysis of Radars for Extended Vehicular Targets with Known and Unknown Shape
Authors: Nil Garcia, Alessio Fascista, Angelo Coluccia, Henk Wymeersch, Canan Aydogdu, Rico Mendrzik, Gonzalo Seco-Granados
Date: 2022-07-11 00:00:00
Tasks: Position

Due to their shorter operating range and large bandwidth, automotive radars can resolve many reflections from their targets of interest, mainly vehicles. This calls for the use of extended-target models in place of simpler and more widely-adopted point-like target models. However, despite some preliminary work, the fundamental connection between the radar's accuracy as a function of the target vehicle state (range, orientation, shape) and radar properties remains largely unknown for extended targets. In this work, we first devise a mathematically tractable analytical model for a vehicle with arbitrary shape, modeled as an extended target parameterized by the center position, the orientation (heading) and the perimeter contour. We show that the derived expressions of the backscatter signal are tractable and correctly capture the effects of the extended-vehicle shape. Analytical derivations of the exact and approximate hybrid Cram\'er-Rao bounds for the position, orientation and contour are provided, which reveal connections with the case of point-like target and uncover the main dependencies with the received energy, bandwidth, and array size. The theoretical investigation is performed on the two different cases of known and unknown vehicle shape. Insightful simulation results are finally presented to validate the theoretical findings, including an analysis of the diversity effect of multiple radars sensing the extended target.
On Outage-based Beamforming Design for Dual-Functional Radar-Communication 6G Systems
Authors: Ahmad Bazzi, Marwa Chafii
Date: 2022-07-11 00:00:00

This article studies and derives beamforming design in a dual-functional radar-communication (DFRC) multiple-input-multiple-output system. We focus on a scenario, where the DFRC base station communicates with downlink communication users, with imperfect channel state information knowledge, and performs target detection, all via the same transmit signal. Through careful relaxation procedures, we arrive at a suitable and novel optimization problem, which maximizes the radar output power in the Bartlett sense, under probabilistic outage signal-to-interference-and-noise ratio constraints. Theoretical analysis proves optimality of the solution given by the relaxed version of the problem, as well as closed-form solutions in certain scenarios. Finally, the achieved performances and trade-offs of the proposed beamforming design are demonstrated through numerical simulations.
Homogeneity Enforced Calibration of Stage Nonidealities for Pipelined ADCs
Authors: Matthias Wagner, Oliver Lang, Thomas Bauernfeind, Mario Huemer
Date: 2022-07-11 00:00:00

Pipelined analog-to-digital converters (ADCs) are fundamental components of various signal processing systems requiring high sampling rates and a high linearity. Over the past years, calibration techniques have been intensively investigated to increase the linearity. In this work, we propose an equalization-based calibration technique which does not require knowledge of the ADC input signal for calibration. For that, a test signal and a scaled version of it are fed into the ADC sequentially, while only the corresponding output samples are used for calibration. Several test signal sources are possible, such as a signal generator (SG) or the system application (SA) itself. For the latter case, the presented method corresponds to a background calibration technique. Thus, slowly changing errors are tracked and calibrated continuously. Because of the low computational complexity of the calibration technique, it is suitable for an on-chip implementation. Ultimately, this work contains an analysis of the stability and convergence behavior as well as simulation results.
Improved Calibration Procedure for Wireless Inertial Measurement Units without Precision Equipment
Authors: Fritz Webering, Sarah Kleinjohann, Nils Stanislawski, Holger Blume
Date: 2022-07-11 00:00:00

Inertial measurement units (IMUs) are used in medical applications for many different purposes. However, an IMU's measurement accuracy can degrade over time, entailing re-calibration. In their 2014 paper, Tedaldi et al. presented an IMU calibration method that does not require external precision equipment or complex procedures. This allows end-users or personnel without expert knowledge of inertial measurement to re-calibrate the sensors by placing them in several suitable but not precisely defined orientations. In this work, we present several improvements to Tedaldi's method, both on the algorithmic level and the calibration procedure: adaptions for low noise accelerometers, a calibration helper object, and packet loss compensation for wireless calibration. We applied the modified calibration procedure to our custom-built IMU platform and verified the consistency of results across multiple calibration runs. In order to minimize the time needed for re-calibration, we analyzed how the calibration result accuracy degrades when fewer calibration orientations are used. We found that N=12 different orientations are sufficient to achieve a very good calibration, and more orientations yielded only marginal improvements. This is a significant improvement compared to the 37 to 50 orientations recommended by Tedaldi. Thus, we were reduced the time required to calibrate a single IMU from ca. 5 minutes to less than 2 minutes without sacrificing any meaningful calibration accuracy.