pwc-archive/papers-with-abstracts huggingface dataset:
Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future Directions
Authors: Chulin Xie, Zhong Cao, Yunhui Long, Diange Yang, Ding Zhao, Bo Li
Date: 2022-09-08 00:00:00
Tasks: Autonomous Vehicles

Recent advances in machine learning have enabled its wide application in different domains, and one of the most exciting applications is autonomous vehicles (AVs), which have encouraged the development of a number of ML algorithms from perception to prediction to planning. However, training AVs usually requires a large amount of training data collected from different driving environments (e.g., cities) as well as different types of personal information (e.g., working hours and routes). Such collected large data, treated as the new oil for ML in the data-centric AI era, usually contains a large amount of privacy-sensitive information which is hard to remove or even audit. Although existing privacy protection approaches have achieved certain theoretical and empirical success, there is still a gap when applying them to real-world applications such as autonomous vehicles. For instance, when training AVs, not only can individually identifiable information reveal privacy-sensitive information, but also population-level information such as road construction within a city, and proprietary-level commercial secrets of AVs. Thus, it is critical to revisit the frontier of privacy risks and corresponding protection approaches in AVs to bridge this gap. Following this goal, in this work, we provide a new taxonomy for privacy risks and protection methods in AVs, and we categorize privacy in AVs into three levels: individual, population, and proprietary. We explicitly list out recent challenges to protect each of these levels of privacy, summarize existing solutions to these challenges, discuss the lessons and conclusions, and provide potential future directions and opportunities for both researchers and practitioners. We believe this work will help to shape the privacy research in AV and guide the privacy protection technology design.
Vision for Bosnia and Herzegovina in Artificial Intelligence Age: Global Trends, Potential Opportunities, Selected Use-cases and Realistic Goals
Authors: Zlatan Ajanović, Emina Aličković, Aida Branković, Sead Delalić, Eldar Kurtić, Salem Malikić, Adnan Mehonić, Hamza Merzić, Kenan Šehić, Bahrudin Trbalić
Date: 2022-09-08 00:00:00

Artificial Intelligence (AI) is one of the most promising technologies of the 21. century, with an already noticeable impact on society and the economy. With this work, we provide a short overview of global trends, applications in industry and selected use-cases from our international experience and work in industry and academia. The goal is to present global and regional positive practices and provide an informed opinion on the realistic goals and opportunities for positioning B&H on the global AI scene.
The Role Of Biology In Deep Learning
Authors: Robert Bain
Date: 2022-09-07 00:00:00
Tasks: Continual Learning, Deep Learning

Artificial neural networks took a lot of inspiration from their biological counterparts in becoming our best machine perceptual systems. This work summarizes some of that history and incorporates modern theoretical neuroscience into experiments with artificial neural networks from the field of deep learning. Specifically, iterative magnitude pruning is used to train sparsely connected networks with 33x fewer weights without loss in performance. These are used to test and ultimately reject the hypothesis that weight sparsity alone improves image noise robustness. Recent work mitigated catastrophic forgetting using weight sparsity, activation sparsity, and active dendrite modeling. This paper replicates those findings, and extends the method to train convolutional neural networks on a more challenging continual learning task. The code has been made publicly available.
Improved Masked Image Generation with Token-Critic
Authors: José Lezama, Huiwen Chang, Lu Jiang, Irfan Essa
Date: 2022-09-09 00:00:00
Tasks: Diversity, Image Generation

Non-autoregressive generative transformers recently demonstrated impressive image generation performance, and orders of magnitude faster sampling than their autoregressive counterparts. However, optimal parallel sampling from the true joint distribution of visual tokens remains an open challenge. In this paper we introduce Token-Critic, an auxiliary model to guide the sampling of a non-autoregressive generative transformer. Given a masked-and-reconstructed real image, the Token-Critic model is trained to distinguish which visual tokens belong to the original image and which were sampled by the generative transformer. During non-autoregressive iterative sampling, Token-Critic is used to select which tokens to accept and which to reject and resample. Coupled with Token-Critic, a state-of-the-art generative transformer significantly improves its performance, and outperforms recent diffusion models and GANs in terms of the trade-off between generated image quality and diversity, in the challenging class-conditional ImageNet generation.
GRASP-Net: Geometric Residual Analysis and Synthesis for Point Cloud Compression
Authors: Jiahao Pang, Muhammad Asad Lodhi, Dong Tian
Date: 2022-09-09 00:00:00

Point cloud compression (PCC) is a key enabler for various 3-D applications, owing to the universality of the point cloud format. Ideally, 3D point clouds endeavor to depict object/scene surfaces that are continuous. Practically, as a set of discrete samples, point clouds are locally disconnected and sparsely distributed. This sparse nature is hindering the discovery of local correlation among points for compression. Motivated by an analysis with fractal dimension, we propose a heterogeneous approach with deep learning for lossy point cloud geometry compression. On top of a base layer compressing a coarse representation of the input, an enhancement layer is designed to cope with the challenging geometric residual/details. Specifically, a point-based network is applied to convert the erratic local details to latent features residing on the coarse point cloud. Then a sparse convolutional neural network operating on the coarse point cloud is launched. It utilizes the continuity/smoothness of the coarse geometry to compress the latent features as an enhancement bit-stream that greatly benefits the reconstruction quality. When this bit-stream is unavailable, e.g., due to packet loss, we support a skip mode with the same architecture which generates geometric details from the coarse point cloud directly. Experimentation on both dense and sparse point clouds demonstrate the state-of-the-art compression performance achieved by our proposal. Our code is available at https://github.com/InterDigitalInc/GRASP-Net.
Pre-training image-language transformers for open-vocabulary tasks
Authors: AJ Piergiovanni, Weicheng Kuo, Anelia Angelova
Date: 2022-09-09 00:00:00
Tasks: Question Answering, Visual Entailment, Visual Question Answering, Visual Question Answering (VQA)

We present a pre-training approach for vision and language transformer models, which is based on a mixture of diverse tasks. We explore both the use of image-text captioning data in pre-training, which does not need additional supervision, as well as object-aware strategies to pre-train the model. We evaluate the method on a number of textgenerative vision+language tasks, such as Visual Question Answering, visual entailment and captioning, and demonstrate large gains over standard pre-training methods.
Bridging the Gap: Differentially Private Equivariant Deep Learning for Medical Image Analysis
Authors: Florian A. Hölzl, Daniel Rueckert, Georgios Kaissis
Date: 2022-09-09 00:00:00
Tasks: Medical Image Analysis, Privacy Preserving

Machine learning with formal privacy-preserving techniques like Differential Privacy (DP) allows one to derive valuable insights from sensitive medical imaging data while promising to protect patient privacy, but it usually comes at a sharp privacy-utility trade-off. In this work, we propose to use steerable equivariant convolutional networks for medical image analysis with DP. Their improved feature quality and parameter efficiency yield remarkable accuracy gains, narrowing the privacy-utility gap.
Saliency Guided Adversarial Training for Learning Generalizable Features with Applications to Medical Imaging Classification System
Authors: Xin Li, Yao Qiang, Chengyin Li, Sijia Liu, Dongxiao Zhu
Date: 2022-09-09 00:00:00

This work tackles a central machine learning problem of performance degradation on out-of-distribution (OOD) test sets. The problem is particularly salient in medical imaging based diagnosis system that appears to be accurate but fails when tested in new hospitals/datasets. Recent studies indicate the system might learn shortcut and non-relevant features instead of generalizable features, so-called good features. We hypothesize that adversarial training can eliminate shortcut features whereas saliency guided training can filter out non-relevant features; both are nuisance features accounting for the performance degradation on OOD test sets. With that, we formulate a novel model training scheme for the deep neural network to learn good features for classification and/or detection tasks ensuring a consistent generalization performance on OOD test sets. The experimental results qualitatively and quantitatively demonstrate the superior performance of our method using the benchmark CXR image data sets on classification tasks.
Towards Confidence-guided Shape Completion for Robotic Applications
Authors: Andrea Rosasco, Stefano Berti, Fabrizio Bottarel, Michele Colledanchise, Lorenzo Natale
Date: 2022-09-09 00:00:00
Tasks: Object, Robotic Grasping

Many robotic tasks involving some form of 3D visual perception greatly benefit from a complete knowledge of the working environment. However, robots often have to tackle unstructured environments and their onboard visual sensors can only provide incomplete information due to limited workspaces, clutter or object self-occlusion. In recent years, deep learning architectures for shape completion have begun taking traction as effective means of inferring a complete 3D object representation from partial visual data. Nevertheless, most of the existing state-of-the-art approaches provide a fixed output resolution in the form of voxel grids, strictly related to the size of the neural network output stage. While this is enough for some tasks, e.g. obstacle avoidance in navigation, grasping and manipulation require finer resolutions and simply scaling up the neural network outputs is computationally expensive. In this paper, we address this limitation by proposing an object shape completion method based on an implicit 3D representation providing a confidence value for each reconstructed point. As a second contribution, we propose a gradient-based method for efficiently sampling such implicit function at an arbitrary resolution, tunable at inference time. We experimentally validate our approach by comparing reconstructed shapes with ground truths, and by deploying our shape completion algorithm in a robotic grasping pipeline. In both cases, we compare results with a state-of-the-art shape completion approach.
Tracking Small and Fast Moving Objects: A Benchmark
Authors: Zhewen Zhang, Fuliang Wu, Yuming Qiu, Jingdong Liang, Shuiwang Li
Date: 2022-09-09 00:00:00
Tasks: Visual Tracking

With more and more large-scale datasets available for training, visual tracking has made great progress in recent years. However, current research in the field mainly focuses on tracking generic objects. In this paper, we present TSFMO, a benchmark for \textbf{T}racking \textbf{S}mall and \textbf{F}ast \textbf{M}oving \textbf{O}bjects. This benchmark aims to encourage research in developing novel and accurate methods for this challenging task particularly. TSFMO consists of 250 sequences with about 50k frames in total. Each frame in these sequences is carefully and manually annotated with a bounding box. To the best of our knowledge, TSFMO is the first benchmark dedicated to tracking small and fast moving objects, especially connected to sports. To understand how existing methods perform and to provide comparison for future research on TSFMO, we extensively evaluate 20 state-of-the-art trackers on the benchmark. The evaluation results exhibit that more effort are required to improve tracking small and fast moving objects. Moreover, to encourage future research, we proposed a novel tracker S-KeepTrack which surpasses all 20 evaluated approaches. By releasing TSFMO, we expect to facilitate future researches and applications of tracking small and fast moving objects. The TSFMO and evaluation results as well as S-KeepTrack are available at \url{https://github.com/CodeOfGithub/S-KeepTrack}.
Temporally Adjustable Longitudinal Fluid-Attenuated Inversion Recovery MRI Estimation / Synthesis for Multiple Sclerosis
Authors: Jueqi Wang, Derek Berger, Erin Mazerolle, Othman Soufan, Jacob Levman
Date: 2022-09-09 00:00:00
Tasks: Image Generation

Multiple Sclerosis (MS) is a chronic progressive neurological disease characterized by the development of lesions in the white matter of the brain. T2-fluid-attenuated inversion recovery (FLAIR) brain magnetic resonance imaging (MRI) provides superior visualization and characterization of MS lesions, relative to other MRI modalities. Longitudinal brain FLAIR MRI in MS, involving repetitively imaging a patient over time, provides helpful information for clinicians towards monitoring disease progression. Predicting future whole brain MRI examinations with variable time lag has only been attempted in limited applications, such as healthy aging and structural degeneration in Alzheimer's Disease. In this article, we present novel modifications to deep learning architectures for MS FLAIR image synthesis, in order to support prediction of longitudinal images in a flexible continuous way. This is achieved with learned transposed convolutions, which support modelling time as a spatially distributed array with variable temporal properties at different spatial locations. Thus, this approach can theoretically model spatially-specific time-dependent brain development, supporting the modelling of more rapid growth at appropriate physical locations, such as the site of an MS brain lesion. This approach also supports the clinician user to define how far into the future a predicted examination should target. Accurate prediction of future rounds of imaging can inform clinicians of potentially poor patient outcomes, which may be able to contribute to earlier treatment and better prognoses. Four distinct deep learning architectures have been developed. The ISBI2015 longitudinal MS dataset was used to validate and compare our proposed approaches. Results demonstrate that a modified ACGAN achieves the best performance and reduces variability in model accuracy.
Talking Head from Speech Audio using a Pre-trained Image Generator
Authors: Mohammed M. Alghamdi, He Wang, Andrew J. Bulpitt, David C. Hogg
Date: 2022-09-09 00:00:00
Tasks: SSIM

We propose a novel method for generating high-resolution videos of talking-heads from speech audio and a single 'identity' image. Our method is based on a convolutional neural network model that incorporates a pre-trained StyleGAN generator. We model each frame as a point in the latent space of StyleGAN so that a video corresponds to a trajectory through the latent space. Training the network is in two stages. The first stage is to model trajectories in the latent space conditioned on speech utterances. To do this, we use an existing encoder to invert the generator, mapping from each video frame into the latent space. We train a recurrent neural network to map from speech utterances to displacements in the latent space of the image generator. These displacements are relative to the back-projection into the latent space of an identity image chosen from the individuals depicted in the training dataset. In the second stage, we improve the visual quality of the generated videos by tuning the image generator on a single image or a short video of any chosen identity. We evaluate our model on standard measures (PSNR, SSIM, FID and LMD) and show that it significantly outperforms recent state-of-the-art methods on one of two commonly used datasets and gives comparable performance on the other. Finally, we report on ablation experiments that validate the components of the model. The code and videos from experiments can be found at https://mohammedalghamdi.github.io/talking-heads-acm-mm
EchoCoTr: Estimation of the Left Ventricular Ejection Fraction from Spatiotemporal Echocardiography
Authors: Rand Muhtaseb, Mohammad Yaqub
Date: 2022-09-09 00:00:00
Tasks: Video Understanding

Learning spatiotemporal features is an important task for efficient video understanding especially in medical images such as echocardiograms. Convolutional neural networks (CNNs) and more recent vision transformers (ViTs) are the most commonly used methods with limitations per each. CNNs are good at capturing local context but fail to learn global information across video frames. On the other hand, vision transformers can incorporate global details and long sequences but are computationally expensive and typically require more data to train. In this paper, we propose a method that addresses the limitations we typically face when training on medical video data such as echocardiographic scans. The algorithm we propose (EchoCoTr) utilizes the strength of vision transformers and CNNs to tackle the problem of estimating the left ventricular ejection fraction (LVEF) on ultrasound videos. We demonstrate how the proposed method outperforms state-of-the-art work to-date on the EchoNet-Dynamic dataset with MAE of 3.95 and $R^2$ of 0.82. These results show noticeable improvement compared to all published research. In addition, we show extensive ablations and comparisons with several algorithms, including ViT and BERT. The code is available at https://github.com/BioMedIA-MBZUAI/EchoCoTr.
Retinal Image Restoration and Vessel Segmentation using Modified Cycle-CBAM and CBAM-UNet
Authors: Alnur Alimanov, Md Baharul Islam
Date: 2022-09-09 00:00:00
Tasks: Generative Adversarial Network, Image Restoration, Retinal Vessel Segmentation, Segmentation, SSIM

Clinical screening with low-quality fundus images is challenging and significantly leads to misdiagnosis. This paper addresses the issue of improving the retinal image quality and vessel segmentation through retinal image restoration. More specifically, a cycle-consistent generative adversarial network (CycleGAN) with a convolution block attention module (CBAM) is used for retinal image restoration. A modified UNet is used for retinal vessel segmentation for the restored retinal images (CBAM-UNet). The proposed model consists of two generators and two discriminators. Generators translate images from one domain to another, i.e., from low to high quality and vice versa. Discriminators classify generated and original images. The retinal vessel segmentation model uses downsampling, bottlenecking, and upsampling layers to generate segmented images. The CBAM has been used to enhance the feature extraction of these models. The proposed method does not require paired image datasets, which are challenging to produce. Instead, it uses unpaired data that consists of low- and high-quality fundus images retrieved from publicly available datasets. The restoration performance of the proposed method was evaluated using full-reference evaluation metrics, e.g., peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). The retinal vessel segmentation performance was compared with the ground-truth fundus images. The proposed method can significantly reduce the degradation effects caused by out-of-focus blurring, color distortion, low, high, and uneven illumination. Experimental results show the effectiveness of the proposed method for retinal image restoration and vessel segmentation.
Pathology Synthesis of 3D-Consistent Cardiac MR Images using 2D VAEs and GANs
Authors: Sina Amirrajab, Cristian Lorenz, Juergen Weese, Josien Pluim, Marcel Breeuwer
Date: 2022-09-09 00:00:00
Tasks: Image Generation, Image Segmentation, Semantic Segmentation

We propose a method for synthesizing cardiac magnetic resonance (MR) images with plausible heart pathologies and realistic appearances for the purpose of generating labeled data for the application of supervised deep-learning (DL) training. The image synthesis consists of label deformation and label-to-image translation tasks. The former is achieved via latent space interpolation in a VAE model, while the latter is accomplished via a label-conditional GAN model. We devise three approaches for label manipulation in the latent space of the trained VAE model; i) \textbf{intra-subject synthesis} aiming to interpolate the intermediate slices of a subject to increase the through-plane resolution, ii) \textbf{inter-subject synthesis} aiming to interpolate the geometry and appearance of intermediate images between two dissimilar subjects acquired with different scanner vendors, and iii) \textbf{pathology synthesis} aiming to synthesize a series of pseudo-pathological synthetic subjects with characteristics of a desired heart disease. Furthermore, we propose to model the relationship between 2D slices in the latent space of the VAE prior to reconstruction for generating 3D-consistent subjects from stacking up 2D slice-by-slice generations. We demonstrate that such an approach could provide a solution to diversify and enrich an available database of cardiac MR images and to pave the way for the development of generalizable DL-based image analysis algorithms. We quantitatively evaluate the quality of the synthesized data in an augmentation scenario to achieve generalization and robustness to multi-vendor and multi-disease data for image segmentation. Our code is available at https://github.com/sinaamirrajab/CardiacPathologySynthesis.
Self-supervised Learning for Heterogeneous Graph via Structure Information based on Metapath
Authors: Shuai Ma, Jian-wei Liu, Xin Zuo
Date: 2022-09-09 00:00:00
Tasks: Link Prediction, Meta-Learning, Node Classification, Representation Learning, Self-Supervised Learning

graph neural networks (GNNs) are the dominant paradigm for modeling and handling graph structure data by learning universal node representation. The traditional way of training GNNs depends on a great many labeled data, which results in high requirements on cost and time. In some special scene, it is even unavailable and impracticable. Self-supervised representation learning, which can generate labels by graph structure data itself, is a potential approach to tackle this problem. And turning to research on self-supervised learning problem for heterogeneous graphs is more challenging than dealing with homogeneous graphs, also there are fewer studies about it. In this paper, we propose a SElfsupervised learning method for heterogeneous graph via Structure Information based on Metapath (SESIM). The proposed model can construct pretext tasks by predicting jump number between nodes in each metapath to improve the representation ability of primary task. In order to predict jump number, SESIM uses data itself to generate labels, avoiding time-consuming manual labeling. Moreover, predicting jump number in each metapath can effectively utilize graph structure information, which is the essential property between nodes. Therefore, SESIM deepens the understanding of models for graph structure. At last, we train primary task and pretext tasks jointly, and use meta-learning to balance the contribution of pretext tasks for primary task. Empirical results validate the performance of SESIM method and demonstrate that this method can improve the representation ability of traditional neural networks on link prediction task and node classification task.