Reviewerly/exHarmony huggingface dataset:
SalsaNext: Fast, Uncertainty-Aware Semantic Segmentation of LiDAR Point Clouds

In this paper, we introduce SalsaNext for the uncertainty-aware semantic segmentation of a full 3D LiDAR point cloud in real-time. SalsaNext is the next version of SalsaNet [1] which has an encoder-decoder architecture where the encoder unit has a set of ResNet blocks and the decoder part combines upsampled features from the residual blocks. In contrast to SalsaNet, we introduce a new context module, replace the ResNet encoder blocks with a new residual dilated convolution stack with gradually increasing receptive fields and add the pixel-shuffle layer in the decoder. Additionally, we switch from stride convolution to average pooling and also apply central dropout treatment. To directly optimize the Jaccard index, we further combine the weighted cross entropy loss with Lovász-Softmax loss [4]. We finally inject a Bayesian treatment to compute the epistemic and aleatoric uncertainties for each point in the cloud. We provide a thorough quantitative evaluation on the Semantic-KITTI dataset [3], which demonstrates that the proposed SalsaNext outperforms other published semantic segmentation networks and achieves $$3.6\%$$ more accuracy over the previous state-of-the-art method. We also release our source code ( https://github.com/TiagoCortinhal/SalsaNext ).
Neural Voice Puppetry: Audio-Driven Facial Reenactment

We present Neural Voice Puppetry, a novel approach for audio-driven facial video synthesis (Video, Code and Demo: https://justusthies.github.io/posts/neural-voice-puppetry/). Given an audio sequence of a source person or digital assistant, we generate a photo-realistic output video of a target person that is in sync with the audio of the source input. This audio-driven facial reenactment is driven by a deep neural network that employs a latent 3D face model space. Through the underlying 3D representation, the model inherently learns temporal stability while we leverage neural rendering to generate photo-realistic output frames. Our approach generalizes across different people, allowing us to synthesize videos of a target actor with the voice of any unknown source actor or even synthetic voices that can be generated utilizing standard text-to-speech approaches. Neural Voice Puppetry has a variety of use-cases, including audio-driven video avatars, video dubbing, and text-driven video synthesis of a talking head. We demonstrate the capabilities of our method in a series of audio- and text-based puppetry examples, including comparisons to state-of-the-art techniques and a user study.
Long-Distance Entanglement Purification for Quantum Communication

High-quality long-distance entanglement is essential for both quantum communication and scalable quantum networks. Entanglement purification is to distill high-quality entanglement from low-quality entanglement in a noisy environment and it plays a key role in quantum repeaters. The previous significant entanglement purification experiments require two pairs of low-quality entangled states and were demonstrated in table-top. Here we propose and report a high-efficiency and long-distance entanglement purification using only one pair of hyperentangled states. We also demonstrate its practical application in entanglement-based quantum key distribution (QKD). One pair of polarization spatial-mode hyperentanglement was distributed over 11 km multicore fiber (noisy channel). After purification, the fidelity of polarization entanglement arises from 0.771 to 0.887 and the effective key rate in entanglement-based QKD increases from 0 to 0.332. The values of Clauser-Horne-Shimony-Holt (CHSH) inequality of polarization entanglement arises from 1.829 to 2.128. Moreover, by using one pair of hyperentanglement and deterministic controlled-NOT gate, the total purification efficiency can be estimated as 6.6x10^3 times than the experiment using two pairs of entangled states with spontaneous parametric down-conversion (SPDC) sources. Our results offer the potential to be implemented as part of a full quantum repeater and large scale quantum network.
A framework of blockchain technology adoption: An investigation of challenges and expected value

The purpose of the paper is to investigate the challenges and implications related to blockchain adoption in the private and public sectors, and from the entrepreneurs’ perspective. Based on 46 semi-structured interviews, a data-driven conceptual framework is proposed focussing on the environmental, organizational and technological challenges of blockchain adoption. The framework includes expected socio-economic value of blockchain adoption at the ecosystem level from a multi-stakeholder perspective: organizations and industries, end-users and society, public sector, and start-ups and entrepreneurs. The paper highlights the differences between permissioned and permissionless blockchains and identifies new constructs in blockchain technology adoption.
Designing Blockchain-Based Access Control Protocol in IoT-Enabled Smart-Grid System

We design a new blockchain-based access control protocol in IoT-enabled smart-grid system, called DBACP-IoTSG. Through the proposed DBACP-IoTSG, the data is securely brought to the service providers from their respective smart meters (SMs). The peer-to-peer (P2P) network is formed by the participating service providers, where the peer nodes are responsible for creating the blocks from the gathered data securely from their corresponding SMs and adding them into the blockchain after validation of the blocks using the voting-based consensus algorithm. In our work, the blockchain is considered as private because the data collected from the consumers of the SMs are private and confidential. By the formal security analysis under the random oracle model, nonmathematical security analysis and software-based formal security verification, DBACP-IoTSG is shown to be resistant against various attacks. We carry out the experimental results of various cryptographic primitives that are needed for comparative analysis using the widely used multiprecision integer and rational arithmetic cryptographic library (MIRACL). A detailed comparative study reveals that DBACP-IoTSG supports more functionality features and provides better security apart from its low communication and computation costs as compared to recently proposed relevant schemes. In addition, the blockchain implementation of DBACP-IoTSG has been performed to measure computational time needed for the varied number of blocks addition and also the varied number of transactions per block in the blockchain.
Generalizing Face Forgery Detection with High-frequency Features

Current face forgery detection methods achieve high accuracy under the within-database scenario where training and testing forgeries are synthesized by the same algorithm. However, few of them gain satisfying performance under the cross-database scenario where training and testing forgeries are synthesized by different algorithms. In this paper, we find that current CNN-based detectors tend to overfit to method-specific color textures and thus fail to generalize. Observing that image noises remove color textures and expose discrepancies between authentic and tampered regions, we propose to utilize the high-frequency noises for face forgery detection. We carefully devise three functional modules to take full advantage of the high-frequency features. The first is the multi-scale high-frequency feature extraction module that extracts high-frequency noises at multiple scales and composes a novel modality. The second is the residual-guided spatial attention module that guides the low-level RGB feature extractor to concentrate more on forgery traces from a new perspective. The last is the cross-modality attention module that leverages the correlation between the two complementary modalities to promote feature learning for each other. Comprehensive evaluations on several benchmark databases corroborate the superior generalization performance of our proposed method.
Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images

In the last few years, image denoising has benefited a lot from the fast development of neural networks. However, the requirement of large amounts of noisy-clean image pairs for supervision limits the wide use of these models. Although there have been a few attempts in training an image denoising model with only single noisy images, existing self-supervised denoising approaches suffer from inefficient network training, loss of useful information, or dependence on noise modeling. In this paper, we present a very simple yet effective method named Neighbor2Neighbor to train an effective image denoising model with only noisy images. Firstly, a random neighbor sub-sampler is proposed for the generation of training image pairs. In detail, input and target used to train a network are images sub-sampled from the same noisy image, satisfying the requirement that paired pixels of paired images are neighbors and have very similar appearance with each other. Secondly, a denoising network is trained on sub-sampled training pairs generated in the first stage, with a proposed regularizer as additional loss for better performance. The proposed Neighbor2Neighbor framework is able to enjoy the progress of state-of-the-art supervised denoising networks in network architecture design. Moreover, it avoids heavy dependence on the assumption of the noise distribution. We explain our approach from a theoretical perspective and further validate it through extensive experiments, including synthetic experiments with different noise distributions in sRGB space and real-world experiments on a denoising benchmark dataset in raw-RGB space.
Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition

Linguistic knowledge is of great benefit to scene text recognition. However, how to effectively model linguistic rules in end-to-end deep networks remains a research challenge. In this paper, we argue that the limited capacity of language models comes from: 1) implicitly language modeling; 2) unidirectional feature representation; and 3) language model with noise input. Correspondingly, we propose an autonomous, bidirectional and iterative ABINet for scene text recognition. Firstly, the autonomous suggests to block gradient flow between vision and language models to enforce explicitly language modeling. Secondly, a novel bidirectional cloze network (BCN) as the language model is proposed based on bidirectional feature representation. Thirdly, we propose an execution manner of iterative correction for language model which can effectively alleviate the impact of noise input. Additionally, based on the ensemble of iterative predictions, we propose a self-training method which can learn from unlabeled images effectively. Extensive experiments indicate that ABINet has superiority on low-quality images and achieves state-of-the-art results on several mainstream benchmarks. Besides, the ABINet trained with ensemble self-training shows promising improvement in realizing human-level recognition. Code is available at https://github.com/FangShancheng/ABINet.
Student engagement in online learning in Latin American higher education during the COVID‐19 pandemic: A systematic review

The COVID-19 pandemic has forced higher education institutions to implement online learning activities based on virtual platforms, allowing little time to prepare and train faculty members to familiarize students with digital technologies. While previous studies have looked at how students engaged with digital technologies in their learning activities, the characteristics of the student engagement in online learning remain underexplored. Therefore, a systematic review of the literature on student engagement in online learning in higher education is much needed. This article synthesizes the findings on student engagement in Latin American higher education institutions during the COVID-19 pandemic. After reviewing the studies on online learning activities, this review examines student engagement from behavioural, cognitive and affective dimensions and identifies the main characteristics of student engagement from these tripartite dimensions. The implications of the findings for online learning in Latin American higher education are as follows: (a) to transform higher education, (b) to provide adequate professional training, (c) to improve Internet connectivity, (d) to ensure quality online learning in higher education and (e) to provide emotional support. These findings will provide valuable guidance for teachers, educational authorities and policy makers and help them make informed decisions to use effective strategies to support online learning in higher education institutions.
Image Anomaly Detection with Generative Adversarial Networks

Many anomaly detection methods exist that perform well on low-dimensional problems however there is a notable lack of effective methods for high-dimensional spaces, such as images. Inspired by recent successes in deep learning we propose a novel approach to anomaly detection using generative adversarial networks. Given a sample under consideration, our method is based on searching for a good representation of that sample in the latent space of the generator; if such a representation is not found, the sample is deemed anomalous. We achieve state-of-the-art performance on standard image benchmark datasets and visual inspection of the most anomalous samples reveals that our method does indeed return anomalies.
Semi-Supervised Video Object Segmentation with Super-Trajectories

We introduce a semi-supervised video segmentation approach based on an efficient video representation, called as "super-trajectory". A super-trajectory corresponds to a group of compact point trajectories that exhibit consistent motion patterns, similar appearances, and close spatiotemporal relationships. We generate the compact trajectories using a probabilistic model, which enables handling of occlusions and drifts effectively. To reliably group point trajectories, we adopt a modified version of the density peaks based clustering algorithm that allows capturing rich spatiotemporal relations among trajectories in the clustering process. We incorporate two intuitive mechanisms for segmentation, called as reverse-tracking and object re-occurrence, for robustness and boosting the performance. Building on the proposed video representation, our segmentation method is discriminative enough to accurately propagate the initial annotations in the first frame onto the remaining frames. Our extensive experimental analyses on three challenging benchmarks demonstrate that, given the annotation in the first frame, our method is capable of extracting the target objects from complex backgrounds, and even reidentifying them after prolonged occlusions, producing high-quality video object segments.
Segmentation of Glomeruli Within Trichrome Images Using Deep Learning

The number of glomeruli and glomerulosclerosis evaluated on kidney biopsy slides constitute standard components of a renal pathology report. Prevailing methods for glomerular assessment remain manual, labor intensive, and nonstandardized. We developed a deep learning framework to accurately identify and segment glomeruli from digitized images of human kidney biopsies.Trichrome-stained images (n = 275) from renal biopsies of 171 patients with chronic kidney disease treated at the Boston Medical Center from 2009 to 2012 were analyzed. A sliding window operation was defined to crop each original image to smaller images. Each cropped image was then evaluated by at least 3 experts into 3 categories: (i) no glomerulus, (ii) normal or partially sclerosed (NPS) glomerulus, and (iii) globally sclerosed (GS) glomerulus. This led to identification of 751 unique images representing nonglomerular regions, 611 images with NPS glomeruli, and 134 images with GS glomeruli. A convolutional neural network (CNN) was trained with cropped images as inputs and corresponding labels as output. Using this model, an image processing routine was developed to scan the test images to segment the GS glomeruli.The CNN model was able to accurately discriminate nonglomerular images from NPS and GS images (performance on test data: Accuracy: 92.67% ± 2.02% and Kappa: 0.8681 ± 0.0392). The segmentation model that was based on the CNN multilabel classifier accurately marked the GS glomeruli on the test data (Matthews correlation coefficient = 0.628).This work demonstrates the power of deep learning for assessing complex histologic structures from digitized human kidney biopsies.
Boundary loss for highly unbalanced segmentation

Widely used loss functions for CNN segmentation, e.g., Dice or cross-entropy, are based on integrals over the segmentation regions. Unfortunately, for highly unbalanced segmentations, such regional summations have values that differ by several orders of magnitude across classes, which affects training performance and stability. We propose a boundary loss, which takes the form of a distance metric on the space of contours, not regions. This can mitigate the difficulties of highly unbalanced problems because it uses integrals over the interface between regions instead of unbalanced integrals over the regions. Furthermore, a boundary loss complements regional information. Inspired by graph-based optimization techniques for computing active-contour flows, we express a non-symmetric $L_2$ distance on the space of contours as a regional integral, which avoids completely local differential computations involving contour points. This yields a boundary loss expressed with the regional softmax probability outputs of the network, which can be easily combined with standard regional losses and implemented with any existing deep network architecture for N-D segmentation. We report comprehensive evaluations and comparisons on different unbalanced problems, showing that our boundary loss can yield significant increases in performances while improving training stability. Our code is publicly available: https://github.com/LIVIAETS/surface-loss .
Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication

Currently, progressively larger deep neural networks are trained on ever growing data corpora. In result, distributed training schemes are becoming increasingly relevant. A major issue in distributed training is the limited communication bandwidth between contributing nodes or prohibitive communication cost in general. To mitigate this problem we propose Sparse Binary Compression (SBC), a compression framework that allows for a drastic reduction of communication cost for distributed training. SBC combines existing techniques of communication delay and gradient sparsification with a novel binarization method and optimal weight update encoding to push compression gains to new limits. By doing so, our method also allows us to smoothly trade-off gradient sparsity and temporal sparsity to adapt to the requirements of the learning task. Our experiments show, that SBC can reduce the upstream communication on a variety of convolutional and recurrent neural network architectures by more than four orders of magnitude without significantly harming the convergence speed in terms of forward-backward passes. For instance, we can train ResNet50 on ImageNet in the same number of iterations to the baseline accuracy, using ×3531 less bits or train it to a 1% lower accuracy using ×37208 less bits. In the latter case, the total upstream communication required is cut from 125 terabytes to 3.35 gigabytes for every participating client.
Devil Is in the Edges: Learning Semantic Boundaries From Noisy Annotations

We tackle the problem of semantic boundary prediction, which aims to identify pixels that belong to object(class) boundaries. We notice that relevant datasets consist of a significant level of label noise, reflecting the fact that precise annotations are laborious to get and thus annotators trade-off quality with efficiency. We aim to learn sharp and precise semantic boundaries by explicitly reasoning about annotation noise during training. We propose a simple new layer and loss that can be used with existing learning-based boundary detectors. Our layer/loss enforces the detector to predict a maximum response along the normal direction at an edge, while also regularizing its direction. We further reason about true object boundaries during training using a level set formulation, which allows the network to learn from misaligned labels in an end-to-end fashion. Experiments show that we improve over the CASENet backbone network by more than 4% in terms of MF(ODS) and 18.61% in terms of AP, outperforming all current state-of-the-art methods including those that deal with alignment. Furthermore, we show that our learned network can be used to significantly improve coarse segmentation labels, lending itself as an efficient way to label new data.
Unsupervised Part-Based Disentangling of Object Shape and Appearance

Large intra-class variation is the result of changes in multiple object characteristics. Images, however, only show the superposition of different variable factors such as appearance or shape. Therefore, learning to disentangle and represent these different characteristics poses a great challenge, especially in the unsupervised case. Moreover, large object articulation calls for a flexible part-based model. We present an unsupervised approach for disentangling appearance and shape by learning parts consistently over all instances of a category. Our model for learning an object representation is trained by simultaneously exploiting invariance and equivariance constraints between synthetically transformed images. Since no part annotation or prior information on an object class is required, the approach is applicable to arbitrary classes. We evaluate our approach on a wide range of object categories and diverse tasks including pose prediction, disentangled image synthesis, and video-to-video translation. The approach outperforms the state-of-the-art on unsupervised keypoint prediction and compares favorably even against supervised approaches on the task of shape and appearance transfer.
A Dual-Path Model With Adaptive Attention for Vehicle Re-Identification

In recent years, attention models have been extensively used for person and vehicle re-identification. Most re-identification methods are designed to focus attention on key-point locations. However, depending on the orientation, the contribution of each key-point varies. In this paper, we present a novel dual-path adaptive attention model for vehicle re-identification (AAVER). The global appearance path captures macroscopic vehicle features while the orientation conditioned part appearance path learns to capture localized discriminative features by focusing attention on the most informative key-points. Through extensive experimentation, we show that the proposed AAVER method is able to accurately re-identify vehicles in unconstrained scenarios, yielding state of the art results on the challenging dataset VeRi-776. As a byproduct, the proposed system is also able to accurately predict vehicle key-points and shows an improvement of more than 7% over state of the art. The code for key-point estimation model is available at https://github.com/Pirazh/Vehicle_Key_ Point_Orientation_Estimation.