Reviewerly/exHarmony huggingface dataset:
ID2image: Leakage of Non-ID Information into Face Descriptors and Inversion from Descriptors to Images

Embedding a face image to a descriptor vector using a deep CNN is a widely used technique in face recognition. Via several possible training strategies, such embeddings are supposed to capture only identity information. Information about the environment (such as background and lighting) or changeable aspects of the face (such as pose, expression, presence of glasses, hat etc.) should be discarded since they are not useful for recognition. In this paper, we present a surprising result that this is not the case. We show that non-ID attributes, as well as landmark positions and the image histogram can be recovered from the ID embedding of state-of-the-art face embedding networks (VGGFace2 and ArcFace). In fact, these non-ID attributes can be predicted from ID embeddings with similar accuracy to a prediction from the original image. Going further, we present an optimisation strategy that uses a generative model (specifically StyleGAN2 for faces) to recover images from an ID embedding. We show photorealistic inversion from ID embedding to face image in which not only is the ID realistically reconstructed but the pose, lighting and background/apparel to some extent as well.
Improved Sensitivity of No-Reference Image Visual Quality Metrics to the Presence of Noise

A problem of no-reference image visual quality assessment when images are corrupted by noise is considered in this paper. A specialized image set is proposed for the following two tasks: automatic verification of sensitivity of no-reference image visual quality metrics to noise, and analysis of blind noise level estimation methods. As a result, a method to improve the sensitivity of a given no reference quality metric to the presence of noise is proposed by combining this metric with a noise level estimator. The proposed method allows to significantly decrease a probability of wrong quality predictions for noisy images. Efficiency of usage of different noise level estimators in the proposed combined metrics is analyzed.
Introducing Spatial Context in Patch-Based Deep Learning for Semantic Segmentation in Whole Body MRI

Improved imaging protocols and rapid scanning are making whole-body imaging increasingly popular in medical applications, where accurate object segmentation often plays an important role. Nowadays, convolutional neural networks (CNN) are the go-to method for automated image segmentation. Unfortunately, with improved image resolution, whole-body scans can grow large in size, preventing CNN training on entire images due to GPU memory limitations. An alternative to full-image training is patch-based training, which however suffers from context loss. We propose a way of reintroducing spatial context with marginal increase in memory requirements by employing landmark-normalized distance maps that carry relative position information for the patches. As a proof-of-concept we evaluate its effect with multiple networks with and without built-in context integration, using a multi-class whole-body MRI dataset. The results show that our proposed approach yields significant improvement over the baseline networks, even when used out-of-the box. We provide a discussion on its regularization properties and usability cases.
Single-Stage 3D Pose Estimation of Vulnerable Road Users Using Pseudo-Labels

Human pose estimation of vulnerable road users is an important perception task for autonomous vehicles which can be exploited for intention prediction in order to guide the vehicle’s actions. Single-stage human pose estimation approaches with their potential in terms of simplicity and efficiency have shown only mediocre results in 2D, and have hardly been investigated in 3D in the autonomous driving domain so far. We tackle this challenge with the 2D single-stage human pose estimator KAPAO. We find that KAPAO achieves state-of-the-art performance in our evaluation on domain-specific 2D benchmark datasets, which motivates its extension for application in 3D. To overcome a lack of ground truth vulnerable road user data for 3D pose estimation, we first extend the Waymo Open Dataset with additional 3D pseudo-labels. We create more than one million 3D poses, that we estimate using the dataset’s exhaustive person bounding boxes and associated LiDAR point clouds. Evaluating their quality, we report a mean per joint position error of less than 10 cm. Having access to large-scale domain-specific 3D pose data, we propose a 3D variant of KAPAO that additionally predicts the depths of joints. We evaluate it on our extended Waymo Open Dataset and compare its performance to that of a LiDAR uplifting baseline. The proposed approach is low-latency and produces plausible poses but struggles to estimate absolute depth precisely, particularly at large distances. We alleviate that limitation by implementing a conditional LiDAR-based depth correction.
Gradient Adjusting Networks for Domain Inversion

StyleGAN2 was demonstrated to be a powerful image generation engine that supports semantic editing. However, in order to manipulate a real-world image, one first needs to be able to retrieve its corresponding latent representation in StyleGAN’s latent space that is decoded to an image as close as possible to the desired image. For many real-world images, a latent representation does not exist, which necessitates the tuning of the generator network. We present a per-image optimization method that tunes a StyleGAN2 generator such that it achieves a local edit to the generator’s weights, resulting in almost perfect inversion, while still allowing image editing, by keeping the rest of the mapping between an input latent representation tensor and an output image relatively intact. The method is based on a one-shot training of a set of shallow update networks (aka. Gradient Modification Modules) that modify the layers of the generator. After training the Gradient Modification Modules, a modified generator is obtained by a single application of these networks to the original parameters, and the previous editing capabilities of the generator are maintained. Our experiments show a sizable gap in performance over the current state of the art in this very active domain. Our code is available at https://github.com/sheffier/gani .
ConvMix: Combining Intermediate Latent Features in Deep Convolutional Neural Networks

In traditional deep learning models, latent features to the downstream task are received only from the terminal layer of the feature extractor. The intermediate layers of a feature extractor contain significant spatially salient information which, when pooled by the interleaved pooling operations, is lost. These intermediate latent embeddings can improve the overall performance for vision tasks when leveraged properly. Recently, more complex combination schemes leveraging the intermediate embeddings directly for the downstream task have been proposed, but often require additional hyperparameters, increasing their computational cost and have limited generalizability between datasets. In this paper, we propose, ConvMix, a novel, learned combination scheme for intermediate latent features of a deep convolutional neural network which can be trained without incurring additional training cost and can be readily transferred between datasets. ConvMix leverages features at multiple stages of a CNN to distill spatial information in images, and create a richer embedding for the downstream task. Giving the network a ‘wider view’ by leveraging multi-level spatially pooled features of the image enables better regularization by preventing learning specific indentifying features but rather focusing on the wider image itself. We visually confirm this ‘wider view’ via GradCam and show that ConvMix ensure that spatially salient features are prioritized in the latent embeddings. In our experiments on CIFAR10-100, CINIC10, STL10, SVHN and TinyImageNet datasets, we show that our approach not only achieves better performance compared to state-of-the-art approaches but more importantly the percentage gain in performance scales with the increase in model/problem complexity due to the internal regularization effect of ConvMix.
Polarimetric Underwater Stereo

We propose a novel method for recovering per-pixel surface normals and depth of objects in water. We show that these two geometric surface properties of each pixel can be reconstructed simultaneously when observing an object underwater, regardless of its texture. Our key idea is to leverage polarized rays along different underwater light paths while considering perspective projection and refraction. We implement our method using an off-the-shelf polarization camera and an LCD, and derive a practical calibration method to determine the values of its imaging parameters using a single planar mirror. Results on a number of real-world objects in water demonstrate the effectiveness of our method.
From Local Binary Patterns to Pixel Difference Networks for Efficient Visual Representation Learning

LBP is a successful hand-crafted feature descriptor in computer vision. However, in the deep learning era, deep neural networks, especially convolutional neural networks (CNNs) can automatically learn powerful task-aware features that are more discriminative and of higher representational capacity. To some extent, such hand-crafted features can be safely ignored when designing deep computer vision models. Nevertheless, due to LBP’s preferable properties in visual representation learning, an interesting topic has arisen to explore the value of LBP in enhancing modern deep models in terms of efficiency, memory consumption, and predictive performance. In this paper, we provide a comprehensive review on such efforts which aims to incorporate the LBP mechanism into the design of CNN modules to make deep models stronger. In retrospect of what has been achieved so far, the paper discusses open challenges and directions for future research.
Anchor-ReID: A Test Time Adaptation for Person Re-identification

Person re-identification (ReID) is a challenging computer vision problem where the objective is to retrieve a person of interest from a gallery of images. Conventional person ReID methods struggle to generalize across different domains, leading to inferior cross-domain performance. To address this, domain generalization (DG) person re-id methods are proposed. The majority of DG studies focus on designing more robust models that are trained on source datasets in an offline setting with explicit components or training mechanisms for domain generalization. Moreover, these frameworks do not take into consideration that in the real world the environment is in continual change resulting in the degradation of the discriminative feature extraction. To tackle these problems, we propose a new problem formulation of Test-time adaption for ReID (TTA ReID). Our TTA ReID takes a pre-trained model that was not designed for domain generalization from source domain, and aims in inference to adapt it to the current target domain in an unsupervised setting. To this end, we propose Anchor ReID, a framework designed to adapt a pre-trained model without altering its network architecture and make it robust to domain shift. Comprehensive experiments on CUHK03, Duke-mtmc, and Market 1501 datasets demonstrate the benefits of the proposed approach. The proposed Anchor ReID framework can improve a pre-trained model (that was not designed for domain generalization) and achieves an absolute gain of mAP 10 in the CUHK03 dataset.
Systematic Augmentation in HSV Space for Semantic Segmentation of Prostate Biopsies

In recent years, the combination of the digitization of the field of pathology and increased computational power has led to a big increase in research of computer-aided diagnostics using systems based on artificial intelligence (AI). This includes detection and classification of prostate cancer, where several studies have shown great promise in automated prostate cancer grading using deep learning based AI systems. However, there is still work to be done to ensure that these algorithms are invariant to possible variations of the digitized microscopy images they are applied to. A standard method in deep learning to increase the variation of the training data is dataset augmentation. All of these studies apply some augmentation of their data, however, there is a lack of evaluation of different methods and their impact on this crucial part of the AI systems. In this study, we look into different color augmentation methods for the task of segmentation of prostate biopsies. Furthermore, we introduce a novel color augmentation method based on stereographic projection. Our results affirm the importance of studying different augmentation methods and indicate a gain in performance using our method.
Online Delivery of Discussion-Oriented Postsecondary Classes during Critical Health Scenarios, Educating Responsible Computer Scientists towards Improved Societal Trust in Artificial Intelligence

This paper discusses how upper year postsecondary computer science students being educated on social responsibility and AI ethics may benefit from online options for delivery of classes, within discussion-based courses.We reflect on the relationship between empathy, AI social responsibility and online learning, including insights provided by published work on these topics.We draw insights from a course offered in Spring 2022, where students had the opportunity to experience both in-person and online classes, when being educated on social implications of computing.Included are a few recommendations for future efforts to support online classes during times of critical health scenarios.
Correlated collective excitation and quantum entanglement between two Rydberg superatoms in steady state

&lt;sec&gt;Owing to the unique physical characteristics of Rydberg atoms, which play an important role in quantum information and quantum computation, the theoretical and applied research of Rydberg atoms have become the hot spots of scientific research in recent years. With the large polarizability of Rydberg atoms, even a small electric field could cause a considerable electric dipole moment, resulting in a strong dipole-dipole interaction between Rydberg atoms. The multiple excitations of the Rydberg states are strongly inhibited because of the strong dipole interaction between atoms within a mesoscopic interaction (blockade) region. We call this phenomenon the dipole blockade effect. The dipole blockade effect makes it possible to build single-photon quantum devices, implement quantum gates, generate quantum entanglement, and simulate many-body quantum problems, etc.&lt;/sec&gt;&lt;sec&gt;A Rydberg atomic ensemble in the same blockade region can be regarded as a superatom. In the same way, if these atoms trapped in two optical dipole traps, each sub-ensemble can be considered as a sub-superatom which is closely related to the superatom. According to the fact that two Rydberg sub-superatoms can be strongly correlated due to sharing no more than one excited Rydberg atom, we study correlated collective excitation and quantum entanglement between two Rydberg sub-superatoms in a steady state. With the superatom model, the problem of exponentially increasing system size with the number of atoms can be circumvented to a certain extent in studying many-body physics. By solving the two-body Lindblad’s master equation accurately, we obtain the analytical expressions for the collective excitation probabilities of the two sub-superatoms, and the concurrence measuring the bipartite entanglement between them. Our results show that they are all sensitive to the number of atoms in each Rydberg superatom: the bigger (including more atoms) the Rydberg superatom, the higher the collective Rydberg excitation probability is. And that the maximally entangled state can only be obtained with two equal-sized Rydberg superatoms. When this condition is fulfilled, the mesoscopic entanglement can be generated by adding the number of atoms in each Rydberg superatom. This may provide an attractive platform for studying the quantum-classical correspondence and have potential promising applications in quantum information processing.&lt;/sec&gt;
Bidirectional Associative Memory as Normalisator Backpropagation Neural Network in the Signature Image Training

One of the technologies needed by humans, is supporting security technology in digital transactions that are present in the form of digital image signature verifier technology. Development of algorithms for the needs of signature verification is required to improve the performance (accuracy and speed) from the existing algorithms. This time, combination of Bidirectional Associative Memory (BAM) and BackPropagation Neural Network is used in the training process the signature in the form of digital images. Digital image signature through the purification process the data before processed with BAM and BackPropagation. To determine the effect of normalization BAM, carried out performance comparisons between the performance with BAM and the performance without BAM. From this study, it is evident that the use of normalisator can increase the speed of the training process digital image signature.
A Classification and Analysis Focusing on Attempts to Give a Computer a Personality: A Technological History of Chatbots as Simple Artificial Intelligence

In recent years, research on artificial intelligence has been thriving. The use of AI by general consumers is rapidly expanding. Among them, the most popular AI would be chatbots. Chatbot systems, which have been proposed since the 1980s based on the image of general-purpose artificial intelligence, have simple mechanisms, but many of them have a high degree of human-likeness. Voice assistants installed in smartphones are often based on chatbot technology, and the digital native generation casually uses them on a daily basis. Chatbots are used in a variety of fields besides business and education and are expected to be used more and more in the future. The number of such chatbots is increasing all over the world, and the number of services is increasing rapidly. The number of users and characteristics of these services are changing day by day, and Mega-Techs are building chatbot platforms one after another within a short period of time. The market is growing steadily. The needs and applications of chatbots are quite diverse, for example, they do not only answer simple questions but also recommend products or guide payments. In this paper, we will overview history of simple artificial intelligence systems, from ELIZA, the ancestor of chatbots, to the present day’s, and classify them in terms of mechanism and structure. We will also conduct a survey on people’s attitudes toward AI and chatbots, in order to highlight the challenges with the future system of chatbots. In fact, many people argue that chatbots are merely displaying search results from a web browser. In this research, we organize and analyze the existing chatbots of various scales and their services.
Ontology Partitioning for Managing Change Effects

Ontology evolution refers to the process of an ontology modification in response to change in the domain or its conceptualization. It takes in several phases which the most important are changes representation, semantics of changes, propagation and validation. Analysis of changes to be made in ontology is necessary to identify potential consequences on ontology and on dependent objects. Indeed, a modification of an ontological entity can generate impacts making the system inconsistent. Thus, impacts propagation is important to keep the system stable. However, managing changes and their effects is not a simple task; it is more difficult if concerned ontology is voluminous. We propose an approach to partition large ontology for change effects managing. Our proposed approach consists of creating a weighted dependency graph from ontology structure and then determining communities using the Island Line algorithm.
TrAcOn: A Traffic Accident Ontology for Identifying Accidents-Prone Areas in Senegal

Traffic accidents are responsible each year for many deaths in the world. Most of them are caused by human fault (speeding, driving while intoxicated, dangerous overtaking, drowsiness, dangerous parking, etc.). Despite the progress noted with the arrival of smart cars and the semantic information representation through ontologies, underdeveloped countries have not yet integrated IT solutions into their transport management systems. Added to this, the absence of concepts that can describe certain types of transport vehicles in ontologies developed for transport systems. Our research objective is to propose an approach to identifying areas at high risk of accidents in less developed countries like Senegal. In this article, we build an ontology of traffic accidents, called TrAcOn (Traffic Accident Ontology) for a traffic accident description system in Senegal. The ontology allows among others to identify highly accidental areas.
The study of DNA methylation profiles of Norway spruce in the zone of technogenic pollution by MS-ISSR and MS-PCR methods

In recent years, there is an increasing attention to the issues of sustainable forest management.One of the promising methods to address these issues is genomic editing.Genomic editing currently faces regulatory limitations due to the possible horizontal transfer of genes from edited plants into natural ecosystems.This study focuses on the issue of obtaining sterile poplar and aspen clones, which are not subject to regulatory restrictions due to the use of genetic engineering methods.This article summarizes early studies of flowering genes aimed at creating sterile plants through gene modification, Describes experiments to reveal the role of genes in flowering using the BARNASE:BARSTAR system, RNA interference and gene editing.The article briefly summarizes the principles of CRISPR/Cas editing.Some online resources used to search for spacers in the genome of the subject being edited are listed.A method is described for obtaining gid RNA inside the pUC57-sgRNA bacterial vector by restriction with BsaI and subsequent spacer ligation.Early studies on methods for the delivery of editing constructs into poplar cells are also reviewed, and the possibility of their application to free-stranded editing is evaluated.The article discusses the significant advantages of the genome editing method over gene modification.The results of creating sterile aspens can be widely used as a breeding base for further experiments on changing economically valuable traits, and will also help to assess the real potential of using gene editing for perennial plants.