Reviewerly/exHarmony huggingface dataset:
Minimal distance calculation between the industrial robot and its workspace based on circle/polygon-slices representation

The real-time and reliable collision detection is the vital basis for the robotic motion control in real applications. In this work, the minimal distance calculation between the industrial robot and its workspace is presented. This method first uses circular/polygonal slices to represent robotic sub-structures with curved edges more accurately, and uses polygonal planes to construct its workspace. Being the basis of the distance calculation, the shortest distance between the spatial circle/polygon and the polygonal plane, as well as that between the circular/polygonal plane and the polygonal plane are first modelled through spatial relation analysis. The simulation validation indicates that the proposed algorithm is more efficient and reliable than both the point-clouds-based and bounding-volume-hierarchies-based algorithms. And the influence of the refinement size on the performance of the proposed model is revealed. Finally, the application example shows that this work is a significant contribution for the collision-free motion control of the industrial robot. The proposed algorithm can also be applied to the distance calculation of other complex objects because of the generality of the proposed slices representation way.
Lexical Richness and Text Length: An Entropy-based Perspective

Text length is a major concern in the measurement of lexical richness, and how lexical richness is affected by text length still remains open. The present study aims to explore the relation between text length and lexical richness from an entropy-based perspective. Results show a non-linear growth pattern of lexical richness by increasing text length. To be specific, lexical richness increases rapidly with shorter texts. It soon reaches a boundary point from which it stabilizes despite the continuous expansion of text length. The boundary point of the lexical richness by the Shannon estimation is around 1000 tokens and that by the Zhang estimation is lower and more varied, including 500, 800, and 1000 tokens. Such stability may be explained by the stabilization of word probability in the text.
A Training Method for Image Compression Networks to Improve Perceptual Quality of Reconstructions

Recently, neural-network based lossy image compression methods have been actively studied and they have achieved remarkable performance. However, the classical evaluation metrics, such as PSNR and MS-SSIM, that the recent approaches have been using in their objective function yield sub-optimal coding efficiency in terms of human perception, although they are very dominant metrics in research and standardization fields. Taking into account that improving the perceptual quality is one of major goals in lossy image compression, we propose a new training method that allows the existing image compression networks to reconstruct perceptually enhanced images. By experiments, we show the effectiveness of our method, both quantitatively and qualitatively.
Detail-Preserving Arbitrary Style Transfer

Style transfer has recently received considerable attention in the academic and industrial field. Recent work has achieved high quality stylized results by using the hidden activation. However, the hierarchy of content image has been mostly ignored. Artistic style factors including texture, stroke sizes or color, also distort, blur, add or remove content details. In this paper, we present a detail-preserving arbitrary style transfer network that can suppress structural distortion and preserve semantic content. We propose a refine network, which can not only spatially match the hierarchical structure but also preserve style patterns. Meanwhile, we introduce the global and local content losses to keep global structure and local detail information, respectively. In addition, our approach is not only effective against detail-preserving arbitrary style transfer, but also friendly with photographic and exposed style transfer. We utilize both quantitative and qualitative evaluations to demonstrate the effectiveness of our method.
Scalable Uncertainty for Computer Vision With Functional Variational Inference

As Deep Learning continues to yield successful applications in Computer Vision, the ability to quantify all forms of uncertainty is a paramount requirement for its safe and reliable deployment in the real-world. In this work, we leverage the formulation of variational inference in function space, where we associate Gaussian Processes (GPs) to both Bayesian CNN priors and variational family. Since GPs are fully determined by their mean and covariance functions, we are able to obtain predictive uncertainty estimates at the cost of a single forward pass through any chosen CNN architecture and for any supervised learning task. By leveraging the structure of the induced covariance matrices, we propose numerically efficient algorithms which enable fast training in the context of high-dimensional tasks such as depth estimation and semantic segmentation. Additionally, we provide sufficient conditions for constructing regression loss functions whose probabilistic counterparts are compatible with aleatoric uncertainty quantification.
Performance Improvement Of Pre-trained Convolutional Neural Networks For Action Recognition

Abstract Action recognition is a challenging task. Deep learning models have been investigated to solve this problem. Setting up a new neural network model is a crucial and time-consuming process. Alternatively, pre-trained convolutional neural network (CNN) models offer rapid modeling. The selection of the hyperparameters of CNNs is a challenging issue that heavily depends on user experience. The parameters of CNNs should be carefully selected to get effective results. For this purpose, the artificial bee colony (ABC) algorithm is used for tuning the parameters to get optimum results. The proposed method includes three main stages: the image preprocessing stage involves automatic cropping of the meaningful area within the images in the data set, the transfer learning stage includes experiments with six different pre-trained CNN models and the hyperparameter tuning stage using the ABC algorithm. Performance comparison of the pre-trained CNN models involving the use and nonuse of the ABC algorithm for the Stanford 40 data set is presented. The experiments show that the pre-trained CNN models with ABC are more successful than pre-trained CNN models without ABC. Additionally, to the best of our knowledge, the improved NASNet-Large CNN model with the ABC algorithm gives the best accuracy of 87.78% for the overall success rate-based performance metric.
Photosequencing of Motion Blur using Short and Long Exposures

Photosequencing aims to transform a motion blurred image to a sequence of sharp images. This problem is challenging due to the inherent ambiguities in temporal ordering as well as the recovery of lost spatial textures due to blur. Adopting a computational photography approach, we propose to capture two short exposure images, along with the original blurred long exposure image to aid in the aforementioned challenges. Post-capture, we recover the sharp photosequence using a novel blur decomposition strategy that recursively splits the long exposure image into smaller exposure intervals. We validate the approach by capturing a variety of scenes with interesting motions using machine vision cameras programmed to capture short and long exposure sequences. Our experimental results show that the proposed method resolves both fast and fine motions better than prior works.
Towards Learning Structure via Consensus for Face Segmentation and Parsing

Face segmentation is the task of densely labeling pixels on the face according to their semantics. While current methods place an emphasis on developing sophisticated architectures, use conditional random fields for smoothness, or rather employ adversarial training, we follow an alternative path towards robust face segmentation and parsing. Occlusions, along with other parts of the face, have a proper structure that needs to be propagated in the model during training. Unlike state-of-the-art methods that treat face segmentation as an independent pixel prediction problem, we argue instead that it should hold highly correlated outputs within the same object pixels. We thereby offer a novel learning mechanism to enforce structure in the prediction via consensus, guided by a robust loss function that forces pixel objects to be consistent with each other. Our face parser is trained by transferring knowledge from another model, yet it encourages spatial consistency while fitting the labels. Different than current practice, our method enjoys pixel-wise predictions, yet paves the way for fewer artifacts, less sparse masks, and spatially coherent outputs.
MemNAS: Memory-Efficient Neural Architecture Search With Grow-Trim Learning

Recent studies on automatic neural architecture search techniques have demonstrated significant performance, competitive to or even better than hand-crafted neural architectures. However, most of the existing search approaches tend to use residual structures and a concatenation connection between shallow and deep features. A resulted neural network model, therefore, is non-trivial for resource-constraint devices to execute since such a model requires large memory to store network parameters and intermediate feature maps along with excessive computing complexity. To address this challenge, we propose MemNAS, a novel growing and trimming based neural architecture search framework that optimizes not only performance but also memory requirement of an inference network. Specifically, in the search process, we consider running memory use, including network parameters and the essential intermediate feature maps memory requirement, as an optimization objective along with performance. Besides, to improve the accuracy of the search, we extract the correlation information among multiple candidate architectures to rank them and then choose the candidates with desired performance and memory efficiency. On the ImageNet classification task, our MemNAS achieves 75.4% accuracy, 0.7% higher than MobileNetV2 with 42.1% less memory requirement. Additional experiments confirm that the proposed MemNAS can perform well across the different targets of the trade-off between accuracy and memory consumption.
3D Viewpoint Estimation Based on Aesthetics

Research on viewpoint in 3D scenes has been a cutting-edge subject in computer vision and 3D scene understanding. Calculating a high quality 3D viewpoint in virtual scenes is conducive to a more comprehensive analysis of 3D graphic structure, which can contribute to an overall understanding of virtual environments. 3D viewpoint research can also be applied to the analysis of structural relationships in virtual scenes and to exploit hidden hierarchical structures, thus it plays a role in fields such as scientific visualization computing or image-based scene modeling. Viewpoint research combined with relevant research results of human visual perception or visual psychology can be used to analyze the visual interest and focus areas of 3D objects in human vision, thus effectively improving the quality of mesh simplification and rendering efficiency, as well as providing an optimization basis for highly complex scenes. In fields such as 3D computer games, virtual reality, and landscape animation, research on high-quality viewpoints can be adopted to optimize global lighting and illumination. Additionally, practical applications can be realized through user attention analysis and aesthetic improvement strategies based on high-quality viewpoints, such as intelligent modeling and scene optimization calculations. In common fields such as 3D model retrieval, practical applications can be realized for saliency research, scientific visualization computing, and medical 3D imaging. In-depth research has been performed and extensive applications have been developed that make use of viewpoint analysis. In this paper, we introduce an optimization strategy to calculate high-quality virtual viewpoints for aesthetic images. A novel framework is proposed for modifying and optimizing the viewpoint calculation model by combining a multibranch CNN and a viewpoint correction method, thus realizing rendered images with a higher aesthetic quality. By attempting to fully integrate the visual perception with the geometric information calculation, we expect this method to achieve more comprehensive applications in many practice areas in the future, such as the realization of aesthetically based virtual camera path planning and analysis of the aesthetic characteristics of virtual environments.
Generating Images Instead of Retrieving Them

Finding images matching a user's intention has been largely based on matching a representation of the user's information needs with an existing collection of images. For example, using an example image or a written query to express the information need and retrieving images that share similarities with the query or example image. However, such an approach is limited to retrieving only images that already exist in the underlying collection. Here, we present a methodology for generating images matching the user intention instead of retrieving them. The methodology utilizes a relevance feedback loop between a user and generative adversarial neural networks (GANs). GANs can generate novel photorealistic images which are initially not present in the underlying collection, but generated in response to user feedback. We report experiments (N=29) where participants generate images using four different domains and various search goals with textual and image targets. The results show that the generated images match the tasks and outperform images selected as baselines from a fixed image collection. Our results demonstrate that generating new information can be more useful for users than retrieving it from a collection of existing information.
RDCFace: Radial Distortion Correction for Face Recognition

The effects of radial lens distortion often appear in wide-angle cameras of surveillance and safeguard systems, which may severely degrade performances of previous face recognition algorithms. Traditional methods for radial lens distortion correction usually employ line features in scenarios that are not suitable for face images. In this paper, we propose a distortion-invariant face recognition system called RDCFace, which directly and only utilize the distorted images of faces, to alleviate the effects of radial lens distortion. RDCFace is an end-to-end trainable cascade network, which can learn rectification and alignment parameters to achieve a better face recognition performance without requiring supervision of facial landmarks and distortion parameters. We design sequential spatial transformer layers to optimize the correction, alignment, and recognition modules jointly. The feasibility of our method comes from implicitly using the statistics of the layout of face features learned from the large-scale face data. Extensive experiments indicate that our method is distortion robust and gains significant improvements on LFW, YTF, CFP, and RadialFace, a real distorted face benchmark compared with state-of-the-art methods.
Regional patterns in standardised and transformed pathfinder elements in soil related to orogenic-style mineralisation in southern New Zealand

Soil samples collected at 50–70 cm depth at 8 km-spaced sample sites over 40,000 km2 of southern New Zealand were analysed for their chemical composition. Nine pathfinder elements (Ag, As, Au, Bi, Mo, Sb, Sn, Te, W), which are useful in exploration for orogenic-style mineralisation, were z-score normalised using two different matrices to account for both lithology and metamorphic grade, which vary significantly across the survey area. Interrogation of interpolated plots of the normalised data, relative to known orogenic-style deposits (Au ± Sb ± W), highlights chemical associations that varied between deposit types, and between similar deposit types mineralised under varying pressure-temperature-time conditions. Active mining for orogenic-style Au along the Hyde-Macraes Shear Zone is coincident with a regionally-significant, multi-element soil anomaly (Ag, As, Au, Mo, Sn, Te, Sb, W). The chemical associations vary along strike of the mined-portion of the shear, such that the northwest has high Ag, Au, Mo, Sn, and Te relative to the southeast which has high Sb, W in soil anomalies, hinting at chemical complexity along the shear. The chemical footprint of this deposit is readily identified in the 8 km-spaced grid, considerably expanding the chemical footprint previously inferred from hard-rock studies. Historic orogenic W mines are associated with high As, Au, Bi, W in soil anomalies, and historic Sb mining with high Bi in soil anomalies. Chemical soil anomalies are likely related to the progressive metamorphic breakdown of minerals, especially rutile and pyrite. Several multi-element soil anomalies that are coincident with known orogenic mineralisation, also occur in areas that have not been mined, and explored only at a reconnaissance level. This study demonstrates the importance of normalisation of chemical data in complex geological settings and highlights the value that can be extracted beyond only plotting raw-concentration, single-element maps, and the significant opportunity in multielement studies of soil in mineral exploration for orogenic-style mineralisation.
Coding Electronic Health Records with Adversarial Reinforcement Path Generation

Electronic Health Record (EHR) coding is the task of assigning one or more International Classification of Diseases (ICD) codes to every EHR. Most previous work either ignores the hierarchical nature of the ICD codes or only focuses on parent-child relations. Moreover, existing EHR coding methods predict ICD codes from the leaf level with the greatest ICD number and the most fine-grained categories, which makes it difficult for models to make correct decisions. In order to address these problems, we model EHR coding as a path generation task. For this approach, we need to address two main challenges: (1) How to model relations between EHR and ICD codes, and relations between ICD codes? (2) How to evaluate the quality of generated ICD paths in order to obtain a signal that can be used to supervise the learning? We propose a coarse-to-fine ICD path generation framework, named Reinforcement Path Generation Network (RPGNet), that implements EHR coding with a Path Generator (PG) and a Path Discriminator (PD). We address challenge (1) by introducing a Path Message Passing (PMP) module in the PG to encode three types of relation: between EHRs and ICD codes, between parent-child ICD codes, and between sibling ICD codes. To address challgenge (2), we propose a PD component that estimates the reward for each ICD code in a generated path. RPGNet is trained with Reinforcement Learning (RL) in an adversarial manner. Experiments on the MIMIC-III benchmark dataset show that RPGNet significantly outperforms state-of-the-art methods in terms of micro-averaged F1 and micro-averaged AUC.
Structure from motion for complex image sets

This paper presents an approach for Structure from Motion (SfM) for unorganized complex image sets. To achieve high accuracy and robustness, image triplets are employed and an (approximate) internal camera calibration is assumed to be known. The complexity of an image set is determined by the camera configurations which may include wide as well as weak baselines. Wide baselines occur for instance when terrestrial images and images from small Unmanned Aerial Systems (UAS) are combined. The resulting large (geometric/radiometric) distortions between images make image matching difficult possibly leading to an incomplete result. Weak baselines mean an insufficient distance between cameras compared to the distance of the observed scene and give rise to critical camera configurations. Inappropriate handling of such configurations may lead to various problems in triangulation-based SfM up to total failure. The focus of our approach lies on a complete linking of images even in case of wide or weak baselines. We do not rely on any additional information such as camera configurations, Global Positioning System (GPS) or an Inertial Navigation System (INS). As basis for generating suitable triplets to link the images, an iterative graph-based method is employed formulating image linking as the search for a terminal Steiner minimum tree in the line graph. SIFT (Lowe, 2004) descriptors are embedded into Hamming space for fast image similarity ranking. This is employed to limit the number of pairs to be geometrically verified by a computationally and more complex wide baseline matching method (Mayer et al., 2012). Critical camera configurations which are not suitable for geometric verification are detected by means of classification (Michelini and Mayer, 2019). Additionally, we propose a graph-based approach for the optimization of the hierarchical merging of triplets to efficiently generate larger image subsets. By this means, a complete, 3D reconstruction of the scene is obtained. Experiments demonstrate that the approach is able to produce reliable orientation for large image sets comprising wide as well as weak baseline configurations.
Lossy Compression with Distortion Constrained Optimization

When training end-to-end learned models for lossy compression, one has to balance the rate and distortion losses. This is typically done by manually setting a tradeoff parameter β, an approach called β-VAE. Using this approach it is difficult to target a specific rate or distortion value, because the result can be very sensitive to β, and the approriate value for β depends on the model and problem setup. As a result, model comparison requires extensive per-model β-tuning, and producing a whole rate-distortion curve (by varying β) for each model to be compared.We argue that the constrained optimization method of Rezende and Viola, 2018 [29] is a lot more appropriate for training lossy compression models because it allows us to obtain the best possible rate subject to a distortion constraint. This enables pointwise model comparisons, by training two models with the same distortion target and comparing their rate. We show that the method does manage to satisfy the constraint on a realistic image compression task, outperforms a constrained optimization method based on a hinge-loss, and is more practical to use for model selection than a β-VAE.
A JND Dataset Based on VVC Compressed Images

In this paper, we establish a just noticeable distortion (JND) dataset based on the next generation video coding standard Versatile Video Coding (VVC). The dataset consists of 202 images which cover a wide range of content with resolution 1920×1080. Each image is encoded by VTM 5.0 intra coding with the quantization parameter (QP) ranging from 13 to 51. The details regarding dataset construction, subjective testing and data post-processing are described in this paper. Finally, the significance of the dataset towards future video coding research is envisioned. All source images as well as the testing data have been made available to the public.