The intentions and factors influencing university students to perform CPR for strangers based on the theory of planned behavior study.

Objective: The Theory of Planned Behavior (TPB) was used to compile a questionnaire to determine the relationship between knowledge, attitude, subjective norm, perceived behavioral control and the intention of university students to perform cardiopulmonary resuscitation (CPR) for strangers, and the factors influencing them.

Methods: We recruited 575 university students who completed an online questionnaire within 30 min to assess knowledge, attitude, subjective norm, and perceived behavioral control related to bystander CPR. Factor analysis was used to evaluate the reliability of the extended questionnaire.

Towards Lightweight Transformer Via Group-Wise Transformation for Vision-and-Language Tasks.

Despite the exciting performance, Transformer is criticized for its excessive parameters and computation cost. However, compressing Transformer remains as an open problem due to its internal complexity of the layer designs, i.e.

Balance-Aware Grid Collage for Small Image Collections.

Grid collages (GClg) of small image collections are popular and useful in many applications, such as personal album management, online photo posting, and graphic design. In this article, we focus on how visual effects influence individual preferences through various arrangements of multiple images under such scenarios. A novel balance-aware metric is proposed to bridge the gap between multi-image joint presentation and visual pleasure.

Network Pruning Using Adaptive Exemplar Filters.

Popular network pruning algorithms reduce redundant information by optimizing hand-crafted models, and may cause suboptimal performance and long time in selecting filters. We innovatively introduce adaptive exemplar filters to simplify the algorithm design, resulting in an automatic and efficient pruning approach called EPruner. Inspired by the face recognition community, we use a message-passing algorithm Affinity Propagation on the weight matrices to obtain an adaptive number of exemplars, which then act as the preserved filters.

MIGO-NAS: Towards Fast and Generalizable Neural Architecture Search.

Neural architecture search (NAS) has achieved unprecedented performance in various computer vision tasks. However, most existing NAS methods are defected in search efficiency and model generalizability. In this paper, we propose a novel NAS framework, termed MIGO-NAS, with the aim to guarantee the efficiency and generalizability in arbitrary search spaces.

Fast Class-Wise Updating for Online Hashing.

Online image hashing has received increasing research attention recently, which processes large-scale data in a streaming fashion to update the hash functions on-the-fly. To this end, most existing works exploit this problem under a supervised setting, i.e.

Content-Based Visual Summarization for Image Collections.

With the surge of images in the information era, people demand an effective and accurate way to access meaningful visual information. Accordingly, effective and accurate communication of information has become indispensable. In this article, we propose a content-based approach that automatically generates a clear and informative visual summarization based on design principles and cognitive psychology to represent image collections.

Semi-Supervised Adversarial Monocular Depth Estimation.

In this paper, we address the problem of monocular depth estimation when only a limited number of training image-depth pairs are available. To achieve a high regression accuracy, the state-of-the-art estimation methods rely on CNNs trained with a large number of image-depth pairs, which are prohibitively costly or even infeasible to acquire. Aiming to break the curse of such expensive data collections, we propose a semi-supervised adversarial learning framework that only utilizes a small number of image-depth pairs in conjunction with a large number of easily-available monocular images to achieve high performance.

Toward an Expert Level of Lung Cancer Detection and Classification Using a Deep Convolutional Neural Network.

Background: Computed tomography (CT) is essential for pulmonary nodule detection in diagnosing lung cancer. As deep learning algorithms have recently been regarded as a promising technique in medical fields, we attempt to integrate a well-trained deep learning algorithm to detect and classify pulmonary nodules derived from clinical CT images.

Materials And Methods: Open-source data sets and multicenter data sets have been used in this study.

Animated Construction of Chinese Brush Paintings.

In this paper, we present a method for reconstructing the drawing process of Chinese brush paintings. We demonstrate the possibility of computing an artistically reasonable drawing order from a static brush painting that is consistent with the rules of art. We map the key principles of drawing composition to our computational framework, which first organizes the strokes in three stages and then optimizes stroke ordering with natural evolution strategies.

Toward Optimal Manifold Hashing via Discrete Locally Linear Embedding.

Binary code learning, also known as hashing, has received increasing attention in large-scale visual search. By transforming high-dimensional features to binary codes, the original Euclidean distance is approximated via Hamming distance. More recently, it is advocated that it is the manifold distance, rather than the Euclidean distance, that should be preserved in the Hamming space.

Data-Driven Synthesis of Cartoon Faces Using Different Styles.

This paper presents a data-driven approach for automatically generating cartoon faces in different styles from a given portrait image. Our stylization pipeline consists of two steps: an offline analysis step to learn about how to select and compose facial components from the databases; a runtime synthesis step to generate the cartoon face by assembling parts from a database of stylized facial components. We propose an optimization framework that, for a given artistic style, simultaneously considers the desired image-cartoon relationships of the facial components and a proper adjustment of the image composition.

Measuring and Predicting Visual Importance of Similar Objects.

Similar objects are ubiquitous and abundant in both natural and artificial scenes. Determining the visual importance of several similar objects in a complex photograph is a challenge for image understanding algorithms. This study aims to define the importance of similar objects in an image and to develop a method that can select the most important instances for an input image from multiple similar objects.

Efficient, Edge-Aware, Combined Color Quantization and Dithering.

In this paper, we present a novel algorithm to simultaneously accomplish color quantization and dithering of images. This is achieved by minimizing a perception-based cost function, which considers pixel-wise differences between filtered versions of the quantized image and the input image. We use edge aware filters in defining the cost function to avoid mixing colors on the opposite sides of an edge.