Collaborative Feature Learning from Social Media

Image feature representation plays an essential role in image recognition and related tasks. The current state-of-the-art feature learning paradigm is supervised learning from labeled data. However, this paradigm requires large-scale category labels, which limits its applicability to domains where labels are hard to obtain. In this paper, we propose a new data-driven feature learning paradigm which does not rely on category labels. Instead, we learn from user behavior data collected on social media. Concretely, we use the image relationship discovered in the latent space from the user behavior data to guide the image feature learning. We collect a large-scale image and user behavior dataset from Behance.net. The dataset consists of 1.9 million images and over 300 million view records from 1.9 million users. We validate our feature learning paradigm on this dataset and find that the learned feature significantly outperforms the state-of-the-art image features in learning better image similarities. We also show that the learned feature performs competitively on various recognition benchmarks

Building a Large Scale Dataset for Image Emotion Recognition: The Fine Print and The Benchmark

Psychological research results have confirmed that people can have different emotional reactions to different visual stimuli. Several papers have been published on the problem of visual emotion analysis. In particular, attempts have been made to analyze and predict people's emotional reaction towards images. To this end, different kinds of hand-tuned features are proposed. The results reported on several carefully selected and labeled small image data sets have confirmed the promise of such features. While the recent successes of many computer vision related tasks are due to the adoption of Convolutional Neural Networks (CNNs), visual emotion analysis has not achieved the same level of success. This may be primarily due to the unavailability of confidently labeled and relatively large image data sets for visual emotion analysis. In this work, we introduce a new data set, which started from 3+ million weakly labeled images of different emotions and ended up 30 times as large as the current largest publicly available visual emotion data set. We hope that this data set encourages further research on visual emotion analysis. We also perform extensive benchmarking analyses on this large data set using the state of the art methods including CNNs.Comment: 7 pages, 7 figures, AAAI 201

Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks

Sentiment analysis of online user generated content is important for many social media analytics tasks. Researchers have largely relied on textual sentiment analysis to develop systems to predict political elections, measure economic indicators, and so on. Recently, social media users are increasingly using images and videos to express their opinions and share their experiences. Sentiment analysis of such large scale visual content can help better extract user sentiments toward events or topics, such as those in image tweets, so that prediction of sentiment from visual content is complementary to textual sentiment analysis. Motivated by the needs in leveraging large scale yet noisy training data to solve the extremely challenging problem of image sentiment analysis, we employ Convolutional Neural Networks (CNN). We first design a suitable CNN architecture for image sentiment analysis. We obtain half a million training samples by using a baseline sentiment algorithm to label Flickr images. To make use of such noisy machine labeled data, we employ a progressive strategy to fine-tune the deep network. Furthermore, we improve the performance on Twitter images by inducing domain transfer with a small number of manually labeled Twitter images. We have conducted extensive experiments on manually labeled Twitter images. The results show that the proposed CNN can achieve better performance in image sentiment analysis than competing algorithms.Comment: 9 pages, 5 figures, AAAI 201

Free vibration analysis of thin-walled curved box girder considering shear lag deformation

In order to investigate dynamic shear lag effect, based on energy variation method and Hamilton principle, vibration governed differential equations of curved box girder are deduced by assuming different shear lag warping displacement modes, the explicit solution to bending frequency of curved box girder is worked out with Galerkin method. The results of a numerical example show that shear lag warping displacement functions have a limit influence on vibration frequency; second degree parabola or catenary are the appropriate shear lag warping displacement function of box girder; Compared the theoretical calculating results with numerical results of ANSYS, the error between them is only 0.57 %, they agree very well with each other, which demonstrates the correctness and reliability of theoretical deduction

Towards an Integrative Model of Destination Attachment: Dimensionality and Influence on Revisit Intention

Attachment is considered a universal human experience that occurs throughout the lifecycle, which provides an opportunity for both self-expression as well as connection to others. However, the greatest challenge for attachment researchers is to integrate diverse perspectives and approaches to define the construct. This study synthesized literature from relevant disciplines (i.e., psychology, marketing and human geography) to provide a comprehensive reflection upon the concept of destination attachment. A four-dimension construct of destination attachment was proposed, and its influence on revisit intention was also examined. This study provides an integrated view of the destination attachment definition, and further empirically examines the validity and reliability of the four-dimensional construct

A Study of the Impact of Restaurant Affiliation on Part-time Workers’ Organizational Citizenship Behavior

The purposes for this study are: 1) To examine the influence of the management style of restaurant affiliation (chain and independent restaurants) on the relationships between organization citizenship behavior and three factors: a. part-time employee’s perceived supervisor support, b. part-time employee’s perceived organizational support, and c. organizational commitment; and 2) To compare the different level of work perceptions and organizational commitment between part-time workers of chain restaurants and part-time workers of independent restaurants. Survey was conducted among 185 part-time workers in the restaurant industry. The results indicated that management styles of different restaurant affiliation have influence on part-time workers