This paper presents a demo of our novel approach to improve single-image super-resolution methods by integrating trainable regularization techniques. Recent advancements, such as the noise Enhanced Super Resolution Generative Adversarial Network Plus (nESRGAN+), have shown promising results in enhancing the performance of ESRGAN. However, despite its success, nESRGAN+ still faces limitations in perceptual quality due to the absence of detailed hallucinations and the presence of unwanted artifacts with slow convergence rates. To address these challenges, we propose the integration of multiple parametric regularization algorithms, enabling iterative adjustment of network gradients. Through a series of experiments, we demonstrate that our approach yields high-quality reconstructed images, effectively restoring complex textures even in previously unseen scenarios. Moreover, the introduced loss functions contribute to accelerated convergence rates and substantial improvements in the visual fidelity of the reconstructed outputs. Our online demo system can accept input images and show the super-resolution images using our method and the two state-of-the-art methods.

In recent years, machine learning has been widely used in the real estate field. However, most of these previous studies have been limited to analysis based on objective perspectives, such as analysis of the structure of the floor plan and rent estimation. On the other hand, we focus on the subjective "living comfort" of real estate properties and aim to predict people's impressions of properties based on information obtained from floor plan images. Specifically, by using deep learning to analyze floor plan images and graph structures reflecting the floor plans, it becomes possible to predict the attractiveness of each property in terms of spaciousness, modernity, privacy, and so on. As a result of the experiments, the effectiveness of using both the floor plan image and the corresponding graph structure for prediction was confirmed.

Cover Caption: The cover image is based on the Research Article Efficient virtual data search for annotationfree vehicle reidentification by Zhijing Wan et al., https://doi.org/10.1002/int.22829.

Vehicle reidentification (re-ID) is the task of retrieving the same vehicle across nonoverlapping cameras, which has made significant progress with the help of abundant manually annotated real images. To avoid the time-consuming and tedious labeling of real images, virtual data sets with large-scale synthetic images have recently been constructed to perform annotation-free model training. However, current methods fail to exploit the potential of virtual data search, that is, searching valuable and representative virtual subdata set for efficient training. This paper presents a novel data sampling strategy from both semantic and feature levels to perform an effective data search. The semantic level determines the sample number of each vehicle identity via the consistency constraint of attribute distribution for source domain and target domain; while the feature level searches valuable and representative samples of each vehicle identity. To our knowledge, we are among the first attempts to search effective virtual data to perform annotation-free vehicle re-ID. Extensive cross-domain experiments from virtual vehicle re-ID data sets to real vehicle re-ID data sets show that our data sampling strategy can significantly reduce the training data volume and even boost the re-ID performance.

The International Joint Workshop on Multimedia Artworks Analysis and Attractiveness Computing in Multimedia (MMArt-ACM) solicits contributions on methodology advancement and novel applications of multimedia artworks and attractiveness computing that emerge in the era of big data and deep learning. Despite the strike of the Covid-19 pandemic, this workshop attracts submissions of diverse topics in these two fields, and the workshop program finally consists of five presented papers. The topics cover image retrieval, image transformation and generation, recommendation system, and image/video summarization. The actual MMArt-ACM'20 Proceedings are available in the ACM DL at: https://dl.acm.org/citation.cfm?id=3379173

Conventional video summarization approaches based on reinforcement learning have the problem that the reward can only be received after the whole summary is generated. Such kind of reward is sparse and it makes reinforcement learning hard to converge. Another problem is that labelling each shot is tedious and costly, which usually prohibits the construction of large-scale datasets. To solve these problems, we propose a weakly supervised hierarchical reinforcement learning framework, which decomposes the whole task into several subtasks to enhance the summarization quality. This framework consists of a manager network and a worker network. For each subtask, the manager is trained to set a subgoal only by a task-level binary label, which requires much fewer labels than conventional approaches. With the guide of the subgoal, the worker predicts the importance scores for video shots in the subtask by policy gradient according to both global reward and innovative defined sub-rewards to overcome the sparse problem. Experiments on two benchmark datasets show that our proposal has achieved the best performance, even better than supervised approaches.

No abstract available.

In this paper, we propose a new measure to estimate the similarity between brands via posts of brands' followers on social network services (SNS). Our method was developed with the intention of exploring the brands that customers are likely to jointly purchase. Nowadays, brands use social media for targeted advertising because influencing users' preferences can greatly affect the trends in sales. We assume that data on SNS allows us to make quantitative comparisons between brands. Our proposed algorithm analyzes the daily photos and hashtags posted by each brand's followers. By clustering them and converting them to histograms, we can calculate the similarity between brands. We evaluated our proposed algorithm with purchase logs, credit card information, and answers to the questionnaires. The experimental results show that the purchase data maintained by a mall or a credit card company can predict the co-purchase very well, but not the customer's willingness to buy products of new brands. On the other hand, our method can predict the users' interest on brands with a correlation value over 0.53, which is pretty high considering that such interest to brands are high subjective and individual dependent.

Online dating services have become popular in modern society. Pair matching prediction between two users in these services can help efficiently increase the possibility of finding their life partners. Deep learning based methods with automatic feature interaction functions such as Factorization Machines (FM) and cross network of Deep & Cross Network (DCN) can model sparse categorical features, which are effective to many recommendation tasks of web applications. To solve the partner recommendation task, we improve these FM-based deep models and DCN by enhancing the representation of feature interaction embedding and proposing a novel design of interaction layer avoiding information loss. Through the experiments on two real-world datasets of two online dating companies, we demonstrate the superior performances of our proposed designs.