Abstract Textile pattern design is a time‐consuming and tedious work. Mihui, our ongoing developing system, employs deep‐learning techniques to automatically generate huge volumes of patterns with the help of human guidance. However, trained as a black box, Mihui cannot provide customized service for each individual designer who shows unique aesthetics preferences. In this article, we introduce the recommendation module of Mihui. The module forwards all generated pattern images to a deep encoding network, where images are mapped into 128‐dimension vectors. For each user of Mihui, we create a profile by his/her purchased or downloaded history. A novel encoder network is proposed to learn a personal taste vector for each user, based on which, we recommend new patterns to him/her. Our records in Mihui show that the recommendation module effectively improve users' experience on Mihui.

Summary As various algorithms and models spring up, object detection‐based deep learning for image analysis has become an established technology in the field of computer vision. Furthermore, object detection for high‐resolution videos derived from ubiquitous surveillance cameras draws many researchers' attention due to its practical significance and the challenge on detecting accuracy and speed. Existing object detection methods mainly resize and compress each frame in the video and then apply the detection algorithm, but with the method, the detection for small objects in dense scenes is far from satisfactory in terms of accuracy because small objects are reduced to pixel‐level sizes in compressed frame images. In order to fix this issue, we propose AROD, a parallel detection framework based on adaptive image cropping. Unlike the traditional first‐compression‐and‐then‐detection methods, AROD adopts adaptive image cropping for distributed parallel detection. In this way, AROD significantly improves the accuracy of small object detection in dense scenes with acceptable overhead. In our experimental evaluation, YOLOv2‐tiny model equipped with AROD reaches 29.29 FPS along with high detection accuracy. Experimental results verify that AROD ensures the high throughput of real‐time video analytics while maintaining high detection accuracy.

Singing is a popular social activity and a pleasant way of expressing one’s feelings. One important reason for unsuccessful singing performance is because the singer fails to choose a suitable song. In this paper, we propose a novel competence-based song recommendation framework for the purpose of singing. It is distinguished from most existing music recommendation systems which rely on the computation of listeners’ interests or similarity. We model a singer’s vocal competence as a singer profile, which takes voice pitch, intensity, and quality into consideration. Then we propose techniques to acquire singer profiles. We also present a song profile model which is used to construct a human annotated song database. Then we propose a learning-to-rank scheme for recommending songs by a singer profile. Finally, we introduce a reduced singer profile which can greatly simplify the vocal competence modelling process. The experimental study on real singers demonstrates the effectiveness of our approach and its advantages over two baseline methods.

Nowadays, an increasing number of singing enthusiasts upload their cover songs and share their performances in online social singing communities. They can also listen and rate other users' song renderings. An important feature of the social singing communities is to recommend appropriate singing-songs which users are able to perform excellently.

Singing is a popular social activity and a good way of expressing one's feelings. One important reason for unsuccessful singing performance is because the singer fails to choose a suitable song. In this paper, we propose a novel singing competence-based song recommendation framework. It is distinguished from most existing music recommendation systems which rely on the computation of listeners' interests or similarity. We model a singer's vocal competence as singer profile, which takes voice pitch, intensity, and quality into consideration. Then we propose techniques to acquire singer profiles. We also present a song profile model which is used to construct a human annotated song database. Finally, we propose a learning-to-rank scheme for recommending songs by singer profile. The experimental study on real singers demonstrates the effectiveness of our approach and its advantages over two baseline methods. To the best of our knowledge, our work is the first to study competence-based song recommendation.

In this demo, we present myDJ, a karaoke recommendation system which recommends the songs people are capable to sing. Different from the existing song recommendation systems which recommend songs people like to listen, myDJ can recommend proper songs according to a subject's physical phonation area. It consists of a singer profiler to analyze the subject's phonation characters. In addition, the song profile for each song in database is extracted. To learn a ranking function, the learning to rank algorithm Listnet is applied under a list of predefined features extracted from each singer-song profile pair. In the results, proper songs which are suitable but challenging for the subject are recommended.

In this poster, we address the problem of location disambiguation for geotagged Web photo resources. We propose an approach for analyzing and partitioning large geotagged photo collections using geographic and semantic information. By organizing the dataset in a structural scheme, we resolve the location ambiguity and clutter problem yield by massive volume of geotagged photos.