Publications

CVTron Web: A Versatile Framework for Online Computer Vision Services

Published in World Congress on Services, 2018

Recently, computer vision has aroused the greatest interest. Many companies have developed online inference system in the field of computer vision associated with web services, however, it still lacks easy-to-use online training system. This paper introduces CVTron Web system, an open source framework that serves as a web-based system and a dashboard for handling the training task, testing the inference, and checking hardware capabilities. By clicking on the web dashboard, developers or those having little programming knowledge will be able to complete several computer vision tasks such as image classification, object detection, and image segmentation for both training and inference.

Recommended citation: Y Chen, X Yao - World Congress on Services, 2018 https://link.springer.com/chapter/10.1007/978-3-319-94472-2_5

Face Based Advertisement Recommendation with Deep Learning: A Case Study

Published in International Conference on Smart Computing and Communication, 2017

Recently, there is a massive growth of the offline advertising industries. To increase the performance of offline advertising, researchers bring out several methodologies. However, the existing advertisement serving schemes are accustomed to focusing on traditional print media, resulting in the lack of personality and impression. Meanwhile, we find that facial features such as age, gender, can help us classify consumers intuitively and rapidly so that it can raise the accuracy in recommendation in a short time. Motivated by an original idea, we offer a Face Based Advertisement Recommendation System (FBARS). We propose that the FBARS works well in offline scenario and basically it could raise the accuracy 4 times. it performs 4 times better than the classic method using collaborative filtering.

Recommended citation: X Yao, Y Chen, R Liao, S Cai - International Conference on Smart Computing and …, 2017 https://link.springer.com/chapter/10.1007/978-3-319-73830-7_10