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20160413-11 于茜: Sketch Me that Shoe

2016-4-9 17:02| 发布者: 姬艳丽T成电| 查看: 7868| 评论: 0

摘要: 报告嘉宾:于茜 (Queen Mary University of London) 报告时间:2016年4月13日(周三)晚20:00(北京时间) 报告题目:Sketch Me that Shoe 主持人:杨恒

报告嘉宾:于茜 (Queen Mary University of London)

报告时间:2016413日(周三)晚2000(北京时间)

报告题目:Sketch Me that Shoe

主持人:杨恒

 

报告摘要:

We investigate the problem of fine-grained sketch-based image retrieval (SBIR), where free-hand human sketches are used as queries to perform instance-level retrieval of images. This is an extremely challenging task because (i) visual comparisons not only need to be fine-grained but also executed cross-domain, (ii) free-hand (finger) sketches are highly abstract, making fine-grained matching harder, and most importantly (iii) annotated cross-domain sketch-photo datasets required for training are scarce, challenging many state-of-the-art machine learning techniques.

In this paper, for the first time, we address all these challenges, providing a step towards the capabilities that would underpin a commercial sketch-based image retrieval application. We introduce a new database of 1,432 sketch- photo pairs from two categories with 32,000 fine-grained triplet ranking annotations. We then develop a deep triplet- ranking model for instance-level SBIR with a novel data augmentation and staged pre-training strategy to alleviate the issue of insufficient fine-grained training data. Extensive experiments are carried out to contribute a variety of insights into the challenges of data sufficiency and over-fitting avoidance when training deep networks for fine-grained cross-domain ranking tasks.

参考文献:

[1] Q. Yu, F. Liu, Y. Song, T. Xiang, and T. Hospedales, C. C. Loy. Sketch Me that Shoe. In CVPR, 2016. (Oral)

[2] Q. Yu, Y. Yang, Y. Song, T. Xiang, and T. Hospedales. Sketch-a-net that beats humans. In BMVC, 2015. (Best science paper)


报告人简介:

Qian Yu is a second year Ph.D. student in Computer Vision group at Queen Mary University of London, advised by Dr. Tao Xiang and Dr. Yi-Zhe Song. Her research focuses on sketch recognition and sketch-based image retrieval. She is also interested in machine learning and human computer interaction.

Before joining the Computer Vision group of QMUL, she graduated from Beijing University of Posts and Telecommunications with a B.S. in Telecommunications Engineering with Management in July 2014.

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