Sketch me that shoe
Refereed conference paper presented and published in conference proceedings

香港中文大學研究人員

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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 sketchphoto pairs from two categories with 32,000 fine-grained triplet ranking annotations. We then develop a deep tripletranking 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 finegrained cross-domain ranking tasks.
著者Yu Q., Liu F., Song Y.-Z., Xiang T., Hospedales T.M., Loy C.C.
會議名稱2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
會議開始日26.06.2016
會議完結日01.07.2016
會議地點Las Vegas
會議國家/地區美國
詳細描述organized by IEEE,
出版年份2016
月份1
日期1
卷號2016-January
頁次799 - 807
國際標準書號9781467388511
國際標準期刊號1063-6919
語言英式英語

上次更新時間 2020-02-09 於 01:25