Z
Zheng Song
Researcher at National University of Singapore
Publications - 15
Citations - 1818
Zheng Song is an academic researcher from National University of Singapore. The author has contributed to research in topics: Object detection & Feature extraction. The author has an hindex of 14, co-authored 15 publications receiving 1731 citations.
Papers
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Proceedings ArticleDOI
Street-to-shop: cross-scenario clothing retrieval via parts alignment and auxiliary set
TL;DR: This paper addresses a practical problem of cross-scenario clothing retrieval - given a daily human photo captured in general environment, e.g., on street, finding similar clothing in online shops, where the photos are captured more professionally and with clean background.
Proceedings ArticleDOI
Hi, magic closet, tell me what to wear!
TL;DR: This paper collects a large clothing What-to-Wear dataset, and thoroughly annotates the whole dataset with 7 multi-value clothing attributes and 10 occasion categories via Amazon Mechanic Turk, to learn a generalize-well model and comprehensively evaluate it.
Proceedings ArticleDOI
Street-to-shop: Cross-scenario clothing retrieval via parts alignment and auxiliary set
TL;DR: This paper proposes a two-step calculation to obtain more reliable one-to-many similarities between the query daily photo and online shopping photos, and concludes that the extensive experimental evaluations on the collected datasets well demonstrate the effectiveness of the proposed framework for cross-scenario clothing retrieval.
Proceedings ArticleDOI
Contextualizing object detection and classification
TL;DR: This paper adopts a new method for adaptive context modeling and iterative boosting that achieves the state-of-the-art performance on object classification and detection tasks of PASCAL Visual Object Classes Challenge (VOC) 2007, 2010 and SUN09 data sets.
Journal ArticleDOI
Contextualizing Object Detection and Classification
TL;DR: This paper adopts a new method for adaptive context modeling and iterative boosting that achieves the state-of-the-art performance on object classification and detection tasks of PASCAL Visual Object Classes Challenge (VOC) 2007, 2010 and SUN09 data sets.