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Xintong Han
Researcher at University of Maryland, College Park
Publications - 66
Citations - 5088
Xintong Han is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Computer science & Object detection. The author has an hindex of 20, co-authored 59 publications receiving 2708 citations. Previous affiliations of Xintong Han include Shanghai Jiao Tong University.
Papers
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Proceedings ArticleDOI
NISP: Pruning Networks Using Neuron Importance Score Propagation
Ruichi Yu,Ang Li,Chun-Fu Chen,Jui-Hsin Lai,Vlad I. Morariu,Xintong Han,Mingfei Gao,Ching-Yung Lin,Larry S. Davis +8 more
TL;DR: Zhang et al. as mentioned in this paper proposed the Neuron Importance Score Propagation (NISP) algorithm to propagate the importance scores of final responses to every neuron in the network.
Proceedings ArticleDOI
Multi-Similarity Loss With General Pair Weighting for Deep Metric Learning
TL;DR: In this article, a general pair weighting (GPW) framework has been proposed, which casts the sampling problem of deep metric learning into a unified view through gradient analysis, providing a powerful tool for understanding recent pair-based loss functions.
Proceedings ArticleDOI
VITON: An Image-Based Virtual Try-on Network
TL;DR: An image-based VIirtual Try-On Network (VITON) without using 3D information in any form is presented, which seamlessly transfers a desired clothing item onto the corresponding region of a person using a coarse-to-fine strategy.
Proceedings ArticleDOI
Two-Stream Neural Networks for Tampered Face Detection
TL;DR: Wang et al. as mentioned in this paper proposed a two-stream network for face tampering detection, which trains a GoogLeNet to detect tampering artifacts in a face classification stream, and train a patch based triplet network to leverage features capturing local noise residuals and camera characteristics as a second stream.
Proceedings ArticleDOI
Learning Fashion Compatibility with Bidirectional LSTMs
TL;DR: Zhang et al. as discussed by the authors proposed to jointly learn a visual-semantic embedding and the compatibility relationships among fashion items in an end-to-end fashion, which can not only perform the aforementioned recommendations effectively but also predict the compatibility of a given outfit.