most citedSpatial-Aware Non-Local Attention for Fashion Landmark Detection

3 citations · 4 across the 2 of their papers we have counts for

collaborators

6 papers

cs.CV20201 cited

PipeNet: Selective Modal Pipeline of Fusion Network for Multi-Modal Face Anti-Spoofing

Qing Yang, Xia Zhu, Jong-Kae Fwu +3

Face anti-spoofing has become an increasingly important and critical security feature for authentication systems, due to rampant and easily launchable presentation attacks. Address…

cs.CV2020

Channel Pruning via Optimal Thresholding

Yun Ye, Ganmei You, Jong-Kae Fwu +3

Structured pruning, especially channel pruning is widely used for the reduced computational cost and the compatibility with off-the-shelf hardware devices. Among existing works, we…

cs.CV2019

Human Mesh Recovery from Monocular Images via a Skeleton-disentangled Representation

Sun Yu, Ye Yun, Liu Wu +3

We describe an end-to-end method for recovering 3D human body mesh from single images and monocular videos. Different from the existing methods try to obtain all the complex 3D pos…

cs.CV2019

Outfit Compatibility Prediction and Diagnosis with Multi-Layered Comparison Network

Xin Wang, Bo Wu, Yun Ye +1

Existing works about fashion outfit compatibility focus on predicting the overall compatibility of a set of fashion items with their information from different modalities. However,…

cs.CV2019

Hard-Aware Fashion Attribute Classification

Yun Ye, Yixin Li, Bo Wu +3

Fashion attribute classification is of great importance to many high-level tasks such as fashion item search, fashion trend analysis, fashion recommendation, etc. The task is chall…

cs.CV20193 cited

Spatial-Aware Non-Local Attention for Fashion Landmark Detection

Yixin Li, Shengqin Tang, Yun Ye +1

Fashion landmark detection is a challenging task even using the current deep learning techniques, due to the large variation and non-rigid deformation of clothes. In order to tackl…