activity
20182021
most citedGoing Deeper Into Face Detection: A Survey

51 citations · 186 across the 17 of their papers we have counts for

collaborators

23 papers

cs.CV202151 cited

Going Deeper Into Face Detection: A Survey

Shervin Minaee, Ping Luo, Zhe Lin +1

Face detection is a crucial first step in many facial recognition and face analysis systems. Early approaches for face detection were mainly based on classifiers built on top of ha…

cs.CV20214 cited

Disentangled Cycle Consistency for Highly-realistic Virtual Try-On

Chongjian Ge, Yibing Song, Yuying Ge +3

Image virtual try-on replaces the clothes on a person image with a desired in-shop clothes image. It is challenging because the person and the in-shop clothes are unpaired. Existin…

cs.CV202111 cited

Parser-Free Virtual Try-on via Distilling Appearance Flows

Yuying Ge, Yibing Song, Ruimao Zhang +3

Image virtual try-on aims to fit a garment image (target clothes) to a person image. Prior methods are heavily based on human parsing. However, slightly-wrong segmentation results…

cs.CV202125 cited

Segmenting Transparent Object in the Wild with Transformer

Enze Xie, Wenjia Wang, Wenhai Wang +4

This work presents a new fine-grained transparent object segmentation dataset, termed Trans10K-v2, extending Trans10K-v1, the first large-scale transparent object segmentation data…

cs.CV20214 cited

FAT: Learning Low-Bitwidth Parametric Representation via Frequency-Aware Transformation

Chaofan Tao, Rui Lin, Quan Chen +3

Learning convolutional neural networks (CNNs) with low bitwidth is challenging because performance may drop significantly after quantization. Prior arts often discretize the networ…

cs.CV20206 cited

Do 2D GANs Know 3D Shape? Unsupervised 3D shape reconstruction from 2D Image GANs

Xingang Pan, Bo Dai, Ziwei Liu +2

Natural images are projections of 3D objects on a 2D image plane. While state-of-the-art 2D generative models like GANs show unprecedented quality in modeling the natural image man…