6 citations · 12 across the 5 of their papers we have counts for
5 papers · 1 filter
Glance to Count: Learning to Rank with Anchors for Weakly-supervised Crowd Counting
Zheng Xiong, Liangyu Chai, Wenxi Liu +3
Crowd image is arguably one of the most laborious data to annotate. In this paper, we devote to reduce the massive demand of densely labeled crowd data, and propose a novel weakly-…
High-resolution Face Swapping via Latent Semantics Disentanglement
Yangyang Xu, Bailin Deng, Junle Wang +3
We present a novel high-resolution face swapping method using the inherent prior knowledge of a pre-trained GAN model. Although previous research can leverage generative priors to…
From Continuity to Editability: Inverting GANs with Consecutive Images
Yangyang Xu, Yong Du, Wenpeng Xiao +2
Existing GAN inversion methods are stuck in a paradox that the inverted codes can either achieve high-fidelity reconstruction, or retain the editing capability. Having only one of…
Reducing Spatial Labeling Redundancy for Semi-supervised Crowd Counting
Yongtuo Liu, Sucheng Ren, Liangyu Chai +4
Labeling is onerous for crowd counting as it should annotate each individual in crowd images. Recently, several methods have been proposed for semi-supervised crowd counting to red…
Co-advise: Cross Inductive Bias Distillation
Sucheng Ren, Zhengqi Gao, Tianyu Hua +4
Transformers recently are adapted from the community of natural language processing as a promising substitute of convolution-based neural networks for visual learning tasks. Howeve…