Anran Zhang

Cross-Domain Attention Network for Unsupervised Domain Adaptation Crowd Counting

Anran Zhang, Jun Xu, Xiaoyan Luo, Xianbin Cao et autres

Unsupervised domain adaptation crowd counting (UDACC) has been studied with practical research utility by getting rid of the labeling burden on large-scale dense crowds in the target domain. Current methods generalize well within the specific domain gap by directly aligning domain distributions …

cn, nl (code pays fourni par la source)

Latent Domain Generation for Unsupervised Domain Adaptation Object Counting

Anran Zhang, Yandan Yang, Jun Xu, Xianbin Cao et autres

Unsupervised cross-domain crowd counting has recently received great attention in computer vision, which generalizes the model from the source domain to the unlabeled target domain. However, it is an extremely challenging task because only unlabeled data is available from the target domain …

cn, nl, gb, ae (code pays fourni par la source)

Model-Agnostic Metric for Zero-Shot Learning

Jiayi Shen, Haochen Wang, Anran Zhang, Qiang Qiu et autres

Zero-shot Learning (ZSL) aims to learn a classifier to recognize unseen categories without training samples. Most ZSL works based on embedding models handle the visual space and the semantic space through a common metric space and then apply a simple nearest neighbor …

cn, us, ae (code pays fourni par la source)

Multi-scale Supervised Attentive Encoder-Decoder Network for Crowd Counting

Anran Zhang, Xiaolong Jiang, Baochang Zhang, Xianbin Cao

Crowd counting is a popular topic with widespread applications. Currently, the biggest challenge to crowd counting is large-scale variation in objects. In this article, we focus on overcoming this challenge by proposing a novel Attentive Encoder-Decoder Network (AEDN), which is supervised on …

cn (code pays fourni par la source)

Design and Realization of Low Power Instant Hot WaterFaucet Based on SCM

Guangsheng Liang, Anran Zhang

Relational Attention Network for Crowd Counting

Anran Zhang, Jiayi Shen, Zehao Xiao, Fan Zhu et autres

Crowd counting is receiving rapidly growing research interests due to its potential application value in numerous real-world scenarios. However, due to various challenges such as occlusion, insufficient resolution and dynamic backgrounds, crowd counting remains an unsolved problem in computer vision. Density estimation …

cn, ae (code pays fourni par la source)

Attentional Neural Fields for Crowd Counting

Anran Zhang, Lei Yue, Jiayi Shen, Fan Zhu et autres

Crowd counting has recently generated huge popularity in computer vision, and is extremely challenging due to the huge scale variations of objects. In this paper, we propose the Attentional Neural Field (ANF) for crowd counting via density estimation. Within the encoder-decoder network, …

cn, ae (code pays fourni par la source)

Multi-Scale Aggregation Network for Direct Face Alignment

Peizhao Li, Anran Zhang, Lei Yue, Xiantong Zhen et autres

Face alignment has been extensively researched in computer vision while remaining a challenging task. Direct face alignment based on convolutional neural networks (CNN) without relying on cascaded regression has recently emerged and achieved promising performance. In this paper, we propose a multi-scale …

cn (code pays fourni par la source)