判别式
相关性
行人
人工智能
模式识别(心理学)
特征(语言学)
行人检测
计算机科学
保险丝(电气)
数据挖掘
模态(人机交互)
机器学习
数学
工程类
运输工程
电气工程
几何学
哲学
语言学
作者
Baoan Li,Long Zhang,Shangzhi Teng,Xueqiang Lyu
出处
期刊:Research Square - Research Square
日期:2024-04-24
标识
DOI:10.21203/rs.3.rs-4292609/v1
摘要
Abstract The main goal of Pedestrian Attribute Recognition (PAR) is to identify various attributes of pedestrians captured in video surveillance. Due to the numerous categories of pedestrian attribute labels, the complex and easily overlooked correlations among attributes, PAR is a challenging task. Traditional methods usually treat each attribute independently, ignoring the possible intrinsic correlations between attributes.We design a pedestrian attribute recognition network ACMFNet which can fuse pedestrian attributes uniqueness features and attribute correlation features. Specifically, we propose an attribute correlation query module (ACQM), which are used to learn discriminative attribute features. Then, we construct a mask fusion module (MFM) to automatically learn the importance of the image feature and attribute correlation feature. To better distinguish the modality differences between images and attribute texts, we propose modality prompt. Experimental results show that our method can significantly enhance the network’s ability to recognize pedestrian attributes. On three pedestrian attribute recognition datasets PA100K, PETA, and UAV-Human, our proposed method shows competitive performance compared to the state-of-the-art methods. Our source code is available at \url{https://github.com/luffy-op/ACMFNet.
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