{"title":"学习自适应转移和任务解耦,实现分辨式一步人员搜索","authors":"","doi":"10.1016/j.knosys.2024.112483","DOIUrl":null,"url":null,"abstract":"<div><p>Mainstream person search models aim to jointly optimize person detection and re-identification (ReID) in a one-step manner. Despite notable progress, existing one-step person search models still face three major challenges in extracting discriminative features: 1) incomplete feature extraction and fusion hinder the effective utilization of multiscale information, 2) the models struggle to capture critical features in complex occlusion scenarios, and 3) the optimization objectives of person detection and ReID are in conflict in the shared feature space. To address these issues, this study proposes a novel adaptive shift and task decoupling (ASTD) method that aims to enhance the accuracy and robustness of extracting discriminative features within the region of interest. In particular, we introduce a scale-aware transformer to handle scale/pose variations and occlusions. This transformer incorporates scale-aware modulation to enhance the utilization of multiscale information and adaptive shift augmentation to learn adaptation to occlusions dynamically. In addition, we design a task decoupling mechanism to hierarchically learn independent task representations using orthogonal loss to decouple two subtasks during training. Experimental results show that ASTD achieves state-of-the-art performance on the CUHK-SYSU and PRW datasets. Our code is accessible at <span><span>https://github.com/zqx951102/ASTD</span><svg><path></path></svg></span>.</p></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Learning adaptive shift and task decoupling for discriminative one-step person search\",\"authors\":\"\",\"doi\":\"10.1016/j.knosys.2024.112483\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Mainstream person search models aim to jointly optimize person detection and re-identification (ReID) in a one-step manner. Despite notable progress, existing one-step person search models still face three major challenges in extracting discriminative features: 1) incomplete feature extraction and fusion hinder the effective utilization of multiscale information, 2) the models struggle to capture critical features in complex occlusion scenarios, and 3) the optimization objectives of person detection and ReID are in conflict in the shared feature space. To address these issues, this study proposes a novel adaptive shift and task decoupling (ASTD) method that aims to enhance the accuracy and robustness of extracting discriminative features within the region of interest. In particular, we introduce a scale-aware transformer to handle scale/pose variations and occlusions. This transformer incorporates scale-aware modulation to enhance the utilization of multiscale information and adaptive shift augmentation to learn adaptation to occlusions dynamically. In addition, we design a task decoupling mechanism to hierarchically learn independent task representations using orthogonal loss to decouple two subtasks during training. Experimental results show that ASTD achieves state-of-the-art performance on the CUHK-SYSU and PRW datasets. Our code is accessible at <span><span>https://github.com/zqx951102/ASTD</span><svg><path></path></svg></span>.</p></div>\",\"PeriodicalId\":49939,\"journal\":{\"name\":\"Knowledge-Based Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.2000,\"publicationDate\":\"2024-09-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Knowledge-Based Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0950705124011171\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Knowledge-Based Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0950705124011171","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Learning adaptive shift and task decoupling for discriminative one-step person search
Mainstream person search models aim to jointly optimize person detection and re-identification (ReID) in a one-step manner. Despite notable progress, existing one-step person search models still face three major challenges in extracting discriminative features: 1) incomplete feature extraction and fusion hinder the effective utilization of multiscale information, 2) the models struggle to capture critical features in complex occlusion scenarios, and 3) the optimization objectives of person detection and ReID are in conflict in the shared feature space. To address these issues, this study proposes a novel adaptive shift and task decoupling (ASTD) method that aims to enhance the accuracy and robustness of extracting discriminative features within the region of interest. In particular, we introduce a scale-aware transformer to handle scale/pose variations and occlusions. This transformer incorporates scale-aware modulation to enhance the utilization of multiscale information and adaptive shift augmentation to learn adaptation to occlusions dynamically. In addition, we design a task decoupling mechanism to hierarchically learn independent task representations using orthogonal loss to decouple two subtasks during training. Experimental results show that ASTD achieves state-of-the-art performance on the CUHK-SYSU and PRW datasets. Our code is accessible at https://github.com/zqx951102/ASTD.
期刊介绍:
Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.