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Interactive Regression and Classification for Dense Object Detector

文献类型：期刊论文

作者Zhou, Linmao1,2; Chang, Hong1,2,3; Ma, Bingpeng1; Shan, Shiguang2
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2022
卷号31页码:3684-3696
关键词Location awareness Detectors Feature extraction Object detection Standards Backpropagation Pipelines Dense object detector localization information interactive
ISSN号1057-7149
DOI10.1109/TIP.2022.3174391
英文摘要In object detection, enhancing feature representation using localization information has been revealed as a crucial procedure to improve detection performance. However, the localization information (i.e., regression feature and regression offset) captured by the regression branch is still not well utilized. In this paper, we propose a simple but effective method called Interactive Regression and Classification (IRC) to better utilize localization information. Specifically, we propose Feature Aggregation Module (FAM) and Localization Attention Module (LAM) to leverage localization information to the classification branch during forward propagation. Furthermore, the classifier also guides the learning of the regression branch during backward propagation, to guarantee that the localization information is beneficial to both regression and classification. Thus, the regression and classification branches are learned in an interactive manner. Our method can be easily integrated into anchor-based and anchor-free object detectors without increasing computation cost. With our method, the performance is significantly improved on many popular dense object detectors, including RetinaNet, FCOS, ATSS, PAA, GFL, GFLV2, OTA, GA-RetinaNet, RepPoints, BorderDet and VFNet. Based on ResNet-101 backbone, IRC achieves 47.2% AP on COCO test-dev, surpassing the previous state-of-the-art PAA (44.8% AP), GFL (45.0% AP) and without sacrificing the efficiency both in training and inference. Moreover, our best model (Res2Net-101-DCN) can achieve a single-model single-scale AP of 51.4%.
资助项目Natural Science Foundation of China (NSFC)[61976203] ; Natural Science Foundation of China (NSFC)[61876171]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000803395500010
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/19586]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Chang, Hong
作者单位1.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol CAS, Beijing 100190, Peoples R China
3.Peng Cheng Lab, Shenzhen 518055, Peoples R China
推荐引用方式
GB/T 7714		 Zhou, Linmao,Chang, Hong,Ma, Bingpeng,et al. Interactive Regression and Classification for Dense Object Detector[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2022,31:3684-3696.
APA Zhou, Linmao,Chang, Hong,Ma, Bingpeng,&Shan, Shiguang.(2022).Interactive Regression and Classification for Dense Object Detector.IEEE TRANSACTIONS ON IMAGE PROCESSING,31,3684-3696.
MLA Zhou, Linmao,et al."Interactive Regression and Classification for Dense Object Detector".IEEE TRANSACTIONS ON IMAGE PROCESSING 31(2022):3684-3696.

入库方式： OAI收割

来源：计算技术研究所

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