End-to-end training of CNN ensembles for person re-identification
نویسندگان
چکیده
منابع مشابه
End-to-end DNN-CNN Classification for Language Identification
A defining problem in spoken language identification (LID) is how to design effective representations which allow features to be extracted that are specific to language information. Recent advances in deep neural networks for feature extraction have led to significant improvements in results, with deep end-to-end methods proving effective. In this paper, a novel network is proposed and explored...
متن کاملEnd-to-End Detection and Re-identification Integrated Net for Person Search
This paper proposes a pedestrian detection and reidentification (re-id) integration net (I-Net) in an end-to-end learning framework. The I-Net is used in real-world video surveillance scenarios, where the target person needs to be searched in the whole scene videos, while the annotations of pedestrian bounding boxes are unavailable. By comparing to the successful CVPR’17 work [Xiao et al., 2017...
متن کاملVirtual CNN Branching: Efficient Feature Ensemble for Person Re-Identification
In this paper we introduce an ensemble method for convolutional neural network (CNN), called “virtual branching,” which can be implemented with nearly no additional parameters and computation on top of standard CNNs. We propose our method in the context of person re-identification (re-ID). Our CNN model consists of shared bottom layers, followed by “virtual” branches, where neurons from a block...
متن کاملFault Identification using end-to-end data by imperialist competitive algorithm
Faults in computer networks may result in millions of dollars in cost. Faults in a network need to be localized and repaired to keep the health of the network. Fault management systems are used to keep today’s complex networks running without significant cost, either by using active techniques or passive techniques. In this paper, we propose a novel approach based on imperialist competitive alg...
متن کاملEnd-to-End Deep Learning for Person Search
Existing person re-identification (re-id) benchmarks and algorithms mainly focus on matching cropped pedestrian images between queries and candidates. However, it is different from real-world scenarios where the annotations of pedestrian bounding boxes are unavailable and the target person needs to be found from whole images. To close the gap, we investigate how to localize and match query pers...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Pattern Recognition
سال: 2020
ISSN: 0031-3203
DOI: 10.1016/j.patcog.2020.107319