Deep metric learning: A survey
M Kaya, HŞ Bilge - Symmetry, 2019 - mdpi.com
Metric learning aims to measure the similarity among samples while using an optimal
distance metric for learning tasks. Metric learning methods, which generally use a linear …
distance metric for learning tasks. Metric learning methods, which generally use a linear …
Random erasing data augmentation
In this paper, we introduce Random Erasing, a new data augmentation method for training
the convolutional neural network (CNN). In training, Random Erasing randomly selects a …
the convolutional neural network (CNN). In training, Random Erasing randomly selects a …
In defense of the triplet loss for person re-identification
In the past few years, the field of computer vision has gone through a revolution fueled
mainly by the advent of large datasets and the adoption of deep convolutional neural …
mainly by the advent of large datasets and the adoption of deep convolutional neural …
Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline)
Employing part-level features offers fine-grained information for pedestrian image
description. A prerequisite of part discovery is that each part should be well located. Instead …
description. A prerequisite of part discovery is that each part should be well located. Instead …
Unlabeled samples generated by gan improve the person re-identification baseline in vitro
The main contribution of this paper is a simple semi-supervised pipeline that only uses the
original training set without collecting extra data. It is challenging in 1) how to obtain more …
original training set without collecting extra data. It is challenging in 1) how to obtain more …
Towards real-time multi-object tracking
Modern multiple object tracking (MOT) systems usually follow the tracking-by-detection
paradigm. It has 1) a detection model for target localization and 2) an appearance …
paradigm. It has 1) a detection model for target localization and 2) an appearance …
Part-based pseudo label refinement for unsupervised person re-identification
Unsupervised person re-identification (re-ID) aims at learning discriminative representations
for person retrieval from unlabeled data. Recent techniques accomplish this task by using …
for person retrieval from unlabeled data. Recent techniques accomplish this task by using …
Learning discriminative features with multiple granularities for person re-identification
G Wang, Y Yuan, X Chen, J Li, X Zhou - Proceedings of the 26th ACM …, 2018 - dl.acm.org
The combination of global and partial features has been an essential solution to improve
discriminative performances in person re-identification (Re-ID) tasks. Previous part-based …
discriminative performances in person re-identification (Re-ID) tasks. Previous part-based …
Diverse part discovery: Occluded person re-identification with part-aware transformer
Occluded person re-identification (Re-ID) is a challenging task as persons are frequently
occluded by various obstacles or other persons, especially in the crowd scenario. To …
occluded by various obstacles or other persons, especially in the crowd scenario. To …