228 research outputs found
Person Re-Identification by Deep Joint Learning of Multi-Loss Classification
Existing person re-identification (re-id) methods rely mostly on either
localised or global feature representation alone. This ignores their joint
benefit and mutual complementary effects. In this work, we show the advantages
of jointly learning local and global features in a Convolutional Neural Network
(CNN) by aiming to discover correlated local and global features in different
context. Specifically, we formulate a method for joint learning of local and
global feature selection losses designed to optimise person re-id when using
only generic matching metrics such as the L2 distance. We design a novel CNN
architecture for Jointly Learning Multi-Loss (JLML) of local and global
discriminative feature optimisation subject concurrently to the same re-id
labelled information. Extensive comparative evaluations demonstrate the
advantages of this new JLML model for person re-id over a wide range of
state-of-the-art re-id methods on five benchmarks (VIPeR, GRID, CUHK01, CUHK03,
Market-1501).Comment: Accepted by IJCAI 201
An Evaluation of Deep CNN Baselines for Scene-Independent Person Re-Identification
In recent years, a variety of proposed methods based on deep convolutional
neural networks (CNNs) have improved the state of the art for large-scale
person re-identification (ReID). While a large number of optimizations and
network improvements have been proposed, there has been relatively little
evaluation of the influence of training data and baseline network architecture.
In particular, it is usually assumed either that networks are trained on
labeled data from the deployment location (scene-dependent), or else adapted
with unlabeled data, both of which complicate system deployment. In this paper,
we investigate the feasibility of achieving scene-independent person ReID by
forming a large composite dataset for training. We present an in-depth
comparison of several CNN baseline architectures for both scene-dependent and
scene-independent ReID, across a range of training dataset sizes. We show that
scene-independent ReID can produce leading-edge results, competitive with
unsupervised domain adaption techniques. Finally, we introduce a new dataset
for comparing within-camera and across-camera person ReID.Comment: To be published in 2018 15th Conference on Computer and Robot Vision
(CRV
PRSNet: A Masked Self-Supervised Learning Pedestrian Re-Identification Method
In recent years, self-supervised learning has attracted widespread academic
debate and addressed many of the key issues of computer vision. The present
research focus is on how to construct a good agent task that allows for
improved network learning of advanced semantic information on images so that
model reasoning is accelerated during pre-training of the current task. In
order to solve the problem that existing feature extraction networks are
pre-trained on the ImageNet dataset and cannot extract the fine-grained
information in pedestrian images well, and the existing pre-task of contrast
self-supervised learning may destroy the original properties of pedestrian
images, this paper designs a pre-task of mask reconstruction to obtain a
pre-training model with strong robustness and uses it for the pedestrian
re-identification task. The training optimization of the network is performed
by improving the triplet loss based on the centroid, and the mask image is
added as an additional sample to the loss calculation, so that the network can
better cope with the pedestrian matching in practical applications after the
training is completed. This method achieves about 5% higher mAP on Marker1501
and CUHK03 data than existing self-supervised learning pedestrian
re-identification methods, and about 1% higher for Rank1, and ablation
experiments are conducted to demonstrate the feasibility of this method. Our
model code is located at https://github.com/ZJieX/prsnet
- …