1,222 research outputs found
A Deep Four-Stream Siamese Convolutional Neural Network with Joint Verification and Identification Loss for Person Re-detection
State-of-the-art person re-identification systems that employ a triplet based
deep network suffer from a poor generalization capability. In this paper, we
propose a four stream Siamese deep convolutional neural network for person
redetection that jointly optimises verification and identification losses over
a four image input group. Specifically, the proposed method overcomes the
weakness of the typical triplet formulation by using groups of four images
featuring two matched (i.e. the same identity) and two mismatched images. This
allows us to jointly increase the interclass variations and reduce the
intra-class variations in the learned feature space. The proposed approach also
optimises over both the identification and verification losses, further
minimising intra-class variation and maximising inter-class variation,
improving overall performance. Extensive experiments on four challenging
datasets, VIPeR, CUHK01, CUHK03 and PRID2011, demonstrates that the proposed
approach achieves state-of-the-art performance.Comment: Published in WACV 201
Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification
Person re-identification (re-id) aims to match pedestrians observed by
disjoint camera views. It attracts increasing attention in computer vision due
to its importance to surveillance system. To combat the major challenge of
cross-view visual variations, deep embedding approaches are proposed by
learning a compact feature space from images such that the Euclidean distances
correspond to their cross-view similarity metric. However, the global Euclidean
distance cannot faithfully characterize the ideal similarity in a complex
visual feature space because features of pedestrian images exhibit unknown
distributions due to large variations in poses, illumination and occlusion.
Moreover, intra-personal training samples within a local range are robust to
guide deep embedding against uncontrolled variations, which however, cannot be
captured by a global Euclidean distance. In this paper, we study the problem of
person re-id by proposing a novel sampling to mine suitable \textit{positives}
(i.e. intra-class) within a local range to improve the deep embedding in the
context of large intra-class variations. Our method is capable of learning a
deep similarity metric adaptive to local sample structure by minimizing each
sample's local distances while propagating through the relationship between
samples to attain the whole intra-class minimization. To this end, a novel
objective function is proposed to jointly optimize similarity metric learning,
local positive mining and robust deep embedding. This yields local
discriminations by selecting local-ranged positive samples, and the learned
features are robust to dramatic intra-class variations. Experiments on
benchmarks show state-of-the-art results achieved by our method.Comment: Published on Pattern Recognitio
Positive Pair Distillation Considered Harmful: Continual Meta Metric Learning for Lifelong Object Re-Identification
Lifelong object re-identification incrementally learns from a stream of
re-identification tasks. The objective is to learn a representation that can be
applied to all tasks and that generalizes to previously unseen
re-identification tasks. The main challenge is that at inference time the
representation must generalize to previously unseen identities. To address this
problem, we apply continual meta metric learning to lifelong object
re-identification. To prevent forgetting of previous tasks, we use knowledge
distillation and explore the roles of positive and negative pairs. Based on our
observation that the distillation and metric losses are antagonistic, we
propose to remove positive pairs from distillation to robustify model updates.
Our method, called Distillation without Positive Pairs (DwoPP), is evaluated on
extensive intra-domain experiments on person and vehicle re-identification
datasets, as well as inter-domain experiments on the LReID benchmark. Our
experiments demonstrate that DwoPP significantly outperforms the
state-of-the-art. The code is here: https://github.com/wangkai930418/DwoPP_codeComment: BMVC 202
Continual representation learning for biometric identification
With the explosion of digital data in recent years, continuously learning new
tasks from a stream of data without forgetting previously acquired knowledge
has become increasingly important. In this paper, we propose a new continual
learning (CL) setting, namely ``continual representation learning'', which
focuses on learning better representation in a continuous way. We also provide
two large-scale multi-step benchmarks for biometric identification, where the
visual appearance of different classes are highly relevant. In contrast to
requiring the model to recognize more learned classes, we aim to learn feature
representation that can be better generalized to not only previously unseen
images but also unseen classes/identities. For the new setting, we propose a
novel approach that performs the knowledge distillation over a large number of
identities by applying the neighbourhood selection and consistency relaxation
strategies to improve scalability and flexibility of the continual learning
model. We demonstrate that existing CL methods can improve the representation
in the new setting, and our method achieves better results than the
competitors
- …