|
Records |
Links |
|
Author |
Svebor Karaman; Giuseppe Lisanti; Andrew Bagdanov; Alberto del Bimbo |
|
|
Title |
Leveraging local neighborhood topology for large scale person re-identification |
Type |
Journal Article |
|
Year |
2014 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
|
|
Volume |
47 |
Issue |
12 |
Pages |
3767–3778 |
|
|
Keywords |
Re-identification; Conditional random field; Semi-supervised; ETHZ; CAVIAR; 3DPeS; CMV100 |
|
|
Abstract |
In this paper we describe a semi-supervised approach to person re-identification that combines discriminative models of person identity with a Conditional Random Field (CRF) to exploit the local manifold approximation induced by the nearest neighbor graph in feature space. The linear discriminative models learned on few gallery images provides coarse separation of probe images into identities, while a graph topology defined by distances between all person images in feature space leverages local support for label propagation in the CRF. We evaluate our approach using multiple scenarios on several publicly available datasets, where the number of identities varies from 28 to 191 and the number of images ranges between 1003 and 36 171. We demonstrate that the discriminative model and the CRF are complementary and that the combination of both leads to significant improvement over state-of-the-art approaches. We further demonstrate how the performance of our approach improves with increasing test data and also with increasing amounts of additional unlabeled data. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
LAMP; 601.240; 600.079 |
Approved |
no |
|
|
Call Number |
Admin @ si @ KLB2014a |
Serial |
2522 |
|
Permanent link to this record |
|
|
|
|
Author |
Qingshan Chen; Zhenzhen Quan; Yujun Li; Chao Zhai; Mikhail Mozerov |
|
|
Title |
An Unsupervised Domain Adaption Approach for Cross-Modality RGB-Infrared Person Re-Identification |
Type |
Journal Article |
|
Year |
2023 |
Publication |
IEEE Sensors Journal |
Abbreviated Journal |
IEEE-SENS |
|
|
Volume |
23 |
Issue |
24 |
Pages |
|
|
|
Keywords |
Q. Chen, Z. Quan, Y. Li, C. Zhai and M. G. Mozerov |
|
|
Abstract |
Dual-camera systems commonly employed in surveillance serve as the foundation for RGB-infrared (IR) cross-modality person re-identification (ReID). However, significant modality differences give rise to inferior performance compared to single-modality scenarios. Furthermore, most existing studies in this area rely on supervised training with meticulously labeled datasets. Labeling RGB-IR image pairs is more complex than labeling conventional image data, and deploying pretrained models on unlabeled datasets can lead to catastrophic performance degradation. In contrast to previous solutions that focus solely on cross-modality or domain adaptation issues, this article presents an end-to-end unsupervised domain adaptation (UDA) framework for the cross-modality person ReID, which can simultaneously address both of these challenges. This model employs source domain classes, target domain clusters, and unclustered instance samples for the training, maximizing the comprehensive use of the dataset. Moreover, it addresses the problem of mismatched clustering labels between the two modalities in the target domain by incorporating a label matching module that reassigns reliable clusters with labels, ensuring correspondence between different modality labels. We construct the loss function by incorporating distinctiveness loss and multiplicity loss, both of which are determined by the similarity of neighboring features in the predicted feature space and the difference between distant features. This approach enables efficient feature clustering and cluster class assignment to occur concurrently. Eight UDA cross-modality person ReID experiments are conducted on three real datasets and six synthetic datasets. The experimental results unequivocally demonstrate that the proposed model outperforms the existing state-of-the-art algorithms to a significant degree. Notably, in RegDB → RegDB_light, the Rank-1 accuracy exhibits a remarkable improvement of 8.24%. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
LAMP |
Approved |
no |
|
|
Call Number |
Admin @ si @ CQL2023 |
Serial |
3884 |
|
Permanent link to this record |
|
|
|
|
Author |
Idoia Ruiz; Bogdan Raducanu; Rakesh Mehta; Jaume Amores |
|
|
Title |
Optimizing speed/accuracy trade-off for person re-identification via knowledge distillation |
Type |
Journal Article |
|
Year |
2020 |
Publication |
Engineering Applications of Artificial Intelligence |
Abbreviated Journal |
EAAI |
|
|
Volume |
87 |
Issue |
|
Pages |
103309 |
|
|
Keywords |
Person re-identification; Network distillation; Image retrieval; Model compression; Surveillance |
|
|
Abstract |
Finding a person across a camera network plays an important role in video surveillance. For a real-world person re-identification application, in order to guarantee an optimal time response, it is crucial to find the balance between accuracy and speed. We analyse this trade-off, comparing a classical method, that comprises hand-crafted feature description and metric learning, in particular, LOMO and XQDA, to deep learning based techniques, using image classification networks, ResNet and MobileNets. Additionally, we propose and analyse network distillation as a learning strategy to reduce the computational cost of the deep learning approach at test time. We evaluate both methods on the Market-1501 and DukeMTMC-reID large-scale datasets, showing that distillation helps reducing the computational cost at inference time while even increasing the accuracy performance. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
LAMP; 600.109; 600.120 |
Approved |
no |
|
|
Call Number |
Admin @ si @ RRM2020 |
Serial |
3401 |
|
Permanent link to this record |
|
|
|
|
Author |
Kai Wang; Joost Van de Weijer; Luis Herranz |
|
|
Title |
ACAE-REMIND for online continual learning with compressed feature replay |
Type |
Journal Article |
|
Year |
2021 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
|
|
Volume |
150 |
Issue |
|
Pages |
122-129 |
|
|
Keywords |
online continual learning; autoencoders; vector quantization |
|
|
Abstract |
Online continual learning aims to learn from a non-IID stream of data from a number of different tasks, where the learner is only allowed to consider data once. Methods are typically allowed to use a limited buffer to store some of the images in the stream. Recently, it was found that feature replay, where an intermediate layer representation of the image is stored (or generated) leads to superior results than image replay, while requiring less memory. Quantized exemplars can further reduce the memory usage. However, a drawback of these methods is that they use a fixed (or very intransigent) backbone network. This significantly limits the learning of representations that can discriminate between all tasks. To address this problem, we propose an auxiliary classifier auto-encoder (ACAE) module for feature replay at intermediate layers with high compression rates. The reduced memory footprint per image allows us to save more exemplars for replay. In our experiments, we conduct task-agnostic evaluation under online continual learning setting and get state-of-the-art performance on ImageNet-Subset, CIFAR100 and CIFAR10 dataset. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
LAMP; 600.147; 601.379; 600.120; 600.141 |
Approved |
no |
|
|
Call Number |
Admin @ si @ WWH2021 |
Serial |
3575 |
|
Permanent link to this record |
|
|
|
|
Author |
Lorenzo Seidenari; Giuseppe Serra; Andrew Bagdanov; Alberto del Bimbo |
|
|
Title |
Local pyramidal descriptors for image recognition |
Type |
Journal Article |
|
Year |
2014 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
|
|
Volume |
36 |
Issue |
5 |
Pages |
1033 - 1040 |
|
|
Keywords |
Object categorization; local features; kernel methods |
|
|
Abstract |
In this paper we present a novel method to improve the flexibility of descriptor matching for image recognition by using local multiresolution
pyramids in feature space. We propose that image patches be represented at multiple levels of descriptor detail and that these levels be defined in terms of local spatial pooling resolution. Preserving multiple levels of detail in local descriptors is a way of hedging one’s bets on which levels will most relevant for matching during learning and recognition. We introduce the Pyramid SIFT (P-SIFT) descriptor and show that its use in four state-of-the-art image recognition pipelines improves accuracy and yields state-of-the-art results. Our technique is applicable independently of spatial pyramid matching and we show that spatial pyramids can be combined with local pyramids to obtain
further improvement.We achieve state-of-the-art results on Caltech-101
(80.1%) and Caltech-256 (52.6%) when compared to other approaches based on SIFT features over intensity images. Our technique is efficient and is extremely easy to integrate into image recognition pipelines. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
0162-8828 |
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
LAMP; 600.079 |
Approved |
no |
|
|
Call Number |
Admin @ si @ SSB2014 |
Serial |
2524 |
|
Permanent link to this record |