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Author | Xavier Soria; Angel Sappa; Riad I. Hammoud | ||||
Title | Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Images | Type | Journal Article | ||
Year | 2018 | Publication | Sensors | Abbreviated Journal | SENS |
Volume | 18 | Issue | 7 | Pages | 2059 |
Keywords ![]() |
RGB-NIR sensor; multispectral imaging; deep learning; CNNs | ||||
Abstract | Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700–1100 nm) cross-talking with the visible bands (400–700 nm).
This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in different scenarios and using different similarity metrics. Both of them improve the state of the art approaches. |
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Area | Expedition | Conference | |||
Notes | ADAS; MSIAU; 600.086; 600.130; 600.122; 600.118 | Approved | no | ||
Call Number | Admin @ si @ SSH2018 | Serial | 3145 | ||
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Author | Eduardo Tusa; Arash Akbarinia; Raquel Gil Rodriguez; Corina Barbalata | ||||
Title | Real-Time Face Detection and Tracking Utilising OpenMP and ROS | Type | Conference Article | ||
Year | 2015 | Publication | 3rd Asia-Pacific Conference on Computer Aided System Engineering | Abbreviated Journal | |
Volume | Issue | Pages | 179 - 184 | ||
Keywords ![]() |
RGB-D; Kinect; Human Detection and Tracking; ROS; OpenMP | ||||
Abstract | The first requisite of a robot to succeed in social interactions is accurate human localisation, i.e. subject detection and tracking. Later, it is estimated whether an interaction partner seeks attention, for example by interpreting the position and orientation of the body. In computer vision, these cues usually are obtained in colour images, whose qualities are degraded in ill illuminated social scenes. In these scenarios depth sensors offer a richer representation. Therefore, it is important to combine colour and depth information. The
second aspect that plays a fundamental role in the acceptance of social robots is their real-time-ability. Processing colour and depth images is computationally demanding. To overcome this we propose a parallelisation strategy of face detection and tracking based on two different architectures: message passing and shared memory. Our results demonstrate high accuracy in low computational time, processing nine times more number of frames in a parallel implementation. This provides a real-time social robot interaction. |
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Address | Quito; Ecuador; July 2015 | ||||
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Language | Summary Language | Original Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | APCASE | ||
Notes | NEUROBIT | Approved | no | ||
Call Number | Admin @ si @ TAG2015 | Serial | 2659 | ||
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Author | Antonio Hernandez; Miguel Angel Bautista; Xavier Perez Sala; Victor Ponce; Sergio Escalera; Xavier Baro; Oriol Pujol; Cecilio Angulo | ||||
Title | Probability-based Dynamic Time Warping and Bag-of-Visual-and-Depth-Words for Human Gesture Recognition in RGB-D | Type | Journal Article | ||
Year | 2014 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 50 | Issue | 1 | Pages | 112-121 |
Keywords ![]() |
RGB-D; Bag-of-Words; Dynamic Time Warping; Human Gesture Recognition | ||||
Abstract | PATREC5825
We present a methodology to address the problem of human gesture segmentation and recognition in video and depth image sequences. A Bag-of-Visual-and-Depth-Words (BoVDW) model is introduced as an extension of the Bag-of-Visual-Words (BoVW) model. State-of-the-art RGB and depth features, including a newly proposed depth descriptor, are analysed and combined in a late fusion form. The method is integrated in a Human Gesture Recognition pipeline, together with a novel probability-based Dynamic Time Warping (PDTW) algorithm which is used to perform prior segmentation of idle gestures. The proposed DTW variant uses samples of the same gesture category to build a Gaussian Mixture Model driven probabilistic model of that gesture class. Results of the whole Human Gesture Recognition pipeline in a public data set show better performance in comparison to both standard BoVW model and DTW approach. |
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Notes | HuPBA;MV; 605.203 | Approved | no | ||
Call Number | Admin @ si @ HBP2014 | Serial | 2353 | ||
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Author | Xinhang Song; Luis Herranz; Shuqiang Jiang | ||||
Title | Depth CNNs for RGB-D Scene Recognition: Learning from Scratch Better than Transferring from RGB-CNNs | Type | Conference Article | ||
Year | 2017 | Publication | 31st AAAI Conference on Artificial Intelligence | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords ![]() |
RGB-D scene recognition; weakly supervised; fine tune; CNN | ||||
Abstract | Scene recognition with RGB images has been extensively studied and has reached very remarkable recognition levels, thanks to convolutional neural networks (CNN) and large scene datasets. In contrast, current RGB-D scene data is much more limited, so often leverages RGB large datasets, by transferring pretrained RGB CNN models and fine-tuning with the target RGB-D dataset. However, we show that this approach has the limitation of hardly reaching bottom layers, which is key to learn modality-specific features. In contrast, we focus on the bottom layers, and propose an alternative strategy to learn depth features combining local weakly supervised training from patches followed by global fine tuning with images. This strategy is capable of learning very discriminative depth-specific features with limited depth images, without resorting to Places-CNN. In addition we propose a modified CNN architecture to further match the complexity of the model and the amount of data available. For RGB-D scene recognition, depth and RGB features are combined by projecting them in a common space and further leaning a multilayer classifier, which is jointly optimized in an end-to-end network. Our framework achieves state-of-the-art accuracy on NYU2 and SUN RGB-D in both depth only and combined RGB-D data. | ||||
Address | San Francisco CA; February 2017 | ||||
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Language | Summary Language | Original Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | AAAI | ||
Notes | LAMP; 600.120 | Approved | no | ||
Call Number | Admin @ si @ SHJ2017 | Serial | 2967 | ||
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Author | Adriana Romero; Carlo Gatta | ||||
Title | Do We Really Need All These Neurons? | Type | Conference Article | ||
Year | 2013 | Publication | 6th Iberian Conference on Pattern Recognition and Image Analysis | Abbreviated Journal | |
Volume | 7887 | Issue | Pages | 460--467 | |
Keywords ![]() |
Retricted Boltzmann Machine; hidden units; unsupervised learning; classification | ||||
Abstract | Restricted Boltzmann Machines (RBMs) are generative neural networks that have received much attention recently. In particular, choosing the appropriate number of hidden units is important as it might hinder their representative power. According to the literature, RBM require numerous hidden units to approximate any distribution properly. In this paper, we present an experiment to determine whether such amount of hidden units is required in a classification context. We then propose an incremental algorithm that trains RBM reusing the previously trained parameters using a trade-off measure to determine the appropriate number of hidden units. Results on the MNIST and OCR letters databases show that using a number of hidden units, which is one order of magnitude smaller than the literature estimate, suffices to achieve similar performance. Moreover, the proposed algorithm allows to estimate the required number of hidden units without the need of training many RBM from scratch. | ||||
Address | Madeira; Portugal; June 2013 | ||||
Corporate Author | Thesis | ||||
Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | 0302-9743 | ISBN | 978-3-642-38627-5 | Medium | |
Area | Expedition | Conference | IbPRIA | ||
Notes | MILAB; 600.046 | Approved | no | ||
Call Number | Admin @ si @ RoG2013 | Serial | 2311 | ||
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Author | Mohamed Ali Souibgui; Sanket Biswas; Andres Mafla; Ali Furkan Biten; Alicia Fornes; Yousri Kessentini; Josep Llados; Lluis Gomez; Dimosthenis Karatzas | ||||
Title | Text-DIAE: a self-supervised degradation invariant autoencoder for text recognition and document enhancement | Type | Conference Article | ||
Year | 2023 | Publication | Proceedings of the 37th AAAI Conference on Artificial Intelligence | Abbreviated Journal | |
Volume | 37 | Issue | 2 | Pages | |
Keywords ![]() |
Representation Learning for Vision; CV Applications; CV Language and Vision; ML Unsupervised; Self-Supervised Learning | ||||
Abstract | In this paper, we propose a Text-Degradation Invariant Auto Encoder (Text-DIAE), a self-supervised model designed to tackle two tasks, text recognition (handwritten or scene-text) and document image enhancement. We start by employing a transformer-based architecture that incorporates three pretext tasks as learning objectives to be optimized during pre-training without the usage of labelled data. Each of the pretext objectives is specifically tailored for the final downstream tasks. We conduct several ablation experiments that confirm the design choice of the selected pretext tasks. Importantly, the proposed model does not exhibit limitations of previous state-of-the-art methods based on contrastive losses, while at the same time requiring substantially fewer data samples to converge. Finally, we demonstrate that our method surpasses the state-of-the-art in existing supervised and self-supervised settings in handwritten and scene text recognition and document image enhancement. Our code and trained models will be made publicly available at https://github.com/dali92002/SSL-OCR | ||||
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Area | Expedition | Conference | AAAI | ||
Notes | DAG | Approved | no | ||
Call Number | Admin @ si @ SBM2023 | Serial | 3848 | ||
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Author | Muhammad Anwer Rao; Fahad Shahbaz Khan; Joost Van de Weijer; Matthieu Molinier; Jorma Laaksonen | ||||
Title | Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification | Type | Journal Article | ||
Year | 2018 | Publication | ISPRS Journal of Photogrammetry and Remote Sensing | Abbreviated Journal | ISPRS J |
Volume | 138 | Issue | Pages | 74-85 | |
Keywords ![]() |
Remote sensing; Deep learning; Scene classification; Local Binary Patterns; Texture analysis | ||||
Abstract | Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene | ||||
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Notes | LAMP; 600.109; 600.106; 600.120 | Approved | no | ||
Call Number | Admin @ si @ RKW2018 | Serial | 3158 | ||
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Author | Monica Piñol; Angel Sappa; Ricardo Toledo | ||||
Title | Adaptive Feature Descriptor Selection based on a Multi-Table Reinforcement Learning Strategy | Type | Journal Article | ||
Year | 2015 | Publication | Neurocomputing | Abbreviated Journal | NEUCOM |
Volume | 150 | Issue | A | Pages | 106–115 |
Keywords ![]() |
Reinforcement learning; Q-learning; Bag of features; Descriptors | ||||
Abstract | This paper presents and evaluates a framework to improve the performance of visual object classification methods, which are based on the usage of image feature descriptors as inputs. The goal of the proposed framework is to learn the best descriptor for each image in a given database. This goal is reached by means of a reinforcement learning process using the minimum information. The visual classification system used to demonstrate the proposed framework is based on a bag of features scheme, and the reinforcement learning technique is implemented through the Q-learning approach. The behavior of the reinforcement learning with different state definitions is evaluated. Additionally, a method that combines all these states is formulated in order to select the optimal state. Finally, the chosen actions are obtained from the best set of image descriptors in the literature: PHOW, SIFT, C-SIFT, SURF and Spin. Experimental results using two public databases (ETH and COIL) are provided showing both the validity of the proposed approach and comparisons with state of the art. In all the cases the best results are obtained with the proposed approach. | ||||
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Notes | ADAS; 600.055; 600.076 | Approved | no | ||
Call Number | Admin @ si @ PST2015 | Serial | 2473 | ||
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Author | Diego Velazquez; Pau Rodriguez; Josep M. Gonfaus; Xavier Roca; Jordi Gonzalez | ||||
Title | A Closer Look at Embedding Propagation for Manifold Smoothing | Type | Journal Article | ||
Year | 2022 | Publication | Journal of Machine Learning Research | Abbreviated Journal | JMLR |
Volume | 23 | Issue | 252 | Pages | 1-27 |
Keywords ![]() |
Regularization; emi-supervised learning; self-supervised learning; adversarial robustness; few-shot classification | ||||
Abstract | Supervised training of neural networks requires a large amount of manually annotated data and the resulting networks tend to be sensitive to out-of-distribution (OOD) data.
Self- and semi-supervised training schemes reduce the amount of annotated data required during the training process. However, OOD generalization remains a major challenge for most methods. Strategies that promote smoother decision boundaries play an important role in out-of-distribution generalization. For example, embedding propagation (EP) for manifold smoothing has recently shown to considerably improve the OOD performance for few-shot classification. EP achieves smoother class manifolds by building a graph from sample embeddings and propagating information through the nodes in an unsupervised manner. In this work, we extend the original EP paper providing additional evidence and experiments showing that it attains smoother class embedding manifolds and improves results in settings beyond few-shot classification. Concretely, we show that EP improves the robustness of neural networks against multiple adversarial attacks as well as semi- and self-supervised learning performance. |
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Address | 9/2022 | ||||
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Notes | Approved | no | |||
Call Number | Admin @ si @ VRG2022 | Serial | 3762 | ||
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Author | Svebor Karaman; Giuseppe Lisanti; Andrew Bagdanov; Alberto del Bimbo | ||||
Title | From re-identification to identity inference: Labeling consistency by local similarity constraints | Type | Book Chapter | ||
Year | 2014 | Publication | Person Re-Identification | Abbreviated Journal | |
Volume | 2 | Issue | Pages | 287-307 | |
Keywords ![]() |
re-identification; Identity inference; Conditional random fields; Video surveillance | ||||
Abstract | In this chapter, we introduce the problem of identity inference as a generalization of person re-identification. It is most appropriate to distinguish identity inference from re-identification in situations where a large number of observations must be identified without knowing a priori that groups of test images represent the same individual. The standard single- and multishot person re-identification common in the literature are special cases of our formulation. We present an approach to solving identity inference by modeling it as a labeling problem in a Conditional Random Field (CRF). The CRF model ensures that the final labeling gives similar labels to detections that are similar in feature space. Experimental results are given on the ETHZ, i-LIDS and CAVIAR datasets. Our approach yields state-of-the-art performance for multishot re-identification, and our results on the more general identity inference problem demonstrate that we are able to infer the identity of very many examples even with very few labeled images in the gallery. | ||||
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Publisher | Springer London | Place of Publication | Editor | ||
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ISSN | 2191-6586 | ISBN | 978-1-4471-6295-7 | Medium | |
Area | Expedition | Conference | |||
Notes | LAMP; 600.079 | Approved | no | ||
Call Number | Admin @ si @KLB2014b | Serial | 2521 | ||
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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. | ||||
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Notes | LAMP; 601.240; 600.079 | Approved | no | ||
Call Number | Admin @ si @ KLB2014a | Serial | 2522 | ||
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Author | Chris Bahnsen; David Vazquez; Antonio Lopez; Thomas B. Moeslund | ||||
Title | Learning to Remove Rain in Traffic Surveillance by Using Synthetic Data | Type | Conference Article | ||
Year | 2019 | Publication | 14th International Conference on Computer Vision Theory and Applications | Abbreviated Journal | |
Volume | Issue | Pages | 123-130 | ||
Keywords ![]() |
Rain Removal; Traffic Surveillance; Image Denoising | ||||
Abstract | Rainfall is a problem in automated traffic surveillance. Rain streaks occlude the road users and degrade the overall visibility which in turn decrease object detection performance. One way of alleviating this is by artificially removing the rain from the images. This requires knowledge of corresponding rainy and rain-free images. Such images are often produced by overlaying synthetic rain on top of rain-free images. However, this method fails to incorporate the fact that rain fall in the entire three-dimensional volume of the scene. To overcome this, we introduce training data from the SYNTHIA virtual world that models rain streaks in the entirety of a scene. We train a conditional Generative Adversarial Network for rain removal and apply it on traffic surveillance images from SYNTHIA and the AAU RainSnow datasets. To measure the applicability of the rain-removed images in a traffic surveillance context, we run the YOLOv2 object detection algorithm on the original and rain-removed frames. The results on SYNTHIA show an 8% increase in detection accuracy compared to the original rain image. Interestingly, we find that high PSNR or SSIM scores do not imply good object detection performance. | ||||
Address | Praga; Czech Republic; February 2019 | ||||
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Area | Expedition | Conference | VISIGRAPP | ||
Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ BVL2019 | Serial | 3256 | ||
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Author | Xavier Carrillo; E Fernandez-Nofrerias; Francesco Ciompi; O. Rodriguez-Leor; Petia Radeva; Neus Salvatella; Oriol Pujol; J. Mauri; A. Bayes | ||||
Title | Changes in Radial Artery Volume Assessed Using Intravascular Ultrasound: A Comparison of Two Vasodilator Regimens in Transradial Coronary Intervention | Type | Journal Article | ||
Year | 2011 | Publication | Journal of Invasive Cardiology | Abbreviated Journal | JOIC |
Volume | 23 | Issue | 10 | Pages | 401-404 |
Keywords ![]() |
radial; vasodilator treatment; percutaneous coronary intervention; IVUS; volumetric IVUS analysis | ||||
Abstract | OBJECTIVES:
This study used intravascular ultrasound (IVUS) to evaluate radial artery volume changes after intraarterial administration of nitroglycerin and/or verapamil. BACKGROUND: Radial artery spasm, which is associated with radial artery size, is the main limitation of the transradial approach in percutaneous coronary interventions (PCI). METHODS: This prospective, randomized study compared the effect of two intra-arterial vasodilator regimens on radial artery volume: 0.2 mg of nitroglycerin plus 2.5 mg of verapamil (Group 1; n = 15) versus 2.5 mg of verapamil alone (Group 2; n = 15). Radial artery lumen volume was assessed using IVUS at two time points: at baseline (5 minutes after sheath insertion) and post-vasodilator (1 minute after drug administration). The luminal volume of the radial artery was computed using ECOC Random Fields (ECOC-RF), a technique used for automatic segmentation of luminal borders in longitudinal cut images from IVUS sequences. RESULTS: There was a significant increase in arterial lumen volume in both groups, with an increase from 451 ± 177 mm³ to 508 ± 192 mm³ (p = 0.001) in Group 1 and from 456 ± 188 mm³ to 509 ± 170 mm³ (p = 0.001) in Group 2. There were no significant differences between the groups in terms of absolute volume increase (58 mm³ versus 53 mm³, respectively; p = 0.65) or in relative volume increase (14% versus 20%, respectively; p = 0.69). CONCLUSIONS: Administration of nitroglycerin plus verapamil or verapamil alone to the radial artery resulted in similar increases in arterial lumen volume according to ECOC-RF IVUS measurements. |
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Notes | MILAB;HuPBA | Approved | no | ||
Call Number | Admin @ si @ CFC2011 | Serial | 1797 | ||
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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%. | ||||
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Notes | LAMP | Approved | no | ||
Call Number | Admin @ si @ CQL2023 | Serial | 3884 | ||
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Author | Abel Gonzalez-Garcia; Davide Modolo; Vittorio Ferrari | ||||
Title | Objects as context for detecting their semantic parts | Type | Conference Article | ||
Year | 2018 | Publication | 31st IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 6907 - 6916 | ||
Keywords ![]() |
Proposals; Semantics; Wheels; Automobiles; Context modeling; Task analysis; Object detection | ||||
Abstract | We present a semantic part detection approach that effectively leverages object information. We use the object appearance and its class as indicators of what parts to expect. We also model the expected relative location of parts inside the objects based on their appearance. We achieve this with a new network module, called OffsetNet, that efficiently predicts a variable number of part locations within a given object. Our model incorporates all these cues to
detect parts in the context of their objects. This leads to considerably higher performance for the challenging task of part detection compared to using part appearance alone (+5 mAP on the PASCAL-Part dataset). We also compare to other part detection methods on both PASCAL-Part and CUB200-2011 datasets. |
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Address | Salt Lake City; USA; June 2018 | ||||
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Area | Expedition | Conference | CVPR | ||
Notes | LAMP; 600.109; 600.120 | Approved | no | ||
Call Number | Admin @ si @ GMF2018 | Serial | 3229 | ||
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