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Author |
Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez |
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Title  |
Incremental Domain Adaptation of Deformable Part-based Models |
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Conference Article |
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Year |
2014 |
Publication |
25th British Machine Vision Conference |
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Keywords |
Pedestrian Detection; Part-based models; Domain Adaptation |
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Abstract |
Nowadays, classifiers play a core role in many computer vision tasks. The underlying assumption for learning classifiers is that the training set and the deployment environment (testing) follow the same probability distribution regarding the features used by the classifiers. However, in practice, there are different reasons that can break this constancy assumption. Accordingly, reusing existing classifiers by adapting them from the previous training environment (source domain) to the new testing one (target domain)
is an approach with increasing acceptance in the computer vision community. In this paper we focus on the domain adaptation of deformable part-based models (DPMs) for object detection. In particular, we focus on a relatively unexplored scenario, i.e. incremental domain adaptation for object detection assuming weak-labeling. Therefore, our algorithm is ready to improve existing source-oriented DPM-based detectors as soon as a little amount of labeled target-domain training data is available, and keeps improving as more of such data arrives in a continuous fashion. For achieving this, we follow a multiple
instance learning (MIL) paradigm that operates in an incremental per-image basis. As proof of concept, we address the challenging scenario of adapting a DPM-based pedestrian detector trained with synthetic pedestrians to operate in real-world scenarios. The obtained results show that our incremental adaptive models obtain equally good accuracy results as the batch learned models, while being more flexible for handling continuously arriving target-domain data. |
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Nottingham; uk; September 2014 |
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BMVA Press |
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Valstar, Michel and French, Andrew and Pridmore, Tony |
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BMVC |
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ADAS; 600.057; 600.054; 600.076 |
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XRV2014c; ADAS @ adas @ xrv2014c |
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2455 |
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Author |
Katerine Diaz; Francesc J. Ferri; W. Diaz |
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Title |
Incremental Generalized Discriminative Common Vectors for Image Classification |
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Journal Article |
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2015 |
Publication |
IEEE Transactions on Neural Networks and Learning Systems |
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TNNLS |
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26 |
Issue |
8 |
Pages |
1761 - 1775 |
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Subspace-based methods have become popular due to their ability to appropriately represent complex data in such a way that both dimensionality is reduced and discriminativeness is enhanced. Several recent works have concentrated on the discriminative common vector (DCV) method and other closely related algorithms also based on the concept of null space. In this paper, we present a generalized incremental formulation of the DCV methods, which allows the update of a given model by considering the addition of new examples even from unseen classes. Having efficient incremental formulations of well-behaved batch algorithms allows us to conveniently adapt previously trained classifiers without the need of recomputing them from scratch. The proposed generalized incremental method has been empirically validated in different case studies from different application domains (faces, objects, and handwritten digits) considering several different scenarios in which new data are continuously added at different rates starting from an initial model. |
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2162-237X |
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ADAS; 600.076 |
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Admin @ si @ DFD2015 |
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2547 |
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Author |
Angel Sappa; M.A. Garcia |
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Title |
Incremental Integration of Multiresolution Range Images |
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2007 |
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The imaging science journal. Vol. 55, No. 3 pp. 127–139 |
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ADAS |
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ADAS @ adas @ SaG2007d |
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812 |
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Author |
Kai Wang; Xialei Liu; Andrew Bagdanov; Luis Herranz; Shangling Jui; Joost Van de Weijer |
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Title |
Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image Recognition |
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Conference Article |
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2022 |
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CVPR 2022 Workshop on Continual Learning (CLVision, 3rd Edition) |
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3728-3738 |
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Training; Computer vision; Image recognition; Upper bound; Conferences; Pattern recognition; Task analysis |
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In this paper we consider the problem of incremental meta-learning in which classes are presented incrementally in discrete tasks. We propose Episodic Replay Distillation (ERD), that mixes classes from the current task with exemplars from previous tasks when sampling episodes for meta-learning. To allow the training to benefit from a large as possible variety of classes, which leads to more gener-
alizable feature representations, we propose the cross-task meta loss. Furthermore, we propose episodic replay distillation that also exploits exemplars for improved knowledge distillation. Experiments on four datasets demonstrate that ERD surpasses the state-of-the-art. In particular, on the more challenging one-shot, long task sequence scenarios, we reduce the gap between Incremental Meta-Learning and
the joint-training upper bound from 3.5% / 10.1% / 13.4% / 11.7% with the current state-of-the-art to 2.6% / 2.9% / 5.0% / 0.2% with our method on Tiered-ImageNet / Mini-ImageNet / CIFAR100 / CUB, respectively. |
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New Orleans, USA; 20 June 2022 |
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CVPRW |
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LAMP; 600.147 |
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Admin @ si @ WLB2022 |
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3686 |
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Author |
Katerine Diaz; Konstantia Georgouli; Anastasios Koidis; Jesus Martinez del Rincon |
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Title |
Incremental model learning for spectroscopy-based food analysis |
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Journal Article |
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Year |
2017 |
Publication |
Chemometrics and Intelligent Laboratory Systems |
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CILS |
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Volume |
167 |
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123-131 |
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Keywords |
Incremental model learning; IGDCV technique; Subspace based learning; IdentificationVegetable oils; FT-IR spectroscopy |
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Abstract |
In this paper we propose the use of incremental learning for creating and improving multivariate analysis models in the field of chemometrics of spectral data. As main advantages, our proposed incremental subspace-based learning allows creating models faster, progressively improving previously created models and sharing them between laboratories and institutions without requiring transferring or disclosing individual spectra samples. In particular, our approach allows to improve the generalization and adaptability of previously generated models with a few new spectral samples to be applicable to real-world situations. The potential of our approach is demonstrated using vegetable oil type identification based on spectroscopic data as case study. Results show how incremental models maintain the accuracy of batch learning methodologies while reducing their computational cost and handicaps. |
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ADAS; 600.118 |
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Admin @ si @ DGK2017 |
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3002 |
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Author |
Elvina Motard; Bogdan Raducanu; Viviane Cadenat; Jordi Vitria |
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Title |
Incremental On-Line Topological Map Learning for A Visual Homing Application |
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Conference Article |
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2007 |
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IEEE International Conference on Robotics and Automation |
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2049–2054 |
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Roma (Italy) |
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ICRA |
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OR; MV |
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BCNPCL @ bcnpcl @ MRC2007 |
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793 |
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Author |
Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias; A. Moreira |
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Title |
Incremental Scenario Representations for Autonomous Driving using Geometric Polygonal Primitives |
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Journal Article |
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Year |
2016 |
Publication |
Robotics and Autonomous Systems |
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RAS |
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83 |
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312-325 |
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Incremental scene reconstruction; Point clouds; Autonomous vehicles; Polygonal primitives |
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When an autonomous vehicle is traveling through some scenario it receives a continuous stream of sensor data. This sensor data arrives in an asynchronous fashion and often contains overlapping or redundant information. Thus, it is not trivial how a representation of the environment observed by the vehicle can be created and updated over time. This paper presents a novel methodology to compute an incremental 3D representation of a scenario from 3D range measurements. We propose to use macro scale polygonal primitives to model the scenario. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Furthermore, we propose mechanisms designed to update the geometric polygonal primitives over time whenever fresh sensor data is collected. Results show that the approach is capable of producing accurate descriptions of the scene, and that it is computationally very efficient when compared to other reconstruction techniques. |
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Elsevier B.V. |
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ADAS; 600.086, 600.076 |
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Admin @ si @OSS2016a |
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2806 |
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Author |
Bogdan Raducanu; Jordi Vitria |
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Title |
Incremental Subspace Learning for Cognitive Visual Processes |
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2007 |
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Advances in Brain, Vision and Artificial Intelligence, 2nd International Symposium |
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4729 |
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214–223 |
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Naples (Italy) |
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LNCS |
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BVAI’07 |
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OR;MV |
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BCNPCL @ bcnpcl @ RaV2007b |
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901 |
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Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias; A. Moreira |
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Title |
Incremental texture mapping for autonomous driving |
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Journal Article |
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2016 |
Publication |
Robotics and Autonomous Systems |
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RAS |
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84 |
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113-128 |
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Scene reconstruction; Autonomous driving; Texture mapping |
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Autonomous vehicles have a large number of on-board sensors, not only for providing coverage all around the vehicle, but also to ensure multi-modality in the observation of the scene. Because of this, it is not trivial to come up with a single, unique representation that feeds from the data given by all these sensors. We propose an algorithm which is capable of mapping texture collected from vision based sensors onto a geometric description of the scenario constructed from data provided by 3D sensors. The algorithm uses a constrained Delaunay triangulation to produce a mesh which is updated using a specially devised sequence of operations. These enforce a partial configuration of the mesh that avoids bad quality textures and ensures that there are no gaps in the texture. Results show that this algorithm is capable of producing fine quality textures. |
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ADAS; 600.086 |
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Admin @ si @ OSS2016b |
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2912 |
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Author |
M. Bressan; Jordi Vitria |
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Title |
Independent Component Analysis and Naïve Bayes Classification. |
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2002 |
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Proceedings of the Second IASTED International Conference Visualilzation, Imaging and Image Proceesing VIIP 2002: 496–501. |
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OR;MV |
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BCNPCL @ bcnpcl @ BrV2002a |
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288 |
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M. Bressan; Jordi Vitria |
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Independent Feature Selection |
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2003 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(10): 1312–1317 (IF: 3.823) |
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M. Bressan; Jordi Vitria |
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Independent Modes of Variation in Point Distribution Models |
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2001 |
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In C. Arcelli, L.P. Cordella, G. Sanniti di Baja (Eds.): Visual Form 2001 4tth International Workshop on Visual Visual Form 2001 4tth International Workshop on Visual Form, IWVF4, Proceedings, LNCS 2059, Springer Verlag, 123 |
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Capri, Italia |
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BCNPCL @ bcnpcl @ BVi2001 |
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M. Bressan |
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Independent modes of variation in Point Distribution models |
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2000 |
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CVC Technical Report #48 |
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Admin @ si @ Bre2000 |
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349 |
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M. Pros |
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Indexacio icònica amb 2D-String per al reconoixement de persones segons la seva vestimenta |
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2000 |
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CVC Technical Report #44 |
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Josep Llados; Gemma Sanchez |
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Title |
Indexing Historical Documents by Word Shape Signatures |
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2007 |
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9th International Conference on Document Analysis and Recognition |
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1 |
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362–366 |
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Abstract |
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Address |
Curitiba (Brasil) |
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Corporate Author |
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Thesis |
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Publisher |
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Place of Publication |
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Editor |
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Language |
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Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
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ISBN |
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Medium |
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Area |
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Expedition |
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Conference |
ICDAR |
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Notes |
DAG |
Approved |
no |
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Call Number |
DAG @ dag @ LlS2007 |
Serial |
882 |
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