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Author (up) Damian Sojka; Yuyang Liu; Dipam Goswami; Sebastian Cygert; Bartłomiej Twardowski; Joost van de Weijer
Title Technical Report for ICCV 2023 Visual Continual Learning Challenge: Continuous Test-time Adaptation for Semantic Segmentation Type Miscellaneous
Year 2023 Publication Arxiv Abbreviated Journal
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Abstract The goal of the challenge is to develop a test-time adaptation (TTA) method, which could adapt the model to gradually changing domains in video sequences for semantic segmentation task. It is based on a synthetic driving video dataset – SHIFT. The source model is trained on images taken during daytime in clear weather. Domain changes at test-time are mainly caused by varying weather conditions and times of day. The TTA methods are evaluated in each image sequence (video) separately, meaning the model is reset to the source model state before the next sequence. Images come one by one and a prediction has to be made at the arrival of each frame. Each sequence is composed of 401 images and starts with the source domain, then gradually drifts to a different one (changing weather or time of day) until the middle of the sequence. In the second half of the sequence, the domain gradually shifts back to the source one. Ground truth data is available only for the validation split of the SHIFT dataset, in which there are only six sequences that start and end with the source domain. We conduct an analysis specifically on those sequences. Ground truth data for test split, on which the developed TTA methods are evaluated for leader board ranking, are not publicly available.
The proposed solution secured a 3rd place in a challenge and received an innovation award. Contrary to the solutions that scored better, we did not use any external pretrained models or specialized data augmentations, to keep the solutions as general as possible. We have focused on analyzing the distributional shift and developing a method that could adapt to changing data dynamics and generalize across different scenarios.
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Notes LAMP Approved no
Call Number Admin @ si @ SLG2023 Serial 3993
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Author (up) Dani Rowe
Title Towards Robust Multiple-Target Tracking in Unconstrained Human-Populated Environments Type Miscellaneous
Year 2008 Publication CVC–UAB Abbreviated Journal
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ISSN ISBN 978–84–935251–5–6 Medium
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Notes Approved no
Call Number Admin @ si @ Row2008 Serial 1103
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Author (up) Dani Rowe; Ignasi Rius; Jordi Gonzalez; Juan J. Villanueva
Title Robust Particle Filtering for Object Tracking Type Miscellaneous
Year 2005 Publication 13th International Conference on Image Analysis and Processing (ICIAP’2005), LNCS 3617: 1158–1165, ISBN 3–540–28869–4 Abbreviated Journal
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Address Cagliary (Italy)
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Notes Approved no
Call Number ISE @ ise @ RRG2005e Serial 577
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Author (up) Dani Rowe; Ignasi Rius; Jordi Gonzalez; Juan J. Villanueva
Title Improving Tracking by Handling Occlusions Type Miscellaneous
Year 2005 Publication 3rd International Conference on Advances in Pattern Recognition (ICAPR’2005), Pattern Recognition and Image Analysis, LNCS 3687: 146–154, ISBN 978–3–540–28833–6 Abbreviated Journal
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Address Bath (United Kingdom)
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Notes Approved no
Call Number ISE @ ise @ RRG2005d Serial 619
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Author (up) Daniel Marczak; Grzegorz Rypesc; Sebastian Cygert; Tomasz Trzcinski; Bartłomiej Twardowski
Title Generalized Continual Category Discovery Type Miscellaneous
Year 2023 Publication arxiv Abbreviated Journal
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Abstract Most of Continual Learning (CL) methods push the limit of supervised learning settings, where an agent is expected to learn new labeled tasks and not forget previous knowledge. However, these settings are not well aligned with real-life scenarios, where a learning agent has access to a vast amount of unlabeled data encompassing both novel (entirely unlabeled) classes and examples from known classes. Drawing inspiration from Generalized Category Discovery (GCD), we introduce a novel framework that relaxes this assumption. Precisely, in any task, we allow for the existence of novel and known classes, and one must use continual version of unsupervised learning methods to discover them. We call this setting Generalized Continual Category Discovery (GCCD). It unifies CL and GCD, bridging the gap between synthetic benchmarks and real-life scenarios. With a series of experiments, we present that existing methods fail to accumulate knowledge from subsequent tasks in which unlabeled samples of novel classes are present. In light of these limitations, we propose a method that incorporates both supervised and unsupervised signals and mitigates the forgetting through the use of centroid adaptation. Our method surpasses strong CL methods adopted for GCD techniques and presents a superior representation learning performance.
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Notes LAMP Approved no
Call Number Admin @ si @ MRC2023 Serial 3985
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Author (up) Daniel Marczak; Sebastian Cygert; Tomasz Trzcinski; Bartlomiej Twardowski
Title Revisiting Supervision for Continual Representation Learning Type Miscellaneous
Year 2023 Publication Arxiv Abbreviated Journal
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Abstract In the field of continual learning, models are designed to learn tasks one after the other. While most research has centered on supervised continual learning, recent studies have highlighted the strengths of self-supervised continual representation learning. The improved transferability of representations built with self-supervised methods is often associated with the role played by the multi-layer perceptron projector. In this work, we depart from this observation and reexamine the role of supervision in continual representation learning. We reckon that additional information, such as human annotations, should not deteriorate the quality of representations. Our findings show that supervised models when enhanced with a multi-layer perceptron head, can outperform self-supervised models in continual representation learning.
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Notes xxx Approved no
Call Number Admin @ si @ MCT2023 Serial 4013
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Author (up) Daniel Ponsa; A.F. Sole; Antonio Lopez; Cristina Cañero; Petia Radeva; Jordi Vitria
Title Regularized EM Type Miscellaneous
Year 1999 Publication Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes Abbreviated Journal
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Address Bilbao
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Notes invisible;ADAS;OR;MILAB;MV Approved no
Call Number ADAS @ adas @ PSL1999 Serial 4
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Author (up) Daniel Ponsa; A.F. Sole; Antonio Lopez; Cristina Cañero; Petia Radeva; Jordi Vitria
Title Regularized EM. Type Miscellaneous
Year 2000 Publication Pattern Recognition and Applications, IOS Press, 69–77. Abbreviated Journal
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Notes invisible;ADAS;OR;MILAB;MV Approved no
Call Number ADAS @ adas @ PSL2000 Serial 336
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Author (up) Daniel Ponsa; Antonio Lopez
Title Seguimiento Visual de Contornos Computerizado Type Miscellaneous
Year 2009 Publication UAB Divulga, Revista de divulgacion cientifica Abbreviated Journal
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Notes spreading;ADAS Approved no
Call Number ADAS @ adas @ PoL2009b Serial 1270
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Author (up) Daniel Ponsa; Antonio Lopez; Felipe Lumbreras; Joan Serrat; T. Graf
Title 3D Vehicle Sensor based on Monocular Vision Type Miscellaneous
Year 2005 Publication Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems, 1096–1101, ISBN:0–7803–9216–7 Abbreviated Journal
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Address Vienna (Austria)
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Notes ADAS Approved no
Call Number ADAS @ adas @ PLL2005 Serial 614
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Author (up) Daniel Ponsa; Antonio Lopez; Joan Serrat; Felipe Lumbreras; T. Graf
Title Multiple Vehicle 3D Tracking Using an Unscented Kalman Filter Type Miscellaneous
Year 2005 Publication Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems, 1108–1113, ISBN:0–7803–9216–7 Abbreviated Journal
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Keywords vehicle detection
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Address Vienna (Austria)
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Notes ADAS Approved no
Call Number ADAS @ adas @ PLS2005 Serial 615
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Author (up) Daniel Ponsa; Jordi Vitria
Title Mobile monitoring system using an agent-oriented approach Type Miscellaneous
Year 1999 Publication Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes Abbreviated Journal
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Address Bilbao
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Notes ADAS;OR;MV Approved no
Call Number ADAS @ adas @ DaV1999 Serial 21
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Author (up) Daniel Ponsa; Xavier Roca
Title A Novel Approach to Generate Multiple Shape Models. Type Miscellaneous
Year 2002 Publication Articulated Motion and Deformable Objects, LNCS 2492: 80–91, Springer Verlag. Abbreviated Journal
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Notes ADAS;ISE Approved no
Call Number ADAS @ adas @ PoR2002a Serial 283
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Author (up) Daniel Ponsa; Xavier Roca
Title Unsupervised Parameterisation of Gaussian Mixture Models Type Miscellaneous
Year 2002 Publication Abbreviated Journal
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Notes ADAS;ISE Approved no
Call Number ADAS @ adas @ PoR2002c Serial 313
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Author (up) Daniel Ponsa; Xavier Roca
Title Multiple Model Approach to Deformable Shape Tracking Type Miscellaneous
Year 2003 Publication In Pattern Recognition and Image Analysis, Lecture Notes in Computer Science 2652:782–792 Abbreviated Journal
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Address Springer-Verlag
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Notes ADAS;ISE Approved no
Call Number ADAS @ adas @ PoR2003 Serial 397
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