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| Author | Albert Gordo; Ernest Valveny | ||||
| Title | The diagonal split: A pre-segmentation step for page layout analysis & classification | Type | Conference Article | ||
| Year | 2009 | Publication | 4th Iberian Conference on Pattern Recognition and Image Analysis | Abbreviated Journal | |
| Volume | 5524 | Issue | Pages | 290–297 | |
| Keywords | |||||
| Abstract | Document classification is an important task in all the processes related to document storage and retrieval. In the case of complex documents, structural features are needed to achieve a correct classification. Unfortunately, physical layout analysis is error prone. In this paper we present a pre-segmentation step based on a divide & conquer strategy that can be used to improve the page segmentation results, independently of the segmentation algorithm used. This pre-segmentation step is evaluated in classification and retrieval using the selective CRLA algorithm for layout segmentation together with a clustering based on the voronoi area diagram, and tested on two different databases, MARG and Girona Archives. | ||||
Address  |
Póvoa de Varzim, Portugal | ||||
| 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 | 978-3-642-02171-8 | Medium | |
| Area | Expedition | Conference | IbPRIA | ||
| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ Gov2009b | Serial | 1176 | ||
| Permanent link to this record | |||||
| Author | Pierluigi Casale; Oriol Pujol; Petia Radeva | ||||
| Title | Face-to-face social activity detection using data collected with a wearable device | Type | Conference Article | ||
| Year | 2009 | Publication | 4th Iberian Conference on Pattern Recognition and Image Analysis | Abbreviated Journal | |
| Volume | 5524 | Issue | Pages | 56–63 | |
| Keywords | |||||
| Abstract | In this work the feasibility of building a socially aware badge that learns from user activities is explored. A wearable multisensor device has been prototyped for collecting data about user movements and photos of the environment where the user acts. Using motion data, speaking and other activities have been classified. Images have been analysed in order to complement motion data and help for the detection of social behaviours. A face detector and an activity classifier are both used for detecting if users have a social activity in the time they worn the device. Good results encourage the improvement of the system at both hardware and software level | ||||
| Address |
Póvoa de Varzim, Portugal | ||||
| 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 | 978-3-642-02171-8 | Medium | |
| Area | Expedition | Conference | IbPRIA | ||
| Notes | MILAB;HuPBA | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ CPR2009b | Serial | 1206 | ||
| Permanent link to this record | |||||
| Author | Marco Pedersoli; Jordi Gonzalez; Juan J. Villanueva | ||||
| Title | High-Speed Human Detection Using a Multiresolution Cascade of Histograms of Oriented Gradients | Type | Conference Article | ||
| Year | 2009 | Publication | 4th Iberian Conference on Pattern Recognition and Image Analysis | Abbreviated Journal | |
| Volume | 5524 | Issue | Pages | ||
| Keywords | |||||
| Abstract | This paper presents a new method for human detection based on a multiresolution cascade of Histograms of Oriented Gradients (HOG) that can highly reduce the computational cost of the detection search without affecting accuracy. The method consists of a cascade of sliding window detectors. Each detector is a Support Vector Machine (SVM) composed by features at different resolution, from coarse for the first level to fine for the last one.
Considering that the spatial stride of the sliding window search is affected by the HOG features size, unlike previous methods based on Adaboost cascades, we can adopt a spatial stride inversely proportional to the features resolution. This produces that the speed-up of the cascade is not only due to the low number of features that need to be computed in the first levels, but also to the lower number of detection windows that needs to be evaluated. Experimental results shows that our method permits a detection rate comparable with the state of the art, but at the same time a gain in the speed of the detection search of 10-20 times depending on the cascade configuration. |
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| Address |
Póvoa de Varzim, Portugal | ||||
| 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 | 978-3-642-02171-8 | Medium | |
| Area | Expedition | Conference | IbPRIA | ||
| Notes | ISE | Approved | no | ||
| Call Number | ISE @ ise @ PGV2009 | Serial | 1214 | ||
| Permanent link to this record | |||||
| Author | Bhaskar Chakraborty; Andrew Bagdanov; Jordi Gonzalez | ||||
| Title | Towards Real-Time Human Action Recognition | Type | Conference Article | ||
| Year | 2009 | Publication | 4th Iberian Conference on Pattern Recognition and Image Analysis | Abbreviated Journal | |
| Volume | 5524 | Issue | Pages | ||
| Keywords | |||||
| Abstract | This work presents a novel approach to human detection based action-recognition in real-time. To realize this goal our method first detects humans in different poses using a correlation-based approach. Recognition of actions is done afterward based on the change of the angular values subtended by various body parts. Real-time human detection and action recognition are very challenging, and most state-of-the-art approaches employ complex feature extraction and classification techniques, which ultimately becomes a handicap for real-time recognition. Our correlation-based method, on the other hand, is computationally efficient and uses very simple gradient-based features. For action recognition angular features of body parts are extracted using a skeleton technique. Results for action recognition are comparable with the present state-of-the-art. | ||||
| Address |
Póvoa de Varzim, Portugal | ||||
| 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 | 978-3-642-02171-8 | Medium | |
| Area | Expedition | Conference | IbPRIA | ||
| Notes | ISE | Approved | no | ||
| Call Number | DAG @ dag @ CBG2009 | Serial | 1215 | ||
| Permanent link to this record | |||||
| Author | Fadi Dornaika; Bogdan Raducanu | ||||
| Title | Detecting and Tracking of 3D Face Pose for Human-Robot Interaction | Type | Conference Article | ||
| Year | 2008 | Publication | IEEE International Conference on Robotics and Automation, | Abbreviated Journal | |
| Volume | Issue | Pages | 1716–1721 | ||
| Keywords | |||||
| Abstract | |||||
| Address |
Pasadena; CA; USA | ||||
| 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 | ICRA | ||
| Notes | OR;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ DoR2008a | Serial | 982 | ||
| Permanent link to this record | |||||
| Author | Arnau Ramisa; Adriana Tapus; Ramon Lopez de Mantaras; Ricardo Toledo | ||||
| Title | Mobile Robot Localization using Panoramic Vision and Combination of Feature Region Detectors | Type | Conference Article | ||
| Year | 2008 | Publication | IEEE International Conference on Robotics and Automation, | Abbreviated Journal | |
| Volume | Issue | Pages | 538–543 | ||
| Keywords | |||||
| Abstract | |||||
| Address |
Pasadena; CA; USA | ||||
| 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 | ICRA | ||
| Notes | RV;ADAS | Approved | no | ||
| Call Number | Admin @ si @ RTL2008 | Serial | 1144 | ||
| Permanent link to this record | |||||
| Author | Michal Drozdzal; Jordi Vitria; Santiago Segui; Carolina Malagelada; Fernando Azpiroz; Petia Radeva | ||||
| Title | Intestinal event segmentation for endoluminal video analysis | Type | Conference Article | ||
| Year | 2014 | Publication | 21st IEEE International Conference on Image Processing | Abbreviated Journal | |
| Volume | Issue | Pages | 3592 - 3596 | ||
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| Abstract | |||||
| Address |
Paris; Francia; October 2014 | ||||
| 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 | ICIP | ||
| Notes | MILAB; OR;MV | Approved | no | ||
| Call Number | Admin @ si @ DVS2014 | Serial | 2565 | ||
| Permanent link to this record | |||||
| Author | Sangeeth Reddy; Minesh Mathew; Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas; C.V. Jawahar | ||||
| Title | RoadText-1K: Text Detection and Recognition Dataset for Driving Videos | Type | Conference Article | ||
| Year | 2020 | Publication | IEEE International Conference on Robotics and Automation | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | Perceiving text is crucial to understand semantics of outdoor scenes and hence is a critical requirement to build intelligent systems for driver assistance and self-driving. Most of the existing datasets for text detection and recognition comprise still images and are mostly compiled keeping text in mind. This paper introduces a new ”RoadText-1K” dataset for text in driving videos. The dataset is 20 times larger than the existing largest dataset for text in videos. Our dataset comprises 1000 video clips of driving without any bias towards text and with annotations for text bounding boxes and transcriptions in every frame. State of the art methods for text detection,
recognition and tracking are evaluated on the new dataset and the results signify the challenges in unconstrained driving videos compared to existing datasets. This suggests that RoadText-1K is suited for research and development of reading systems, robust enough to be incorporated into more complex downstream tasks like driver assistance and self-driving. The dataset can be found at http://cvit.iiit.ac.in/research/ projects/cvit-projects/roadtext-1k |
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| Address |
Paris; Francia; ??? | ||||
| 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 | ICRA | ||
| Notes | DAG; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ RMG2020 | Serial | 3400 | ||
| Permanent link to this record | |||||
| Author | Arnau Baro; Pau Riba; Alicia Fornes | ||||
| Title | A Starting Point for Handwritten Music Recognition | Type | Conference Article | ||
| Year | 2018 | Publication | 1st International Workshop on Reading Music Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 5-6 | ||
| Keywords | Optical Music Recognition; Long Short-Term Memory; Convolutional Neural Networks; MUSCIMA++; CVCMUSCIMA | ||||
| Abstract | In the last years, the interest in Optical Music Recognition (OMR) has reawakened, especially since the appearance of deep learning. However, there are very few works addressing handwritten scores. In this work we describe a full OMR pipeline for handwritten music scores by using Convolutional and Recurrent Neural Networks that could serve as a baseline for the research community. | ||||
| Address |
Paris; France; September 2018 | ||||
| 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 | WORMS | ||
| Notes | DAG; 600.097; 601.302; 601.330; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ BRF2018 | Serial | 3223 | ||
| Permanent link to this record | |||||
| Author | Eduardo Aguilar; Bogdan Raducanu; Petia Radeva; Joost Van de Weijer | ||||
| Title | Continual Evidential Deep Learning for Out-of-Distribution Detection | Type | Conference Article | ||
| Year | 2023 | Publication | IEEE/CVF International Conference on Computer Vision (ICCV) Workshops -Visual Continual Learning workshop | Abbreviated Journal | |
| Volume | Issue | Pages | 3444-3454 | ||
| Keywords | |||||
| Abstract | Uncertainty-based deep learning models have attracted a great deal of interest for their ability to provide accurate and reliable predictions. Evidential deep learning stands out achieving remarkable performance in detecting out-of-distribution (OOD) data with a single deterministic neural network. Motivated by this fact, in this paper we propose the integration of an evidential deep learning method into a continual learning framework in order to perform simultaneously incremental object classification and OOD detection. Moreover, we analyze the ability of vacuity and dissonance to differentiate between in-distribution data belonging to old classes and OOD data. The proposed method, called CEDL, is evaluated on CIFAR-100 considering two settings consisting of 5 and 10 tasks, respectively. From the obtained results, we could appreciate that the proposed method, in addition to provide comparable results in object classification with respect to the baseline, largely outperforms OOD detection compared to several posthoc methods on three evaluation metrics: AUROC, AUPR and FPR95. | ||||
| Address |
Paris; France; October 2023 | ||||
| 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 | ICCVW | ||
| Notes | LAMP; MILAB | Approved | no | ||
| Call Number | Admin @ si @ ARR2023 | Serial | 3841 | ||
| Permanent link to this record | |||||
| Author | Albin Soutif; Antonio Carta; Andrea Cossu; Julio Hurtado; Hamed Hemati; Vincenzo Lomonaco; Joost Van de Weijer | ||||
| Title | A Comprehensive Empirical Evaluation on Online Continual Learning | Type | Conference Article | ||
| Year | 2023 | Publication | Visual Continual Learning (ICCV-W) | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | Online continual learning aims to get closer to a live learning experience by learning directly on a stream of data with temporally shifting distribution and by storing a minimum amount of data from that stream. In this empirical evaluation, we evaluate various methods from the literature that tackle online continual learning. More specifically, we focus on the class-incremental setting in the context of image classification, where the learner must learn new classes incrementally from a stream of data. We compare these methods on the Split-CIFAR100 and Split-TinyImagenet benchmarks, and measure their average accuracy, forgetting, stability, and quality of the representations, to evaluate various aspects of the algorithm at the end but also during the whole training period. We find that most methods suffer from stability and underfitting issues. However, the learned representations are comparable to i.i.d. training under the same computational budget. No clear winner emerges from the results and basic experience replay, when properly tuned and implemented, is a very strong baseline. We release our modular and extensible codebase at this https URL based on the avalanche framework to reproduce our results and encourage future research. | ||||
| Address |
Paris; France; October 2023 | ||||
| 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 | ICCVW | ||
| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ SCC2023 | Serial | 3938 | ||
| Permanent link to this record | |||||
| Author | Matej Kristan; Jiri Matas; Martin Danelljan; Michael Felsberg; Hyung Jin Chang; Luka Cehovin Zajc; Alan Lukezic; Ondrej Drbohlav; Zhongqun Zhang; Khanh-Tung Tran; Xuan-Son Vu; Johanna Bjorklund; Christoph Mayer; Yushan Zhang; Lei Ke; Jie Zhao; Gustavo Fernandez; Noor Al-Shakarji; Dong An; Michael Arens; Stefan Becker; Goutam Bhat; Sebastian Bullinger; Antoni B. Chan; Shijie Chang; Hanyuan Chen; Xin Chen; Yan Chen; Zhenyu Chen; Yangming Cheng; Yutao Cui; Chunyuan Deng; Jiahua Dong; Matteo Dunnhofer; Wei Feng; Jianlong Fu; Jie Gao; Ruize Han; Zeqi Hao; Jun-Yan He; Keji He; Zhenyu He; Xiantao Hu; Kaer Huang; Yuqing Huang; Yi Jiang; Ben Kang; Jin-Peng Lan; Hyungjun Lee; Chenyang Li; Jiahao Li; Ning Li; Wangkai Li; Xiaodi Li; Xin Li; Pengyu Liu; Yue Liu; Huchuan Lu; Bin Luo; Ping Luo; Yinchao Ma; Deshui Miao; Christian Micheloni; Kannappan Palaniappan; Hancheol Park; Matthieu Paul; HouWen Peng; Zekun Qian; Gani Rahmon; Norbert Scherer-Negenborn; Pengcheng Shao; Wooksu Shin; Elham Soltani Kazemi; Tianhui Song; Rainer Stiefelhagen; Rui Sun; Chuanming Tang; Zhangyong Tang; Imad Eddine Toubal; Jack Valmadre; Joost van de Weijer; Luc Van Gool; Jash Vira; Stephane Vujasinovic; Cheng Wan; Jia Wan; Dong Wang; Fei Wang; Feifan Wang; He Wang; Limin Wang; Song Wang; Yaowei Wang; Zhepeng Wang; Gangshan Wu; Jiannan Wu; Qiangqiang Wu; Xiaojun Wu; Anqi Xiao; Jinxia Xie; Chenlong Xu; Min Xu; Tianyang Xu; Yuanyou Xu; Bin Yan; Dawei Yang; Ming-Hsuan Yang; Tianyu Yang; Yi Yang; Zongxin Yang; Xuanwu Yin; Fisher Yu; Hongyuan Yu; Qianjin Yu; Weichen Yu; YongSheng Yuan; Zehuan Yuan; Jianlin Zhang; Lu Zhang; Tianzhu Zhang; Guodongfang Zhao; Shaochuan Zhao; Yaozong Zheng; Bineng Zhong; Jiawen Zhu; Xuefeng Zhu; Yueting Zhuang; ChengAo Zong; Kunlong Zuo | ||||
| Title | The First Visual Object Tracking Segmentation VOTS2023 Challenge Results | Type | Conference Article | ||
| Year | 2023 | Publication | Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | 1796-1818 | ||
| Keywords | |||||
| Abstract | The Visual Object Tracking Segmentation VOTS2023 challenge is the eleventh annual tracker benchmarking activity of the VOT initiative. This challenge is the first to merge short-term and long-term as well as single-target and multiple-target tracking with segmentation masks as the only target location specification. A new dataset was created; the ground truth has been withheld to prevent overfitting. New performance measures and evaluation protocols have been created along with a new toolkit and an evaluation server. Results of the presented 47 trackers indicate that modern tracking frameworks are well-suited to deal with convergence of short-term and long-term tracking and that multiple and single target tracking can be considered a single problem. A leaderboard, with participating trackers details, the source code, the datasets, and the evaluation kit are publicly available at the challenge website\footnote https://www.votchallenge.net/vots2023/. | ||||
| Address |
Paris; France; October 2023 | ||||
| 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 | ICCVW | ||
| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ KMD2023 | Serial | 3939 | ||
| Permanent link to this record | |||||
| Author | Joakim Bruslund Haurum; Sergio Escalera; Graham W. Taylor; Thomas B. | ||||
| Title | Which Tokens to Use? Investigating Token Reduction in Vision Transformers | Type | Conference Article | ||
| Year | 2023 | Publication | Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | Since the introduction of the Vision Transformer (ViT), researchers have sought to make ViTs more efficient by removing redundant information in the processed tokens. While different methods have been explored to achieve this goal, we still lack understanding of the resulting reduction patterns and how those patterns differ across token reduction methods and datasets. To close this gap, we set out to understand the reduction patterns of 10 different token reduction methods using four image classification datasets. By systematically comparing these methods on the different classification tasks, we find that the Top-K pruning method is a surprisingly strong baseline. Through in-depth analysis of the different methods, we determine that: the reduction patterns are generally not consistent when varying the capacity of the backbone model, the reduction patterns of pruning-based methods significantly differ from fixed radial patterns, and the reduction patterns of pruning-based methods are correlated across classification datasets. Finally we report that the similarity of reduction patterns is a moderate-to-strong proxy for model performance. Project page at https://vap.aau.dk/tokens. | ||||
| Address |
Paris; France; October 2023 | ||||
| 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 | ICCVW | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ BET2023 | Serial | 3940 | ||
| Permanent link to this record | |||||
| Author | Xavier Soria; Yachuan Li; Mohammad Rouhani; Angel Sappa | ||||
| Title | Tiny and Efficient Model for the Edge Detection Generalization | Type | Conference Article | ||
| Year | 2023 | Publication | Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | Most high-level computer vision tasks rely on low-level image operations as their initial processes. Operations such as edge detection, image enhancement, and super-resolution, provide the foundations for higher level image analysis. In this work we address the edge detection considering three main objectives: simplicity, efficiency, and generalization since current state-of-the-art (SOTA) edge detection models are increased in complexity for better accuracy. To achieve this, we present Tiny and Efficient Edge Detector (TEED), a light convolutional neural network with only 58K parameters, less than 0:2% of the state-of-the-art models. Training on the BIPED dataset takes less than 30 minutes, with each epoch requiring less than 5 minutes. Our proposed model is easy to train and it quickly converges within very first few epochs, while the predicted edge-maps are crisp and of high quality. Additionally, we propose a new dataset to test the generalization of edge detection, which comprises samples from popular images used in edge detection and image segmentation. The source code is available in https://github.com/xavysp/TEED. | ||||
| Address |
Paris; France; October 2023 | ||||
| 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 | ICCVW | ||
| Notes | MSIAU | Approved | no | ||
| Call Number | Admin @ si @ SLR2023 | Serial | 3941 | ||
| Permanent link to this record | |||||
| Author | Damian Sojka; Sebastian Cygert; Bartlomiej Twardowski; Tomasz Trzcinski | ||||
| Title | AR-TTA: A Simple Method for Real-World Continual Test-Time Adaptation | Type | Conference Article | ||
| Year | 2023 | Publication | Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | 3491-3495 | ||
| Keywords | |||||
| Abstract | Test-time adaptation is a promising research direction that allows the source model to adapt itself to changes in data distribution without any supervision. Yet, current methods are usually evaluated on benchmarks that are only a simplification of real-world scenarios. Hence, we propose to validate test-time adaptation methods using the recently introduced datasets for autonomous driving, namely CLAD-C and SHIFT. We observe that current test-time adaptation methods struggle to effectively handle varying degrees of domain shift, often resulting in degraded performance that falls below that of the source model. We noticed that the root of the problem lies in the inability to preserve the knowledge of the source model and adapt to dynamically changing, temporally correlated data streams. Therefore, we enhance well-established self-training framework by incorporating a small memory buffer to increase model stability and at the same time perform dynamic adaptation based on the intensity of domain shift. The proposed method, named AR-TTA, outperforms existing approaches on both synthetic and more real-world benchmarks and shows robustness across a variety of TTA scenarios. | ||||
| Address |
Paris; France; October 2023 | ||||
| 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 | ICCVW | ||
| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ SCT2023 | Serial | 3943 | ||
| Permanent link to this record | |||||