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| Author | Spencer Low; Oliver Nina; Angel Sappa; Erik Blasch; Nathan Inkawhich | ||||
| Title | Multi-Modal Aerial View Object Classification Challenge Results-PBVS 2023 | Type | Conference Article | ||
| Year | 2023 | Publication | Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | 412-421 | ||
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| Abstract | This paper presents the findings and results of the third edition of the Multi-modal Aerial View Object Classification (MAVOC) challenge in a detailed and comprehensive manner. The challenge consists of two tracks. The primary aim of both tracks is to encourage research into building recognition models that utilize both synthetic aperture radar (SAR) and electro-optical (EO) imagery. Participating teams are encouraged to develop multi-modal approaches that incorporate complementary information from both domains. While the 2021 challenge demonstrated the feasibility of combining both modalities, the 2022 challenge expanded on the capability of multi-modal models. The 2023 challenge introduces a refined version of the UNICORN dataset and demonstrates significant improvements made. The 2023 challenge adopts an updated UNIfied CO-incident Optical and Radar for recognitioN (UNICORN V2) dataset and competition format. Two tasks are featured: SAR classification and SAR + EO classification. In addition to measuring accuracy of models, we also introduce out-of-distribution measures to encourage model robustness.The majority of this paper is dedicated to discussing the top performing methods and evaluating their performance on our blind test set. It is worth noting that all of the top ten teams outperformed the Resnet-50 baseline. The top team for SAR classification achieved a 173% performance improvement over the baseline, while the top team for SAR + EO classification achieved a 175% improvement. | ||||
| Address | Vancouver; Canada; June 2023 | ||||
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CVPRW | ||
| Notes | MSIAU | Approved | no | ||
| Call Number | Admin @ si @ LNS2023b | Serial | 3915 | ||
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| Author | Chenshen Wu; Joost Van de Weijer | ||||
| Title | Density Map Distillation for Incremental Object Counting | Type | Conference Article | ||
| Year | 2023 | Publication | Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | 2505-2514 | ||
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| Abstract | We investigate the problem of incremental learning for object counting, where a method must learn to count a variety of object classes from a sequence of datasets. A naïve approach to incremental object counting would suffer from catastrophic forgetting, where it would suffer from a dramatic performance drop on previous tasks. In this paper, we propose a new exemplar-free functional regularization method, called Density Map Distillation (DMD). During training, we introduce a new counter head for each task and introduce a distillation loss to prevent forgetting of previous tasks. Additionally, we introduce a cross-task adaptor that projects the features of the current backbone to the previous backbone. This projector allows for the learning of new features while the backbone retains the relevant features for previous tasks. Finally, we set up experiments of incremental learning for counting new objects. Results confirm that our method greatly reduces catastrophic forgetting and outperforms existing methods. | ||||
| Address | Vancouver; Canada; June 2023 | ||||
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| Area | Expedition | Conference |
CVPRW | ||
| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ WuW2023 | Serial | 3916 | ||
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| Author | Hao Fang; Ajian Liu; Jun Wan; Sergio Escalera; Hugo Jair Escalante; Zhen Lei | ||||
| Title | Surveillance Face Presentation Attack Detection Challenge | Type | Conference Article | ||
| Year | 2023 | Publication | Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | 6360-6370 | ||
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| Abstract | Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, most of the studies lacked consideration of long-distance scenarios. Specifically, compared with FAS in traditional scenes such as phone unlocking, face payment, and self-service security inspection, FAS in long-distance such as station squares, parks, and self-service supermarkets are equally important, but it has not been sufficiently explored yet. In order to fill this gap in the FAS community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask). SuHiFiMask contains 10,195 videos from 101 subjects of different age groups, which are collected by 7 mainstream surveillance cameras. Based on this dataset and protocol-3 for evaluating the robustness of the algorithm under quality changes, we organized a face presentation attack detection challenge in surveillance scenarios. It attracted 180 teams for the development phase with a total of 37 teams qualifying for the final round. The organization team re-verified and re-ran the submitted code and used the results as the final ranking. In this paper, we present an overview of the challenge, including an introduction to the dataset used, the definition of the protocol, the evaluation metrics, and the announcement of the competition results. Finally, we present the top-ranked algorithms and the research ideas provided by the competition for attack detection in long-range surveillance scenarios. | ||||
| Address | Vancouver; Canada; June 2023 | ||||
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| Area | Expedition | Conference |
CVPRW | ||
| Notes | HuPBA | Approved | no | ||
| Call Number | Admin @ si @ FLW2023 | Serial | 3917 | ||
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| Author | Galadrielle Humblot-Renaux; Sergio Escalera; Thomas B. Moeslund | ||||
| Title | Beyond AUROC & co. for evaluating out-of-distribution detection performance | Type | Conference Article | ||
| Year | 2023 | Publication | Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | 3880-3889 | ||
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| Abstract | While there has been a growing research interest in developing out-of-distribution (OOD) detection methods, there has been comparably little discussion around how these methods should be evaluated. Given their relevance for safe(r) AI, it is important to examine whether the basis for comparing OOD detection methods is consistent with practical needs. In this work, we take a closer look at the go-to metrics for evaluating OOD detection, and question the approach of exclusively reducing OOD detection to a binary classification task with little consideration for the detection threshold. We illustrate the limitations of current metrics (AUROC & its friends) and propose a new metric – Area Under the Threshold Curve (AUTC), which explicitly penalizes poor separation between ID and OOD samples. Scripts and data are available at https://github.com/glhr/beyond-auroc | ||||
| Address | Vancouver; Canada; June 2023 | ||||
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| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference |
CVPRW | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ HEM2023 | Serial | 3918 | ||
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| Author | Dong Wang; Jia Guo; Qiqi Shao; Haochi He; Zhian Chen; Chuanbao Xiao; Ajian Liu; Sergio Escalera; Hugo Jair Escalante; Zhen Lei; Jun Wan; Jiankang Deng | ||||
| Title | Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results | Type | Conference Article | ||
| Year | 2023 | Publication | Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | 6379-6390 | ||
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| Abstract | Face anti-spoofing (FAS) is an essential mechanism for safeguarding the integrity of automated face recognition systems. Despite substantial advancements, the generalization of existing approaches to real-world applications remains challenging. This limitation can be attributed to the scarcity and lack of diversity in publicly available FAS datasets, which often leads to overfitting during training or saturation during testing. In terms of quantity, the number of spoof subjects is a critical determinant. Most datasets comprise fewer than 2,000 subjects. With regard to diversity, the majority of datasets consist of spoof samples collected in controlled environments using repetitive, mechanical processes. This data collection methodology results in homogenized samples and a dearth of scenario diversity. To address these shortcomings, we introduce the Wild Face Anti-Spoofing (WFAS) dataset, a large-scale, diverse FAS dataset collected in unconstrained settings. Our dataset encompasses 853,729 images of 321,751 spoof subjects and 529,571 images of 148,169 live subjects, representing a substantial increase in quantity. Moreover, our dataset incorporates spoof data obtained from the internet, spanning a wide array of scenarios and various commercial sensors, including 17 presentation attacks (PAs) that encompass both 2D and 3D forms. This novel data collection strategy markedly enhances FAS data diversity. Leveraging the WFAS dataset and Protocol 1 (Known-Type), we host the Wild Face Anti-Spoofing Challenge at the CVPR2023 workshop. Additionally, we meticulously evaluate representative methods using Protocol 1 and Protocol 2 (Unknown-Type). Through an in-depth examination of the challenge outcomes and benchmark baselines, we provide insightful analyses and propose potential avenues for future research. The dataset is released under Insightface 1 . | ||||
| Address | Vancouver; Canada; June 2023 | ||||
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| Area | Expedition | Conference |
CVPRW | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ WGS2023 | Serial | 3919 | ||
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| Author | Patricia Marquez; Debora Gil; Aura Hernandez-Sabate | ||||
| Title | Evaluation of the Capabilities of Confidence Measures for Assessing Optical Flow Quality | Type | Conference Article | ||
| Year | 2013 | Publication | ICCV Workshop on Computer Vision in Vehicle Technology: From Earth to Mars | Abbreviated Journal | |
| Volume | Issue | Pages | 624-631 | ||
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| Abstract | Assessing Optical Flow (OF) quality is essential for its further use in reliable decision support systems. The absence of ground truth in such situations leads to the computation of OF Confidence Measures (CM) obtained from either input or output data. A fair comparison across the capabilities of the different CM for bounding OF error is required in order to choose the best OF-CM pair for discarding points where OF computation is not reliable. This paper presents a statistical probabilistic framework for assessing the quality of a given CM. Our quality measure is given in terms of the percentage of pixels whose OF error bound can not be determined by CM values. We also provide statistical tools for the computation of CM values that ensures a given accuracy of the flow field. | ||||
| Address | Sydney; Australia; December 2013 | ||||
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| Area | Expedition | Conference |
CVTT:E2M | ||
| Notes | IAM; ADAS; 600.044; 600.057; 601.145 | Approved | no | ||
| Call Number | Admin @ si @ MGH2013b | Serial | 2351 | ||
| Permanent link to this record | |||||
| Author | Jose Manuel Alvarez; Theo Gevers; Antonio Lopez | ||||
| Title | Evaluating Color Representation for Online Road Detection | Type | Conference Article | ||
| Year | 2013 | Publication | ICCV Workshop on Computer Vision in Vehicle Technology: From Earth to Mars | Abbreviated Journal | |
| Volume | Issue | Pages | 594-595 | ||
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| Abstract | Detecting traversable road areas ahead a moving vehicle is a key process for modern autonomous driving systems. Most existing algorithms use color to classify pixels as road or background. These algorithms reduce the effect of lighting variations and weather conditions by exploiting the discriminant/invariant properties of different color representations. However, up to date, no comparison between these representations have been conducted. Therefore, in this paper, we perform an evaluation of existing color representations for road detection. More specifically, we focus on color planes derived from RGB data and their most com-
mon combinations. The evaluation is done on a set of 7000 road images acquired using an on-board camera in different real-driving situations. |
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| Area | Expedition | Conference |
CVVT:E2M | ||
| Notes | ADAS;ISE | Approved | no | ||
| Call Number | Admin @ si @ AGL2013 | Serial | 2794 | ||
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| Author | David Vazquez; Antonio Lopez; Daniel Ponsa; Javier Marin | ||||
| Title | Cool world: domain adaptation of virtual and real worlds for human detection using active learning | Type | Conference Article | ||
| Year | 2011 | Publication | NIPS Domain Adaptation Workshop: Theory and Application | Abbreviated Journal | NIPS-DA |
| Volume | Issue | Pages | |||
| Keywords | Pedestrian Detection; Virtual; Domain Adaptation; Active Learning | ||||
| Abstract | Image based human detection is of paramount interest for different applications. The most promising human detectors rely on discriminatively learnt classifiers, i.e., trained with labelled samples. However, labelling is a manual intensive task, especially in cases like human detection where it is necessary to provide at least bounding boxes framing the humans for training. To overcome such problem, in Marin et al. we have proposed the use of a virtual world where the labels of the different objects are obtained automatically. This means that the human models (classifiers) are learnt using the appearance of realistic computer graphics. Later, these models are used for human detection in images of the real world. The results of this technique are surprisingly good. However, these are not always as good as the classical approach of training and testing with data coming from the same camera and the same type of scenario. Accordingly, in Vazquez et al. we cast the problem as one of supervised domain adaptation. In doing so, we assume that a small amount of manually labelled samples from real-world images is required. To collect these labelled samples we use an active learning technique. Thus, ultimately our human model is learnt by the combination of virtual- and real-world labelled samples which, to the best of our knowledge, was not done before. Here, we term such combined space cool world. In this extended abstract we summarize our proposal, and include quantitative results from Vazquez et al. showing its validity. | ||||
| Address | Granada, Spain | ||||
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| Publisher | Place of Publication | Granada, Spain | Editor | ||
| Language | English | Summary Language | English | Original Title | |
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| Area | Expedition | Conference |
DA-NIPS | ||
| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ VLP2011b | Serial | 1756 | ||
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| Author | Partha Pratim Roy; Umapada Pal; Josep Llados | ||||
| Title | Multi-oriented English Text Line Extraction using Background and Foreground Information | Type | Conference Article | ||
| Year | 2008 | Publication | Proceedings of the 8th IAPR International Workshop on Document Analysis Systems, | Abbreviated Journal | |
| Volume | Issue | Pages | 315–322 | ||
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| Address | Nara (Japo) | ||||
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DAS | ||
| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ RPL2008b | Serial | 1047 | ||
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| Author | Marçal Rusiñol; Josep Llados | ||||
| Title | Word and Symbol Spotting using Spatial Organization of Local Descriptors | Type | Conference Article | ||
| Year | 2008 | Publication | Proceedings of the 8th IAPR International Workshop on Document Analysis Systems, | Abbreviated Journal | |
| Volume | Issue | Pages | 489–496 | ||
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| Abstract | |||||
| Address | Nara (Japan) | ||||
| Corporate Author | Thesis | ||||
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DAS | ||
| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ RuL2008b | Serial | 1059 | ||
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| Author | Mathieu Nicolas Delalandre; Ernest Valveny; Josep Llados | ||||
| Title | Performance Evaluation of Symbol Recognition and Spotting Systems | Type | Conference Article | ||
| Year | 2008 | Publication | Proceedings of the 8th International Workshop on Document Analysis Systems, | Abbreviated Journal | |
| Volume | Issue | Pages | 497–505 | ||
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| Abstract | |||||
| Address | Nara (Japan) | ||||
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| Area | Expedition | Conference |
DAS | ||
| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ DVL2008b | Serial | 1060 | ||
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| Author | Joan Mas; Jose Antonio Rodriguez; Dimosthenis Karatzas; Gemma Sanchez; Josep Llados | ||||
| Title | HistoSketch: A Semi-Automatic Annotation Tool for Archival Documents | Type | Conference Article | ||
| Year | 2008 | Publication | Proceedings of the 8th International Workshop on Document Analysis Systems, | Abbreviated Journal | |
| Volume | Issue | Pages | 517–524 | ||
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| Abstract | |||||
| Address | Nara (Japan) | ||||
| Corporate Author | Thesis | ||||
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| Language | Summary Language | Original Title | |||
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DAS | ||
| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ MRK2008a | Serial | 1061 | ||
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| Author | Dimosthenis Karatzas | ||||
| Title | Detecting Gradients in Text Images Using the Hough Transform | Type | Conference Article | ||
| Year | 2008 | Publication | Proceedings of the 8th International Workshop on Document Analysis Systems, | Abbreviated Journal | |
| Volume | Issue | Pages | 245–252 | ||
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| Abstract | |||||
| Address | Nara (Japan) | ||||
| Corporate Author | Thesis | ||||
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| Area | Expedition | Conference |
DAS | ||
| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ Kar2008 | Serial | 1062 | ||
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| Author | Alicia Fornes; Josep Llados; Gemma Sanchez; Horst Bunke | ||||
| Title | Writer Identification in Old Handwritten Music Scores | Type | Conference Article | ||
| Year | 2008 | Publication | Proceedings of the 8th International Workshop on Document Analysis Systems, | Abbreviated Journal | |
| Volume | Issue | Pages | 347–353 | ||
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| Abstract | |||||
| Address | Nara (Japan) | ||||
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| Area | Expedition | Conference |
DAS | ||
| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ FLS2008b | Serial | 1078 | ||
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| Author | Albert Gordo; Alicia Fornes; Ernest Valveny; Josep Llados | ||||
| Title | A Bag of Notes Approach to Writer Identification in Old Handwritten Music Scores | Type | Conference Article | ||
| Year | 2010 | Publication | 9th IAPR International Workshop on Document Analysis Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 247–254 | ||
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| Abstract | Determining the authorship of a document, namely writer identification, can be an important source of information for document categorization. Contrary to text documents, the identification of the writer of graphical documents is still a challenge. In this paper we present a robust approach for writer identification in a particular kind of graphical documents, old music scores. This approach adapts the bag of visual terms method for coping with graphic documents. The identification is performed only using the graphical music notation. For this purpose, we generate a graphic vocabulary without recognizing any music symbols, and consequently, avoiding the difficulties in the recognition of hand-drawn symbols in old and degraded documents. The proposed method has been tested on a database of old music scores from the 17th to 19th centuries, achieving very high identification rates. | ||||
| Address | Boston; USA; | ||||
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| ISSN | ISBN | 978-1-60558-773-8 | Medium | ||
| Area | Expedition | Conference |
DAS | ||
| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ GFV2010 | Serial | 1320 | ||
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