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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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| Area | Expedition | Conference | 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 | MSIAU | 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 | MSIAU | 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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| 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 | Senmao Li; Joost Van de Weijer; Yaxing Wang; Fahad Shahbaz Khan; Meiqin Liu; Jian Yang | ||||
| Title | 3D-Aware Multi-Class Image-to-Image Translation with NeRFs | Type | Conference Article | ||
| Year | 2023 | Publication | 36th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 12652-12662 | ||
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| Abstract | Recent advances in 3D-aware generative models (3D-aware GANs) combined with Neural Radiance Fields (NeRF) have achieved impressive results. However no prior works investigate 3D-aware GANs for 3D consistent multiclass image-to-image (3D-aware 121) translation. Naively using 2D-121 translation methods suffers from unrealistic shape/identity change. To perform 3D-aware multiclass 121 translation, we decouple this learning process into a multiclass 3D-aware GAN step and a 3D-aware 121 translation step. In the first step, we propose two novel techniques: a new conditional architecture and an effective training strategy. In the second step, based on the well-trained multiclass 3D-aware GAN architecture, that preserves view-consistency, we construct a 3D-aware 121 translation system. To further reduce the view-consistency problems, we propose several new techniques, including a U-net-like adaptor network design, a hierarchical representation constrain and a relative regularization loss. In exten-sive experiments on two datasets, quantitative and qualitative results demonstrate that we successfully perform 3D-aware 121 translation with multi-view consistency. Code is available in 3DI2I. | ||||
| Address | Vancouver; Canada; June 2023 | ||||
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| Area | Expedition | Conference | CVPR | ||
| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ LWW2023b | Serial | 3920 | ||
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| Author | Hugo Bertiche; Niloy J Mitra; Kuldeep Kulkarni; Chun Hao Paul Huang; Tuanfeng Y Wang; Meysam Madadi; Sergio Escalera; Duygu Ceylan | ||||
| Title | Blowing in the Wind: CycleNet for Human Cinemagraphs from Still Images | Type | Conference Article | ||
| Year | 2023 | Publication | 36th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 459-468 | ||
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| Abstract | Cinemagraphs are short looping videos created by adding subtle motions to a static image. This kind of media is popular and engaging. However, automatic generation of cinemagraphs is an underexplored area and current solutions require tedious low-level manual authoring by artists. In this paper, we present an automatic method that allows generating human cinemagraphs from single RGB images. We investigate the problem in the context of dressed humans under the wind. At the core of our method is a novel cyclic neural network that produces looping cinemagraphs for the target loop duration. To circumvent the problem of collecting real data, we demonstrate that it is possible, by working in the image normal space, to learn garment motion dynamics on synthetic data and generalize to real data. We evaluate our method on both synthetic and real data and demonstrate that it is possible to create compelling and plausible cinemagraphs from single RGB images. | ||||
| Address | Vancouver; Canada; June 2023 | ||||
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| Area | Expedition | Conference | CVPR | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ BMK2023 | Serial | 3921 | ||
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| Author | Albin Soutif; Antonio Carta; Joost Van de Weijer | ||||
| Title | Improving Online Continual Learning Performance and Stability with Temporal Ensembles | Type | Conference Article | ||
| Year | 2023 | Publication | 2nd Conference on Lifelong Learning Agents | Abbreviated Journal | |
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| Abstract | Neural networks are very effective when trained on large datasets for a large number of iterations. However, when they are trained on non-stationary streams of data and in an online fashion, their performance is reduced (1) by the online setup, which limits the availability of data, (2) due to catastrophic forgetting because of the non-stationary nature of the data. Furthermore, several recent works (Caccia et al., 2022; Lange et al., 2023) arXiv:2205.13452 showed that replay methods used in continual learning suffer from the stability gap, encountered when evaluating the model continually (rather than only on task boundaries). In this article, we study the effect of model ensembling as a way to improve performance and stability in online continual learning. We notice that naively ensembling models coming from a variety of training tasks increases the performance in online continual learning considerably. Starting from this observation, and drawing inspirations from semi-supervised learning ensembling methods, we use a lightweight temporal ensemble that computes the exponential moving average of the weights (EMA) at test time, and show that it can drastically increase the performance and stability when used in combination with several methods from the literature. | ||||
| Address | Montreal; Canada; August 2023 | ||||
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| Area | Expedition | Conference | COLLAS | ||
| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ SCW2023 | Serial | 3922 | ||
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| Author | Cristina Palmero; Oleg V Komogortsev; Sergio Escalera; Sachin S Talathi | ||||
| Title | Multi-Rate Sensor Fusion for Unconstrained Near-Eye Gaze Estimation | Type | Conference Article | ||
| Year | 2023 | Publication | Proceedings of the 2023 Symposium on Eye Tracking Research and Applications | Abbreviated Journal | |
| Volume | Issue | Pages | 1-8 | ||
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| Abstract | The power requirements of video-oculography systems can be prohibitive for high-speed operation on portable devices. Recently, low-power alternatives such as photosensors have been evaluated, providing gaze estimates at high frequency with a trade-off in accuracy and robustness. Potentially, an approach combining slow/high-fidelity and fast/low-fidelity sensors should be able to exploit their complementarity to track fast eye motion accurately and robustly. To foster research on this topic, we introduce OpenSFEDS, a near-eye gaze estimation dataset containing approximately 2M synthetic camera-photosensor image pairs sampled at 500 Hz under varied appearance and camera position. We also formulate the task of sensor fusion for gaze estimation, proposing a deep learning framework consisting in appearance-based encoding and temporal eye-state dynamics. We evaluate several single- and multi-rate fusion baselines on OpenSFEDS, achieving 8.7% error decrease when tracking fast eye movements with a multi-rate approach vs. a gaze forecasting approach operating with a low-speed sensor alone. | ||||
| Address | Tubingen; Germany; May 2023 | ||||
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| Area | Expedition | Conference | ETRA | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ PKE2023 | Serial | 3923 | ||
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| Author | Stepan Simsa; Michal Uricar; Milan Sulc; Yash Patel; Ahmed Hamdi; Matej Kocian; Matyas Skalicky; Jiri Matas; Antoine Doucet; Mickael Coustaty; Dimosthenis Karatzas | ||||
| Title | Overview of DocILE 2023: Document Information Localization and Extraction | Type | Conference Article | ||
| Year | 2023 | Publication | International Conference of the Cross-Language Evaluation Forum for European Languages | Abbreviated Journal | |
| Volume | 14163 | Issue | Pages | 276–293 | |
| Keywords | Information Extraction; Computer Vision; Natural Language Processing; Optical Character Recognition; Document Understanding | ||||
| Abstract | This paper provides an overview of the DocILE 2023 Competition, its tasks, participant submissions, the competition results and possible future research directions. This first edition of the competition focused on two Information Extraction tasks, Key Information Localization and Extraction (KILE) and Line Item Recognition (LIR). Both of these tasks require detection of pre-defined categories of information in business documents. The second task additionally requires correctly grouping the information into tuples, capturing the structure laid out in the document. The competition used the recently published DocILE dataset and benchmark that stays open to new submissions. The diversity of the participant solutions indicates the potential of the dataset as the submissions included pure Computer Vision, pure Natural Language Processing, as well as multi-modal solutions and utilized all of the parts of the dataset, including the annotated, synthetic and unlabeled subsets. | ||||
| Address | Thessaloniki; Greece; September 2023 | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | CLEF | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ SUS2023a | Serial | 3924 | ||
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| Author | Albert Tatjer; Bhalaji Nagarajan; Ricardo Marques; Petia Radeva | ||||
| Title | CCLM: Class-Conditional Label Noise Modelling | Type | Conference Article | ||
| Year | 2023 | Publication | 11th Iberian Conference on Pattern Recognition and Image Analysis | Abbreviated Journal | |
| Volume | 14062 | Issue | Pages | 3-14 | |
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| Abstract | The performance of deep neural networks highly depends on the quality and volume of the training data. However, cost-effective labelling processes such as crowdsourcing and web crawling often lead to data with noisy (i.e., wrong) labels. Making models robust to this label noise is thus of prime importance. A common approach is using loss distributions to model the label noise. However, the robustness of these methods highly depends on the accuracy of the division of training set into clean and noisy samples. In this work, we dive in this research direction highlighting the existing problem of treating this distribution globally and propose a class-conditional approach to split the clean and noisy samples. We apply our approach to the popular DivideMix algorithm and show how the local treatment fares better with respect to the global treatment of loss distribution. We validate our hypothesis on two popular benchmark datasets and show substantial improvements over the baseline experiments. We further analyze the effectiveness of the proposal using two different metrics – Noise Division Accuracy and Classiness. | ||||
| Address | Alicante; Spain; June 2023 | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | IbPRIA | ||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ TNM2023 | Serial | 3925 | ||
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| Author | Guillermo Torres; Jan Rodríguez Dueñas; Sonia Baeza; Antoni Rosell; Carles Sanchez; Debora Gil | ||||
| Title | Prediction of Malignancy in Lung Cancer using several strategies for the fusion of Multi-Channel Pyradiomics Images | Type | Conference Article | ||
| Year | 2023 | Publication | 7th Workshop on Digital Image Processing for Medical and Automotive Industry in the framework of SYNASC 2023 | Abbreviated Journal | |
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| Abstract | This study shows the generation process and the subsequent study of the representation space obtained by extracting GLCM texture features from computer-aided tomography (CT) scans of pulmonary nodules (PN). For this, data from 92 patients from the Germans Trias i Pujol University Hospital were used. The workflow focuses on feature extraction using Pyradiomics and the VGG16 Convolutional Neural Network (CNN). The aim of the study is to assess whether the data obtained have a positive impact on the diagnosis of lung cancer (LC). To design a machine learning (ML) model training method that allows generalization, we train SVM and neural network (NN) models, evaluating diagnosis performance using metrics defined at slice and nodule level. | ||||
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| Area | Expedition | Conference | DIPMAI | ||
| Notes | IAM | Approved | no | ||
| Call Number | Admin @ si @ TRB2023 | Serial | 3926 | ||
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| Author | Patricia Suarez; Angel Sappa | ||||
| Title | Toward a Thermal Image-Like Representation | Type | Conference Article | ||
| Year | 2023 | Publication | Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications | Abbreviated Journal | |
| Volume | Issue | Pages | 133-140 | ||
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| Abstract | This paper proposes a novel model to obtain thermal image-like representations to be used as an input in any thermal image compressive sensing approach (e.g., thermal image: filtering, enhancing, super-resolution). Thermal images offer interesting information about the objects in the scene, in addition to their temperature. Unfortunately, in most of the cases thermal cameras acquire low resolution/quality images. Hence, in order to improve these images, there are several state-of-the-art approaches that exploit complementary information from a low-cost channel (visible image) to increase the image quality of an expensive channel (infrared image). In these SOTA approaches visible images are fused at different levels without paying attention the images acquire information at different bands of the spectral. In this paper a novel approach is proposed to generate thermal image-like representations from a low cost visible images, by means of a contrastive cycled GAN network. Obtained representations (synthetic thermal image) can be later on used to improve the low quality thermal image of the same scene. Experimental results on different datasets are presented. | ||||
| Address | Lisboa; Portugal; February 2023 | ||||
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| Area | Expedition | Conference | VISIGRAPP | ||
| Notes | MSIAU | Approved | no | ||
| Call Number | Admin @ si @ SuS2023b | Serial | 3927 | ||
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| Author | David Dueñas; Mostafa Kamal; Petia Radeva | ||||
| Title | Efficient Deep Learning Ensemble for Skin Lesion Classification | Type | Conference Article | ||
| Year | 2023 | Publication | Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications | Abbreviated Journal | |
| Volume | Issue | Pages | 303-314 | ||
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| Abstract | Vision Transformers (ViTs) are deep learning techniques that have been gaining in popularity in recent years.
In this work, we study the performance of ViTs and Convolutional Neural Networks (CNNs) on skin lesions classification tasks, specifically melanoma diagnosis. We show that regardless of the performance of both architectures, an ensemble of them can improve their generalization. We also present an adaptation to the Gram-OOD* method (detecting Out-of-distribution (OOD) using Gram matrices) for skin lesion images. Moreover, the integration of super-convergence was critical to success in building models with strict computing and training time constraints. We evaluated our ensemble of ViTs and CNNs, demonstrating that generalization is enhanced by placing first in the 2019 and third in the 2020 ISIC Challenge Live Leaderboards (available at https://challenge.isic-archive.com/leaderboards/live/). |
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| Address | Lisboa; Portugal; February 2023 | ||||
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| Area | Expedition | Conference | VISIGRAPP | ||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ DKR2023 | Serial | 3928 | ||
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| Author | Christian Keilstrup Ingwersen; Artur Xarles; Albert Clapes; Meysam Madadi; Janus Nortoft Jensen; Morten Rieger Hannemose; Anders Bjorholm Dahl; Sergio Escalera | ||||
| Title | Video-based Skill Assessment for Golf: Estimating Golf Handicap | Type | Conference Article | ||
| Year | 2023 | Publication | Proceedings of the 6th International Workshop on Multimedia Content Analysis in Sports | Abbreviated Journal | |
| Volume | Issue | Pages | 31-39 | ||
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| Abstract | Automated skill assessment in sports using video-based analysis holds great potential for revolutionizing coaching methodologies. This paper focuses on the problem of skill determination in golfers by leveraging deep learning models applied to a large database of video recordings of golf swings. We investigate different regression, ranking and classification based methods and compare to a simple baseline approach. The performance is evaluated using mean squared error (MSE) as well as computing the percentages of correctly ranked pairs based on the Kendall correlation. Our results demonstrate an improvement over the baseline, with a 35% lower mean squared error and 68% correctly ranked pairs. However, achieving fine-grained skill assessment remains challenging. This work contributes to the development of AI-driven coaching systems and advances the understanding of video-based skill determination in the context of golf. | ||||
| Address | Otawa; Canada; October 2023 | ||||
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| Area | Expedition | Conference | MMSports | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ KXC2023 | Serial | 3929 | ||
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