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Stepan Simsa; Michal Uricar; Milan Sulc; Yash Patel; Ahmed Hamdi; Matej Kocian; Matyas Skalicky; Jiri Matas; Antoine Doucet; Mickael Coustaty; Dimosthenis Karatzas |
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Title |
Overview of DocILE 2023: Document Information Localization and Extraction |
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Conference Article |
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Year |
2023 |
Publication |
International Conference of the Cross-Language Evaluation Forum for European Languages |
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14163 |
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276–293 |
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Information Extraction; Computer Vision; Natural Language Processing; Optical Character Recognition; Document Understanding |
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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. |
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Thessaloniki; Greece; September 2023 |
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CLEF |
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DAG |
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no |
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Admin @ si @ SUS2023a |
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3924 |
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Author |
Marcos V Conde; Florin Vasluianu; Javier Vazquez; Radu Timofte |
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Title |
Perceptual image enhancement for smartphone real-time applications |
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Conference Article |
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Year |
2023 |
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Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision |
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1848-1858 |
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Recent advances in camera designs and imaging pipelines allow us to capture high-quality images using smartphones. However, due to the small size and lens limitations of the smartphone cameras, we commonly find artifacts or degradation in the processed images. The most common unpleasant effects are noise artifacts, diffraction artifacts, blur, and HDR overexposure. Deep learning methods for image restoration can successfully remove these artifacts. However, most approaches are not suitable for real-time applications on mobile devices due to their heavy computation and memory requirements. In this paper, we propose LPIENet, a lightweight network for perceptual image enhancement, with the focus on deploying it on smartphones. Our experiments show that, with much fewer parameters and operations, our model can deal with the mentioned artifacts and achieve competitive performance compared with state-of-the-art methods on standard benchmarks. Moreover, to prove the efficiency and reliability of our approach, we deployed the model directly on commercial smartphones and evaluated its performance. Our model can process 2K resolution images under 1 second in mid-level commercial smartphones. |
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Waikoloa; Hawai; USA; January 2023 |
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WACV |
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MACO; CIC |
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no |
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Admin @ si @ CVV2023 |
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3900 |
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Simone Zini; Alex Gomez-Villa; Marco Buzzelli; Bartlomiej Twardowski; Andrew D. Bagdanov; Joost Van de Weijer |
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Planckian Jitter: countering the color-crippling effects of color jitter on self-supervised training |
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Conference Article |
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2023 |
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11th International Conference on Learning Representations |
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Several recent works on self-supervised learning are trained by mapping different augmentations of the same image to the same feature representation. The data augmentations used are of crucial importance to the quality of learned feature representations. In this paper, we analyze how the color jitter traditionally used in data augmentation negatively impacts the quality of the color features in learned feature representations. To address this problem, we propose a more realistic, physics-based color data augmentation – which we call Planckian Jitter – that creates realistic variations in chromaticity and produces a model robust to illumination changes that can be commonly observed in real life, while maintaining the ability to discriminate image content based on color information. Experiments confirm that such a representation is complementary to the representations learned with the currently-used color jitter augmentation and that a simple concatenation leads to significant performance gains on a wide range of downstream datasets. In addition, we present a color sensitivity analysis that documents the impact of different training methods on model neurons and shows that the performance of the learned features is robust with respect to illuminant variations. |
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1 -5 May 2023, Kigali, Ruanda |
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ICLR |
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LAMP; 600.147; 611.008; 5300006 |
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no |
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Admin @ si @ ZGB2023 |
Serial |
3820 |
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Author |
Guillermo Torres; Jan Rodríguez Dueñas; Sonia Baeza; Antoni Rosell; Carles Sanchez; Debora Gil |
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Prediction of Malignancy in Lung Cancer using several strategies for the fusion of Multi-Channel Pyradiomics Images |
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Conference Article |
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2023 |
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7th Workshop on Digital Image Processing for Medical and Automotive Industry in the framework of SYNASC 2023 |
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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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DIPMAI |
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IAM |
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no |
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Admin @ si @ TRB2023 |
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3926 |
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Ruben Tito; Khanh Nguyen; Marlon Tobaben; Raouf Kerkouche; Mohamed Ali Souibgui; Kangsoo Jung; Lei Kang; Ernest Valveny; Antti Honkela; Mario Fritz; Dimosthenis Karatzas |
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Title |
Privacy-Aware Document Visual Question Answering |
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Miscellaneous |
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Year |
2023 |
Publication |
Arxiv |
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Document Visual Question Answering (DocVQA) is a fast growing branch of document understanding. Despite the fact that documents contain sensitive or copyrighted information, none of the current DocVQA methods offers strong privacy guarantees.
In this work, we explore privacy in the domain of DocVQA for the first time. We highlight privacy issues in state of the art multi-modal LLM models used for DocVQA, and explore possible solutions.
Specifically, we focus on the invoice processing use case as a realistic, widely used scenario for document understanding, and propose a large scale DocVQA dataset comprising invoice documents and associated questions and answers. We employ a federated learning scheme, that reflects the real-life distribution of documents in different businesses, and we explore the use case where the ID of the invoice issuer is the sensitive information to be protected.
We demonstrate that non-private models tend to memorise, behaviour that can lead to exposing private information. We then evaluate baseline training schemes employing federated learning and differential privacy in this multi-modal scenario, where the sensitive information might be exposed through any of the two input modalities: vision (document image) or language (OCR tokens).
Finally, we design an attack exploiting the memorisation effect of the model, and demonstrate its effectiveness in probing different DocVQA models. |
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DAG |
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no |
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Admin @ si @ PNT2023 |
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4012 |
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Author |
Antonio Carta; Andrea Cossu; Vincenzo Lomonaco; Davide Bacciu; Joost Van de Weijer |
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Title |
Projected Latent Distillation for Data-Agnostic Consolidation in Distributed Continual Learning |
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Miscellaneous |
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2023 |
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Arxiv |
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Distributed learning on the edge often comprises self-centered devices (SCD) which learn local tasks independently and are unwilling to contribute to the performance of other SDCs. How do we achieve forward transfer at zero cost for the single SCDs? We formalize this problem as a Distributed Continual Learning scenario, where SCD adapt to local tasks and a CL model consolidates the knowledge from the resulting stream of models without looking at the SCD's private data. Unfortunately, current CL methods are not directly applicable to this scenario. We propose Data-Agnostic Consolidation (DAC), a novel double knowledge distillation method that consolidates the stream of SC models without using the original data. DAC performs distillation in the latent space via a novel Projected Latent Distillation loss. Experimental results show that DAC enables forward transfer between SCDs and reaches state-of-the-art accuracy on Split CIFAR100, CORe50 and Split TinyImageNet, both in reharsal-free and distributed CL scenarios. Somewhat surprisingly, even a single out-of-distribution image is sufficient as the only source of data during consolidation. |
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LAMP |
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no |
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Admin @ si @ CCL2023 |
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3871 |
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Zahra Raisi-Estabragh; Carlos Martin-Isla; Louise Nissen; Liliana Szabo; Victor M. Campello; Sergio Escalera; Simon Winther; Morten Bottcher; Karim Lekadir; and Steffen E. Petersen |
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Radiomics analysis enhances the diagnostic performance of CMR stress perfusion: a proof-of-concept study using the Dan-NICAD dataset |
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2023 |
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Frontiers in Cardiovascular Medicine |
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FCM |
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HUPBA |
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no |
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Admin @ si @ RMN2023 |
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3937 |
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Siyang Song; Micol Spitale; Cheng Luo; German Barquero; Cristina Palmero; Sergio Escalera; Michel Valstar; Tobias Baur; Fabien Ringeval; Elisabeth Andre; Hatice Gunes |
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REACT2023: The First Multiple Appropriate Facial Reaction Generation Challenge |
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Conference Article |
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2023 |
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Proceedings of the 31st ACM International Conference on Multimedia |
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9620–9624 |
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The Multiple Appropriate Facial Reaction Generation Challenge (REACT2023) is the first competition event focused on evaluating multimedia processing and machine learning techniques for generating human-appropriate facial reactions in various dyadic interaction scenarios, with all participants competing strictly under the same conditions. The goal of the challenge is to provide the first benchmark test set for multi-modal information processing and to foster collaboration among the audio, visual, and audio-visual behaviour analysis and behaviour generation (a.k.a generative AI) communities, to compare the relative merits of the approaches to automatic appropriate facial reaction generation under different spontaneous dyadic interaction conditions. This paper presents: (i) the novelties, contributions and guidelines of the REACT2023 challenge; (ii) the dataset utilized in the challenge; and (iii) the performance of the baseline systems on the two proposed sub-challenges: Offline Multiple Appropriate Facial Reaction Generation and Online Multiple Appropriate Facial Reaction Generation, respectively. The challenge baseline code is publicly available at https://github.com/reactmultimodalchallenge/baseline_react2023. |
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Otawa; Canada; October 2023 |
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MM |
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HUPBA |
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no |
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Admin @ si @ SSL2023 |
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3931 |
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George Tom; Minesh Mathew; Sergi Garcia Bordils; Dimosthenis Karatzas; CV Jawahar |
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Reading Between the Lanes: Text VideoQA on the Road |
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Conference Article |
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2023 |
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17th International Conference on Document Analysis and Recognition |
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14192 |
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137–154 |
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VideoQA; scene text; driving videos |
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Text and signs around roads provide crucial information for drivers, vital for safe navigation and situational awareness. Scene text recognition in motion is a challenging problem, while textual cues typically appear for a short time span, and early detection at a distance is necessary. Systems that exploit such information to assist the driver should not only extract and incorporate visual and textual cues from the video stream but also reason over time. To address this issue, we introduce RoadTextVQA, a new dataset for the task of video question answering (VideoQA) in the context of driver assistance. RoadTextVQA consists of 3, 222 driving videos collected from multiple countries, annotated with 10, 500 questions, all based on text or road signs present in the driving videos. We assess the performance of state-of-the-art video question answering models on our RoadTextVQA dataset, highlighting the significant potential for improvement in this domain and the usefulness of the dataset in advancing research on in-vehicle support systems and text-aware multimodal question answering. The dataset is available at http://cvit.iiit.ac.in/research/projects/cvit-projects/roadtextvqa. |
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San Jose; CA; USA; August 2023 |
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ICDAR |
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DAG |
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no |
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Admin @ si @ TMG2023 |
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3906 |
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Daniel Marczak; Sebastian Cygert; Tomasz Trzcinski; Bartlomiej Twardowski |
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Revisiting Supervision for Continual Representation Learning |
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2023 |
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Arxiv |
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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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xxx |
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Admin @ si @ MCT2023 |
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4013 |
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David Pujol Perich; Albert Clapes; Sergio Escalera |
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SADA: Semantic adversarial unsupervised domain adaptation for Temporal Action Localization |
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2023 |
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Arxiv |
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Temporal Action Localization (TAL) is a complex task that poses relevant challenges, particularly when attempting to generalize on new -- unseen -- domains in real-world applications. These scenarios, despite realistic, are often neglected in the literature, exposing these solutions to important performance degradation. In this work, we tackle this issue by introducing, for the first time, an approach for Unsupervised Domain Adaptation (UDA) in sparse TAL, which we refer to as Semantic Adversarial unsupervised Domain Adaptation (SADA). Our contributions are threefold: (1) we pioneer the development of a domain adaptation model that operates on realistic sparse action detection benchmarks; (2) we tackle the limitations of global-distribution alignment techniques by introducing a novel adversarial loss that is sensitive to local class distributions, ensuring finer-grained adaptation; and (3) we present a novel set of benchmarks based on EpicKitchens100 and CharadesEgo, that evaluate multiple domain shifts in a comprehensive manner. Our experiments indicate that SADA improves the adaptation across domains when compared to fully supervised state-of-the-art and alternative UDA methods, attaining a performance boost of up to 6.14% mAP. |
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HUPBA |
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no |
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Admin @ si @ PCE2023 |
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4014 |
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Yi Xiao; Felipe Codevilla; Diego Porres; Antonio Lopez |
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Scaling Vision-Based End-to-End Autonomous Driving with Multi-View Attention Learning |
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2023 |
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International Conference on Intelligent Robots and Systems |
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On end-to-end driving, human driving demonstrations are used to train perception-based driving models by imitation learning. This process is supervised on vehicle signals (e.g., steering angle, acceleration) but does not require extra costly supervision (human labeling of sensor data). As a representative of such vision-based end-to-end driving models, CILRS is commonly used as a baseline to compare with new driving models. So far, some latest models achieve better performance than CILRS by using expensive sensor suites and/or by using large amounts of human-labeled data for training. Given the difference in performance, one may think that it is not worth pursuing vision-based pure end-to-end driving. However, we argue that this approach still has great value and potential considering cost and maintenance. In this paper, we present CIL++, which improves on CILRS by both processing higher-resolution images using a human-inspired HFOV as an inductive bias and incorporating a proper attention mechanism. CIL++ achieves competitive performance compared to models which are more costly to develop. We propose to replace CILRS with CIL++ as a strong vision-based pure end-to-end driving baseline supervised by only vehicle signals and trained by conditional imitation learning. |
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Detroit; USA; October 2023 |
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IROS |
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ADAS |
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Admin @ si @ XCP2023 |
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3930 |
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Fei Yang; Kai Wang; Joost Van de Weijer |
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ScrollNet: DynamicWeight Importance for Continual Learning |
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Conference Article |
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2023 |
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Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops |
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3345-3355 |
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The principle underlying most existing continual learning (CL) methods is to prioritize stability by penalizing changes in parameters crucial to old tasks, while allowing for plasticity in other parameters. The importance of weights for each task can be determined either explicitly through learning a task-specific mask during training (e.g., parameter isolation-based approaches) or implicitly by introducing a regularization term (e.g., regularization-based approaches). However, all these methods assume that the importance of weights for each task is unknown prior to data exposure. In this paper, we propose ScrollNet as a scrolling neural network for continual learning. ScrollNet can be seen as a dynamic network that assigns the ranking of weight importance for each task before data exposure, thus achieving a more favorable stability-plasticity tradeoff during sequential task learning by reassigning this ranking for different tasks. Additionally, we demonstrate that ScrollNet can be combined with various CL methods, including regularization-based and replay-based approaches. Experimental results on CIFAR100 and TinyImagenet datasets show the effectiveness of our proposed method. |
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Paris; France; October 2023 |
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no |
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Admin @ si @ WWW2023 |
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3945 |
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Pau Torras; Mohamed Ali Souibgui; Sanket Biswas; Alicia Fornes |
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Title |
Segmentation-Free Alignment of Arbitrary Symbol Transcripts to Images |
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Conference Article |
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2023 |
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Document Analysis and Recognition – ICDAR 2023 Workshops |
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14193 |
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83-93 |
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Historical Manuscripts; Symbol Alignment |
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Developing arbitrary symbol recognition systems is a challenging endeavour. Even using content-agnostic architectures such as few-shot models, performance can be substantially improved by providing a number of well-annotated examples into training. In some contexts, transcripts of the symbols are available without any position information associated to them, which enables using line-level recognition architectures. A way of providing this position information to detection-based architectures is finding systems that can align the input symbols with the transcription. In this paper we discuss some symbol alignment techniques that are suitable for low-data scenarios and provide an insight on their perceived strengths and weaknesses. In particular, we study the usage of Connectionist Temporal Classification models, Attention-Based Sequence to Sequence models and we compare them with the results obtained on a few-shot recognition system. |
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ICDAR |
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DAG |
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no |
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Admin @ si @ TSS2023 |
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3850 |
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Subhajit Maity; Sanket Biswas; Siladittya Manna; Ayan Banerjee; Josep Llados; Saumik Bhattacharya; Umapada Pal |
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Title |
SelfDocSeg: A Self-Supervised vision-based Approach towards Document Segmentation |
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Conference Article |
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2023 |
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17th International Conference on Doccument Analysis and Recognition |
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14187 |
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342–360 |
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Document layout analysis is a known problem to the documents research community and has been vastly explored yielding a multitude of solutions ranging from text mining, and recognition to graph-based representation, visual feature extraction, etc. However, most of the existing works have ignored the crucial fact regarding the scarcity of labeled data. With growing internet connectivity to personal life, an enormous amount of documents had been available in the public domain and thus making data annotation a tedious task. We address this challenge using self-supervision and unlike, the few existing self-supervised document segmentation approaches which use text mining and textual labels, we use a complete vision-based approach in pre-training without any ground-truth label or its derivative. Instead, we generate pseudo-layouts from the document images to pre-train an image encoder to learn the document object representation and localization in a self-supervised framework before fine-tuning it with an object detection model. We show that our pipeline sets a new benchmark in this context and performs at par with the existing methods and the supervised counterparts, if not outperforms. The code is made publicly available at: this https URL |
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Document Layout Analysis; Document |
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Admin @ si @ MBM2023 |
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3990 |
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