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| Author | Souhail Bakkali; Sanket Biswas; Zuheng Ming; Mickael Coustaty; Marçal Rusiñol; Oriol Ramos Terrades; Josep Llados | ||||
| Title | TransferDoc: A Self-Supervised Transferable Document Representation Learning Model Unifying Vision and Language | Type | Miscellaneous | ||
| Year | 2023 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | The field of visual document understanding has witnessed a rapid growth in emerging challenges and powerful multi-modal strategies. However, they rely on an extensive amount of document data to learn their pretext objectives in a ``pre-train-then-fine-tune'' paradigm and thus, suffer a significant performance drop in real-world online industrial settings. One major reason is the over-reliance on OCR engines to extract local positional information within a document page. Therefore, this hinders the model's generalizability, flexibility and robustness due to the lack of capturing global information within a document image. We introduce TransferDoc, a cross-modal transformer-based architecture pre-trained in a self-supervised fashion using three novel pretext objectives. TransferDoc learns richer semantic concepts by unifying language and visual representations, which enables the production of more transferable models. Besides, two novel downstream tasks have been introduced for a ``closer-to-real'' industrial evaluation scenario where TransferDoc outperforms other state-of-the-art approaches. | ||||
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| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ BBM2023 | Serial | 3995 | ||
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| Author | Ruben Tito; Khanh Nguyen; Marlon Tobaben; Raouf Kerkouche; Mohamed Ali Souibgui; Kangsoo Jung; Lei Kang; Ernest Valveny; Antti Honkela; Mario Fritz; Dimosthenis Karatzas | ||||
| Title | Privacy-Aware Document Visual Question Answering | Type | Miscellaneous | ||
| Year | 2023 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | 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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| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ PNT2023 | Serial | 4012 | ||
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| Author | Daniel Marczak; Sebastian Cygert; Tomasz Trzcinski; Bartlomiej Twardowski | ||||
| Title | Revisiting Supervision for Continual Representation Learning | Type | Miscellaneous | ||
| Year | 2023 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | In the field of continual learning, models are designed to learn tasks one after the other. While most research has centered on supervised continual learning, recent studies have highlighted the strengths of self-supervised continual representation learning. The improved transferability of representations built with self-supervised methods is often associated with the role played by the multi-layer perceptron projector. In this work, we depart from this observation and reexamine the role of supervision in continual representation learning. We reckon that additional information, such as human annotations, should not deteriorate the quality of representations. Our findings show that supervised models when enhanced with a multi-layer perceptron head, can outperform self-supervised models in continual representation learning. | ||||
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| Notes | xxx | Approved | no | ||
| Call Number | Admin @ si @ MCT2023 | Serial | 4013 | ||
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| Author | Jose Luis Gomez; Manuel Silva; Antonio Seoane; Agnes Borras; Mario Noriega; German Ros; Jose Antonio Iglesias; Antonio Lopez | ||||
| Title | All for One, and One for All: UrbanSyn Dataset, the third Musketeer of Synthetic Driving Scenes | Type | Miscellaneous | ||
| Year | 2023 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | We introduce UrbanSyn, a photorealistic dataset acquired through semi-procedurally generated synthetic urban driving scenarios. Developed using high-quality geometry and materials, UrbanSyn provides pixel-level ground truth, including depth, semantic segmentation, and instance segmentation with object bounding boxes and occlusion degree. It complements GTAV and Synscapes datasets to form what we coin as the 'Three Musketeers'. We demonstrate the value of the Three Musketeers in unsupervised domain adaptation for image semantic segmentation. Results on real-world datasets, Cityscapes, Mapillary Vistas, and BDD100K, establish new benchmarks, largely attributed to UrbanSyn. We make UrbanSyn openly and freely accessible (this http URL). | ||||
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| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ GSS2023 | Serial | 4015 | ||
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| Author | Mohamed Ali Souibgui; Sanket Biswas; Andres Mafla; Ali Furkan Biten; Alicia Fornes; Yousri Kessentini; Josep Llados; Lluis Gomez; Dimosthenis Karatzas | ||||
| Title | Text-DIAE: a self-supervised degradation invariant autoencoder for text recognition and document enhancement | Type | Conference Article | ||
| Year | 2023 | Publication | Proceedings of the 37th AAAI Conference on Artificial Intelligence | Abbreviated Journal | |
| Volume | 37 | Issue | 2 | Pages | |
| Keywords | Representation Learning for Vision; CV Applications; CV Language and Vision; ML Unsupervised; Self-Supervised Learning | ||||
| Abstract | In this paper, we propose a Text-Degradation Invariant Auto Encoder (Text-DIAE), a self-supervised model designed to tackle two tasks, text recognition (handwritten or scene-text) and document image enhancement. We start by employing a transformer-based architecture that incorporates three pretext tasks as learning objectives to be optimized during pre-training without the usage of labelled data. Each of the pretext objectives is specifically tailored for the final downstream tasks. We conduct several ablation experiments that confirm the design choice of the selected pretext tasks. Importantly, the proposed model does not exhibit limitations of previous state-of-the-art methods based on contrastive losses, while at the same time requiring substantially fewer data samples to converge. Finally, we demonstrate that our method surpasses the state-of-the-art in existing supervised and self-supervised settings in handwritten and scene text recognition and document image enhancement. Our code and trained models will be made publicly available at https://github.com/dali92002/SSL-OCR | ||||
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AAAI | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ SBM2023 | Serial | 3848 | ||
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| Author | Khanh Nguyen; Ali Furkan Biten; Andres Mafla; Lluis Gomez; Dimosthenis Karatzas | ||||
| Title | Show, Interpret and Tell: Entity-Aware Contextualised Image Captioning in Wikipedia | Type | Conference Article | ||
| Year | 2023 | Publication | Proceedings of the 37th AAAI Conference on Artificial Intelligence | Abbreviated Journal | |
| Volume | 37 | Issue | 2 | Pages | 1940-1948 |
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| Abstract | Humans exploit prior knowledge to describe images, and are able to adapt their explanation to specific contextual information given, even to the extent of inventing plausible explanations when contextual information and images do not match. In this work, we propose the novel task of captioning Wikipedia images by integrating contextual knowledge. Specifically, we produce models that jointly reason over Wikipedia articles, Wikimedia images and their associated descriptions to produce contextualized captions. The same Wikimedia image can be used to illustrate different articles, and the produced caption needs to be adapted to the specific context allowing us to explore the limits of the model to adjust captions to different contextual information. Dealing with out-of-dictionary words and Named Entities is a challenging task in this domain. To address this, we propose a pre-training objective, Masked Named Entity Modeling (MNEM), and show that this pretext task results to significantly improved models. Furthermore, we verify that a model pre-trained in Wikipedia generalizes well to News Captioning datasets. We further define two different test splits according to the difficulty of the captioning task. We offer insights on the role and the importance of each modality and highlight the limitations of our model. | ||||
| Address | Washington; USA; February 2023 | ||||
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AAAI | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ NBM2023 | Serial | 3860 | ||
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| Author | Guillermo Torres; Debora Gil; Antoni Rosell; S. Mena; Carles Sanchez | ||||
| Title | Virtual Radiomics Biopsy for the Histological Diagnosis of Pulmonary Nodules | Type | Conference Article | ||
| Year | 2023 | Publication | 37th International Congress and Exhibition is organized by Computer Assisted Radiology and Surgery | Abbreviated Journal | |
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| Abstract | Pòster | ||||
| Address | Munich; Germany; June 2023 | ||||
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CARS | ||
| Notes | IAM | Approved | no | ||
| Call Number | Admin @ si @ TGR2023a | Serial | 3950 | ||
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| Author | Mohamed Ramzy Ibrahim; Robert Benavente; Daniel Ponsa; Felipe Lumbreras | ||||
| Title | Unveiling the Influence of Image Super-Resolution on Aerial Scene Classification | Type | Conference Article | ||
| Year | 2023 | Publication | Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications | Abbreviated Journal | |
| Volume | 14469 | Issue | Pages | 214–228 | |
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| Abstract | Deep learning has made significant advances in recent years, and as a result, it is now in a stage where it can achieve outstanding results in tasks requiring visual understanding of scenes. However, its performance tends to decline when dealing with low-quality images. The advent of super-resolution (SR) techniques has started to have an impact on the field of remote sensing by enabling the restoration of fine details and enhancing image quality, which could help to increase performance in other vision tasks. However, in previous works, contradictory results for scene visual understanding were achieved when SR techniques were applied. In this paper, we present an experimental study on the impact of SR on enhancing aerial scene classification. Through the analysis of different state-of-the-art SR algorithms, including traditional methods and deep learning-based approaches, we unveil the transformative potential of SR in overcoming the limitations of low-resolution (LR) aerial imagery. By enhancing spatial resolution, more fine details are captured, opening the door for an improvement in scene understanding. We also discuss the effect of different image scales on the quality of SR and its effect on aerial scene classification. Our experimental work demonstrates the significant impact of SR on enhancing aerial scene classification compared to LR images, opening new avenues for improved remote sensing applications. | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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CIARP | ||
| Notes | MSIAU | Approved | no | ||
| Call Number | Admin @ si @ IBP2023 | Serial | 4008 | ||
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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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CLEF | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ SUS2023a | Serial | 3924 | ||
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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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COLLAS | ||
| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ SCW2023 | Serial | 3922 | ||
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| Author | Matthias Eisenmann; Annika Reinke; Vivienn Weru; Minu D. Tizabi; Fabian Isensee; Tim J. Adler; Sharib Ali; Vincent Andrearczyk; Marc Aubreville; Ujjwal Baid; Spyridon Bakas; Niranjan Balu; Sophia Bano; Jorge Bernal; Sebastian Bodenstedt; Alessandro Casella; Veronika Cheplygina; Marie Daum; Marleen de Bruijne | ||||
| Title | Why Is the Winner the Best? | Type | Conference Article | ||
| Year | 2023 | Publication | Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 19955-19966 | ||
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| Abstract | International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multi-center study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and postprocessing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work. | ||||
| Address | Vancouver; Canada; June 2023 | ||||
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CVPR | ||
| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ ERW2023 | Serial | 3842 | ||
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| Author | JW Xiao; CB Zhang; J. Feng; Xialei Liu; Joost Van de Weijer; MM Cheng | ||||
| Title | Endpoints Weight Fusion for Class Incremental Semantic Segmentation | Type | Conference Article | ||
| Year | 2023 | Publication | Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 7204-7213 | ||
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| Abstract | Class incremental semantic segmentation (CISS) focuses on alleviating catastrophic forgetting to improve discrimination. Previous work mainly exploit regularization (e.g., knowledge distillation) to maintain previous knowledge in the current model. However, distillation alone often yields limited gain to the model since only the representations of old and new models are restricted to be consistent. In this paper, we propose a simple yet effective method to obtain a model with strong memory of old knowledge, named Endpoints Weight Fusion (EWF). In our method, the model containing old knowledge is fused with the model retaining new knowledge in a dynamic fusion manner, strengthening the memory of old classes in ever-changing distributions. In addition, we analyze the relation between our fusion strategy and a popular moving average technique EMA, which reveals why our method is more suitable for class-incremental learning. To facilitate parameter fusion with closer distance in the parameter space, we use distillation to enhance the optimization process. Furthermore, we conduct experiments on two widely used datasets, achieving the state-of-the-art performance. | ||||
| Address | Vancouver; Canada; June 2023 | ||||
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CVPR | ||
| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ XZF2023 | Serial | 3854 | ||
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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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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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CVPR | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ BMK2023 | Serial | 3921 | ||
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| Author | Yawei Li; Yulun Zhang; Radu Timofte; Luc Van Gool; Zhijun Tu; Kunpeng Du; Hailing Wang; Hanting Chen; Wei Li; Xiaofei Wang; Jie Hu; Yunhe Wang; Xiangyu Kong; Jinlong Wu; Dafeng Zhang; Jianxing Zhang; Shuai Liu; Furui Bai; Chaoyu Feng; Hao Wang; Yuqian Zhang; Guangqi Shao; Xiaotao Wang; Lei Lei; Rongjian Xu; Zhilu Zhang; Yunjin Chen; Dongwei Ren; Wangmeng Zuo; Qi Wu; Mingyan Han; Shen Cheng; Haipeng Li; Ting Jiang; Chengzhi Jiang; Xinpeng Li; Jinting Luo; Wenjie Lin; Lei Yu; Haoqiang Fan; Shuaicheng Liu; Aditya Arora; Syed Waqas Zamir; Javier Vazquez; Konstantinos G. Derpanis; Michael S. Brown; Hao Li; Zhihao Zhao; Jinshan Pan; Jiangxin Dong; Jinhui Tang; Bo Yang; Jingxiang Chen; Chenghua Li; Xi Zhang; Zhao Zhang; Jiahuan Ren; Zhicheng Ji; Kang Miao; Suiyi Zhao; Huan Zheng; YanYan Wei; Kangliang Liu; Xiangcheng Du; Sijie Liu; Yingbin Zheng; Xingjiao Wu; Cheng Jin; Rajeev Irny; Sriharsha Koundinya; Vighnesh Kamath; Gaurav Khandelwal; Sunder Ali Khowaja; Jiseok Yoon; Ik Hyun Lee; Shijie Chen; Chengqiang Zhao; Huabin Yang; Zhongjian Zhang; Junjia Huang; Yanru Zhang | ||||
| Title | NTIRE 2023 challenge on image denoising: Methods 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 | 1904-1920 | ||
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| Abstract | This paper reviews the NTIRE 2023 challenge on image denoising (σ = 50) with a focus on the proposed solutions and results. The aim is to obtain a network design capable to produce high-quality results with the best performance measured by PSNR for image denoising. Independent additive white Gaussian noise (AWGN) is assumed and the noise level is 50. The challenge had 225 registered participants, and 16 teams made valid submissions. They gauge the state-of-the-art for image denoising. | ||||
| Address | Vancouver; Canada; June 2023 | ||||
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CVPRW | ||
| Notes | MACO; CIC | Approved | no | ||
| Call Number | Admin @ si @ LZT2023 | Serial | 3910 | ||
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