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| Author | Subhajit Maity; Sanket Biswas; Siladittya Manna; Ayan Banerjee; Josep Llados; Saumik Bhattacharya; Umapada Pal | ||||
| Title | SelfDocSeg: A Self-Supervised vision-based Approach towards Document Segmentation | Type | Conference Article | ||
| Year | 2023 | Publication | 17th International Conference on Doccument Analysis and Recognition | Abbreviated Journal | |
| Volume | 14187 | Issue | Pages  |
342–360 | |
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| Abstract | 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 | ||||
| Address | Document Layout Analysis; Document | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ MBM2023 | Serial | 3990 | ||
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| Author | Ayan Banerjee; Sanket Biswas; Josep Llados; Umapada Pal | ||||
| Title | SwinDocSegmenter: An End-to-End Unified Domain Adaptive Transformer for Document Instance Segmentation | Type | Conference Article | ||
| Year | 2023 | Publication | 17th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | 14187 | Issue | Pages |
307–325 | |
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| Abstract | Instance-level segmentation of documents consists in assigning a class-aware and instance-aware label to each pixel of the image. It is a key step in document parsing for their understanding. In this paper, we present a unified transformer encoder-decoder architecture for en-to-end instance segmentation of complex layouts in document images. The method adapts a contrastive training with a mixed query selection for anchor initialization in the decoder. Later on, it performs a dot product between the obtained query embeddings and the pixel embedding map (coming from the encoder) for semantic reasoning. Extensive experimentation on competitive benchmarks like PubLayNet, PRIMA, Historical Japanese (HJ), and TableBank demonstrate that our model with SwinL backbone achieves better segmentation performance than the existing state-of-the-art approaches with the average precision of 93.72, 54.39, 84.65 and 98.04 respectively under one billion parameters. The code is made publicly available at: github.com/ayanban011/SwinDocSegmenter . | ||||
| Address | San Jose; CA; USA; August 2023 | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ BBL2023 | Serial | 3893 | ||
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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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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | VISIGRAPP | ||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ DKR2023 | Serial | 3928 | ||
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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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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CLEF | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ SUS2023a | Serial | 3924 | ||
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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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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CIARP | ||
| Notes | MSIAU | Approved | no | ||
| Call Number | Admin @ si @ IBP2023 | Serial | 4008 | ||
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| Author | Adarsh Tiwari; Sanket Biswas; Josep Llados | ||||
| Title | Can Pre-trained Language Models Help in Understanding Handwritten Symbols? | Type | Conference Article | ||
| Year | 2023 | Publication | 17th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | 14193 | Issue | Pages |
199–211 | |
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| Abstract | The emergence of transformer models like BERT, GPT-2, GPT-3, RoBERTa, T5 for natural language understanding tasks has opened the floodgates towards solving a wide array of machine learning tasks in other modalities like images, audio, music, sketches and so on. These language models are domain-agnostic and as a result could be applied to 1-D sequences of any kind. However, the key challenge lies in bridging the modality gap so that they could generate strong features beneficial for out-of-domain tasks. This work focuses on leveraging the power of such pre-trained language models and discusses the challenges in predicting challenging handwritten symbols and alphabets. | ||||
| Address | San Jose; CA; USA; August 2023 | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ TBL2023 | Serial | 3908 | ||
| Permanent link to this record | |||||
| Author | Patricia Suarez; Dario Carpio; Angel Sappa | ||||
| Title | Boosting Guided Super-Resolution Performance with Synthesized Images | Type | Conference Article | ||
| Year | 2023 | Publication | 17th International Conference on Signal-Image Technology & Internet-Based Systems | Abbreviated Journal | |
| Volume | Issue | Pages |
189-195 | ||
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| Abstract | Guided image processing techniques are widely used for extracting information from a guiding image to aid in the processing of the guided one. These images may be sourced from different modalities, such as 2D and 3D, or different spectral bands, like visible and infrared. In the case of guided cross-spectral super-resolution, features from the two modal images are extracted and efficiently merged to migrate guidance information from one image, usually high-resolution (HR), toward the guided one, usually low-resolution (LR). Different approaches have been recently proposed focusing on the development of architectures for feature extraction and merging in the cross-spectral domains, but none of them care about the different nature of the given images. This paper focuses on the specific problem of guided thermal image super-resolution, where an LR thermal image is enhanced by an HR visible spectrum image. To improve existing guided super-resolution techniques, a novel scheme is proposed that maps the original guiding information to a thermal image-like representation that is similar to the output. Experimental results evaluating five different approaches demonstrate that the best results are achieved when the guiding and guided images share the same domain. | ||||
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| Area | Expedition | Conference | SITIS | ||
| Notes | MSIAU | Approved | no | ||
| Call Number | Admin @ si @ SCS2023c | Serial | 4011 | ||
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| Author | Mickael Cormier; Andreas Specker; Julio C. S. Jacques; Lucas Florin; Jurgen Metzler; Thomas B. Moeslund; Kamal Nasrollahi; Sergio Escalera; Jurgen Beyerer | ||||
| Title | UPAR Challenge: Pedestrian Attribute Recognition and Attribute-based Person Retrieval – Dataset, Design, and Results | Type | Conference Article | ||
| Year | 2023 | Publication | 2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops | Abbreviated Journal | |
| Volume | Issue | Pages |
166-175 | ||
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| Abstract | In civilian video security monitoring, retrieving and tracking a person of interest often rely on witness testimony and their appearance description. Deployed systems rely on a large amount of annotated training data and are expected to show consistent performance in diverse areas and gen-eralize well between diverse settings w.r.t. different view-points, illumination, resolution, occlusions, and poses for indoor and outdoor scenes. However, for such generalization, the system would require a large amount of various an-notated data for training and evaluation. The WACV 2023 Pedestrian Attribute Recognition and Attributed-based Per-son Retrieval Challenge (UPAR-Challenge) aimed to spot-light the problem of domain gaps in a real-world surveil-lance context and highlight the challenges and limitations of existing methods. The UPAR dataset, composed of 40 important binary attributes over 12 attribute categories across four datasets, was extended with data captured from a low-flying UAV from the P-DESTRE dataset. To this aim, 0.6M additional annotations were manually labeled and vali-dated. Each track evaluated the robustness of the competing methods to domain shifts by training on limited data from a specific domain and evaluating using data from unseen do-mains. The challenge attracted 41 registered participants, but only one team managed to outperform the baseline on one track, emphasizing the task's difficulty. This work de-scribes the challenge design, the adopted dataset, obtained results, as well as future directions on the topic. | ||||
| Address | Waikoloa; Hawai; USA; January 2023 | ||||
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| Area | Expedition | Conference | WACVW | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ CSJ2023 | Serial | 3902 | ||
| Permanent link to this record | |||||
| Author | Stepan Simsa; Milan Sulc; Michal Uricar; Yash Patel; Ahmed Hamdi; Matej Kocian; Matyas Skalicky; Jiri Matas; Antoine Doucet; Mickael Coustaty; Dimosthenis Karatzas | ||||
| Title | DocILE Benchmark for Document Information Localization and Extraction | Type | Conference Article | ||
| Year | 2023 | Publication | 17th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | 14188 | Issue | Pages |
147–166 | |
| Keywords | Document AI; Information Extraction; Line Item Recognition; Business Documents; Intelligent Document Processing | ||||
| Abstract | This paper introduces the DocILE benchmark with the largest dataset of business documents for the tasks of Key Information Localization and Extraction and Line Item Recognition. It contains 6.7k annotated business documents, 100k synthetically generated documents, and nearly 1M unlabeled documents for unsupervised pre-training. The dataset has been built with knowledge of domain- and task-specific aspects, resulting in the following key features: (i) annotations in 55 classes, which surpasses the granularity of previously published key information extraction datasets by a large margin; (ii) Line Item Recognition represents a highly practical information extraction task, where key information has to be assigned to items in a table; (iii) documents come from numerous layouts and the test set includes zero- and few-shot cases as well as layouts commonly seen in the training set. The benchmark comes with several baselines, including RoBERTa, LayoutLMv3 and DETR-based Table Transformer; applied to both tasks of the DocILE benchmark, with results shared in this paper, offering a quick starting point for future work. The dataset, baselines and supplementary material are available at https://github.com/rossumai/docile. | ||||
| Address | San Jose; CA; USA; August 2023 | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ SSU2023 | Serial | 3903 | ||
| Permanent link to this record | |||||
| Author | George Tom; Minesh Mathew; Sergi Garcia Bordils; Dimosthenis Karatzas; CV Jawahar | ||||
| Title | Reading Between the Lanes: Text VideoQA on the Road | Type | Conference Article | ||
| Year | 2023 | Publication | 17th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | 14192 | Issue | Pages |
137–154 | |
| Keywords | VideoQA; scene text; driving videos | ||||
| Abstract | 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. | ||||
| Address | San Jose; CA; USA; August 2023 | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ TMG2023 | Serial | 3906 | ||
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| Author | Asma Bensalah; Antonio Parziale; Giuseppe De Gregorio; Angelo Marcelli; Alicia Fornes; Josep Llados | ||||
| Title | I Can’t Believe It’s Not Better: In-air Movement for Alzheimer Handwriting Synthetic Generation | Type | Conference Article | ||
| Year | 2023 | Publication | 21st International Graphonomics Conference | Abbreviated Journal | |
| Volume | Issue | Pages |
136–148 | ||
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| Abstract | During recent years, there here has been a boom in terms of deep learning use for handwriting analysis and recognition. One main application for handwriting analysis is early detection and diagnosis in the health field. Unfortunately, most real case problems still suffer a scarcity of data, which makes difficult the use of deep learning-based models. To alleviate this problem, some works resort to synthetic data generation. Lately, more works are directed towards guided data synthetic generation, a generation that uses the domain and data knowledge to generate realistic data that can be useful to train deep learning models. In this work, we combine the domain knowledge about the Alzheimer’s disease for handwriting and use it for a more guided data generation. Concretely, we have explored the use of in-air movements for synthetic data generation. | ||||
| Address | Evora; Portugal; October 2023 | ||||
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| Area | Expedition | Conference | IGS | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ BPG2023 | Serial | 3838 | ||
| Permanent link to this record | |||||
| 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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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | VISIGRAPP | ||
| Notes | MSIAU | Approved | no | ||
| Call Number | Admin @ si @ SuS2023b | Serial | 3927 | ||
| Permanent link to this record | |||||
| Author | Sergi Garcia Bordils; Dimosthenis Karatzas; Marçal Rusiñol | ||||
| Title | Accelerating Transformer-Based Scene Text Detection and Recognition via Token Pruning | Type | Conference Article | ||
| Year | 2023 | Publication | 17th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | 14192 | Issue | Pages |
106-121 | |
| Keywords | Scene Text Detection; Scene Text Recognition; Transformer Acceleration | ||||
| Abstract | Scene text detection and recognition is a crucial task in computer vision with numerous real-world applications. Transformer-based approaches are behind all current state-of-the-art models and have achieved excellent performance. However, the computational requirements of the transformer architecture makes training these methods slow and resource heavy. In this paper, we introduce a new token pruning strategy that significantly decreases training and inference times without sacrificing performance, striking a balance between accuracy and speed. We have applied this pruning technique to our own end-to-end transformer-based scene text understanding architecture. Our method uses a separate detection branch to guide the pruning of uninformative image features, which significantly reduces the number of tokens at the input of the transformer. Experimental results show how our network is able to obtain competitive results on multiple public benchmarks while running at significantly higher speeds. | ||||
| Address | San Jose; CA; USA; August 2023 | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ GKR2023a | Serial | 3907 | ||
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| Author | Artur Xarles; Sergio Escalera; Thomas B. Moeslund; Albert Clapes | ||||
| Title | ASTRA: An Action Spotting TRAnsformer for Soccer Videos | Type | Conference Article | ||
| Year | 2023 | Publication | Proceedings of the 6th International Workshop on Multimedia Content Analysis in Sports | Abbreviated Journal | |
| Volume | Issue | Pages |
93–102 | ||
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| Abstract | In this paper, we introduce ASTRA, a Transformer-based model designed for the task of Action Spotting in soccer matches. ASTRA addresses several challenges inherent in the task and dataset, including the requirement for precise action localization, the presence of a long-tail data distribution, non-visibility in certain actions, and inherent label noise. To do so, ASTRA incorporates (a) a Transformer encoder-decoder architecture to achieve the desired output temporal resolution and to produce precise predictions, (b) a balanced mixup strategy to handle the long-tail distribution of the data, (c) an uncertainty-aware displacement head to capture the label variability, and (d) input audio signal to enhance detection of non-visible actions. Results demonstrate the effectiveness of ASTRA, achieving a tight Average-mAP of 66.82 on the test set. Moreover, in the SoccerNet 2023 Action Spotting challenge, we secure the 3rd position with an Average-mAP of 70.21 on the challenge set. | ||||
| Address | Otawa; Canada; October 2023 | ||||
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| Area | Expedition | Conference | MMSports | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ XEM2023 | Serial | 3970 | ||
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| Author | Patricia Suarez; Henry Velesaca; Dario Carpio; Angel Sappa | ||||
| Title | Corn kernel classification from few training samples | Type | Journal | ||
| Year | 2023 | Publication | Artificial Intelligence in Agriculture | Abbreviated Journal | |
| Volume | 9 | Issue | Pages |
89-99 | |
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| Abstract | This article presents an efficient approach to classify a set of corn kernels in contact, which may contain good, or defective kernels along with impurities. The proposed approach consists of two stages, the first one is a next-generation segmentation network, trained by using a set of synthesized images that is applied to divide the given image into a set of individual instances. An ad-hoc lightweight CNN architecture is then proposed to classify each instance into one of three categories (ie good, defective, and impurities). The segmentation network is trained using a strategy that avoids the time-consuming and human-error-prone task of manual data annotation. Regarding the classification stage, the proposed ad-hoc network is designed with only a few sets of layers to result in a lightweight architecture capable of being used in integrated solutions. Experimental results and comparisons with previous approaches showing both the improvement in accuracy and the reduction in time are provided. Finally, the segmentation and classification approach proposed can be easily adapted for use with other cereal types. | ||||
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| Notes | MSIAU | Approved | no | ||
| Call Number | Admin @ si @ SVC2023 | Serial | 3892 | ||
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