| Records |
Author  |
Sonia Baeza; Debora Gil; Carles Sanchez; Guillermo Torres; Ignasi Garcia Olive; Ignasi Guasch; Samuel Garcia Reina; Felipe Andreo; Jose Luis Mate; Jose Luis Vercher; Antonio Rosell |
| Title |
Biopsia virtual radiomica para el diagnóstico histológico de nódulos pulmonares – Resultados intermedios del proyecto Radiolung |
Type |
Conference Article |
| Year |
2023 |
Publication |
SEPAR |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
|
| Keywords |
|
| Abstract |
Pòster |
| Address |
Granada; Spain; June 2023 |
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
SEPAR |
| Notes |
IAM |
Approved |
no |
| Call Number |
Admin @ si @ BGS2023 |
Serial |
3951 |
| Permanent link to this record |
| |
|
| |
| 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 |
|
| Volume |
|
Issue |
|
Pages |
|
| Keywords |
|
| 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. |
| Address |
|
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ BBM2023 |
Serial |
3995 |
| Permanent link to this record |
| |
|
| |
| Author |
Souhail Bakkali; Zuheng Ming; Mickael Coustaty; Marçal Rusiñol; Oriol Ramos Terrades |
| Title |
VLCDoC: Vision-Language Contrastive Pre-Training Model for Cross-Modal Document Classification |
Type |
Journal Article |
| Year |
2023 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
139 |
Issue |
|
Pages |
109419 |
| Keywords |
|
| Abstract |
Multimodal learning from document data has achieved great success lately as it allows to pre-train semantically meaningful features as a prior into a learnable downstream approach. In this paper, we approach the document classification problem by learning cross-modal representations through language and vision cues, considering intra- and inter-modality relationships. Instead of merging features from different modalities into a common representation space, the proposed method exploits high-level interactions and learns relevant semantic information from effective attention flows within and across modalities. The proposed learning objective is devised between intra- and inter-modality alignment tasks, where the similarity distribution per task is computed by contracting positive sample pairs while simultaneously contrasting negative ones in the common feature representation space}. Extensive experiments on public document classification datasets demonstrate the effectiveness and the generalization capacity of our model on both low-scale and large-scale datasets. |
| Address |
|
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
ISSN 0031-3203 |
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
DAG; 600.140; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ BMC2023 |
Serial |
3826 |
| Permanent link to this record |
| |
|
| |
| Author |
Soumya Jahagirdar; Minesh Mathew; Dimosthenis Karatzas; CV Jawahar |
| Title |
Watching the News: Towards VideoQA Models that can Read |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
|
| Keywords |
|
| Abstract |
Video Question Answering methods focus on commonsense reasoning and visual cognition of objects or persons and their interactions over time. Current VideoQA approaches ignore the textual information present in the video. Instead, we argue that textual information is complementary to the action and provides essential contextualisation cues to the reasoning process. To this end, we propose a novel VideoQA task that requires reading and understanding the text in the video. To explore this direction, we focus on news videos and require QA systems to comprehend and answer questions about the topics presented by combining visual and textual cues in the video. We introduce the ``NewsVideoQA'' dataset that comprises more than 8,600 QA pairs on 3,000+ news videos obtained from diverse news channels from around the world. We demonstrate the limitations of current Scene Text VQA and VideoQA methods and propose ways to incorporate scene text information into VideoQA methods. |
| Address |
Waikoloa; Hawai; USA; January 2023 |
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
WACV |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ JMK2023 |
Serial |
3899 |
| Permanent link to this record |
| |
|
| |
| Author |
Soumya Jahagirdar; Minesh Mathew; Dimosthenis Karatzas; CV Jawahar |
| Title |
Understanding Video Scenes Through Text: Insights from Text-Based Video Question Answering |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
|
| Keywords |
|
| Abstract |
Researchers have extensively studied the field of vision and language, discovering that both visual and textual content is crucial for understanding scenes effectively. Particularly, comprehending text in videos holds great significance, requiring both scene text understanding and temporal reasoning. This paper focuses on exploring two recently introduced datasets, NewsVideoQA and M4-ViteVQA, which aim to address video question answering based on textual content. The NewsVideoQA dataset contains question-answer pairs related to the text in news videos, while M4- ViteVQA comprises question-answer pairs from diverse categories like vlogging, traveling, and shopping. We provide an analysis of the formulation of these datasets on various levels, exploring the degree of visual understanding and multi-frame comprehension required for answering the questions. Additionally, the study includes experimentation with BERT-QA, a text-only model, which demonstrates comparable performance to the original methods on both datasets, indicating the shortcomings in the formulation of these datasets. Furthermore, we also look into the domain adaptation aspect by examining the effectiveness of training on M4-ViteVQA and evaluating on NewsVideoQA and vice-versa, thereby shedding light on the challenges and potential benefits of out-of-domain training. |
| Address |
Paris; France; October 2023 |
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
ICCVW |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ JMK2023 |
Serial |
3946 |
| Permanent link to this record |
| |
|
| |
| Author |
Spencer Low; Oliver Nina; Angel Sappa; Erik Blasch; Nathan Inkawhich |
| Title |
Multi-Modal Aerial View Image Challenge: Translation From Synthetic Aperture Radar to Electro-Optical Domain 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 |
515-523 |
| Keywords |
|
| Abstract |
This paper unveils the discoveries and outcomes of the inaugural iteration of the Multi-modal Aerial View Image Challenge (MAVIC) aimed at image translation. The primary objective of this competition is to stimulate research efforts towards the development of models capable of translating co-aligned images between multiple modalities. To accomplish the task of image translation, the competition utilizes images obtained from both synthetic aperture radar (SAR) and electro-optical (EO) sources. Specifically, the challenge centers on the translation from the SAR modality to the EO modality, an area of research that has garnered attention. The inaugural challenge demonstrates the feasibility of the task. The dataset utilized in this challenge is derived from the UNIfied COincident Optical and Radar for recognitioN (UNICORN) dataset. We introduce an new version of the UNICORN dataset that is focused on enabling the sensor translation task. Performance evaluation is conducted using a combination of measures to ensure high fidelity and high accuracy translations. |
| Address |
Vancouver; Canada; June 2023 |
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
CVPRW |
| Notes |
MSIAU |
Approved |
no |
| Call Number |
Admin @ si @ LNS2023a |
Serial |
3913 |
| Permanent link to this record |
| |
|
| |
| 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 |
| Keywords |
|
| 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 |
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
CVPRW |
| Notes |
MSIAU |
Approved |
no |
| Call Number |
Admin @ si @ LNS2023b |
Serial |
3915 |
| Permanent link to this record |
| |
|
| |
| 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 |
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| 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 |
| 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 |
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| 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 |
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 |
| Keywords |
|
| 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 |
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
ICDAR |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ MBM2023 |
Serial |
3990 |
| Permanent link to this record |
| |
|
| |
| Author |
Swathikiran Sudhakaran; Sergio Escalera; Oswald Lanz |
| Title |
Gate-Shift-Fuse for Video Action Recognition |
Type |
Journal Article |
| Year |
2023 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
| Volume |
45 |
Issue |
9 |
Pages |
10913-10928 |
| Keywords |
Action Recognition; Video Classification; Spatial Gating; Channel Fusion |
| Abstract |
Convolutional Neural Networks are the de facto models for image recognition. However 3D CNNs, the straight forward extension of 2D CNNs for video recognition, have not achieved the same success on standard action recognition benchmarks. One of the main reasons for this reduced performance of 3D CNNs is the increased computational complexity requiring large scale annotated datasets to train them in scale. 3D kernel factorization approaches have been proposed to reduce the complexity of 3D CNNs. Existing kernel factorization approaches follow hand-designed and hard-wired techniques. In this paper we propose Gate-Shift-Fuse (GSF), a novel spatio-temporal feature extraction module which controls interactions in spatio-temporal decomposition and learns to adaptively route features through time and combine them in a data dependent manner. GSF leverages grouped spatial gating to decompose input tensor and channel weighting to fuse the decomposed tensors. GSF can be inserted into existing 2D CNNs to convert them into an efficient and high performing spatio-temporal feature extractor, with negligible parameter and compute overhead. We perform an extensive analysis of GSF using two popular 2D CNN families and achieve state-of-the-art or competitive performance on five standard action recognition benchmarks. |
| Address |
1 Sept. 2023 |
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
HUPBA; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ SEL2023 |
Serial |
3814 |
| Permanent link to this record |
| |
|
| |
| Author |
Valeriya Khan; Sebastian Cygert; Bartlomiej Twardowski; Tomasz Trzcinski |
| Title |
Looking Through the Past: Better Knowledge Retention for Generative Replay in Continual Learning |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
3496-3500 |
| Keywords |
|
| Abstract |
In this work, we improve the generative replay in a continual learning setting. We notice that in VAE-based generative replay, the generated features are quite far from the original ones when mapped to the latent space. Therefore, we propose modifications that allow the model to learn and generate complex data. More specifically, we incorporate the distillation in latent space between the current and previous models to reduce feature drift. Additionally, a latent matching for the reconstruction and original data is proposed to improve generated features alignment. Further, based on the observation that the reconstructions are better for preserving knowledge, we add the cycling of generations through the previously trained model to make them closer to the original data. Our method outperforms other generative replay methods in various scenarios. |
| Address |
|
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
ICCVW |
| Notes |
LAMP |
Approved |
no |
| Call Number |
Admin @ si @ KCT2023 |
Serial |
3942 |
| Permanent link to this record |
| |
|
| |
| Author |
Weijia Wu; Yuzhong Zhao; Zhuang Li; Jiahong Li; Mike Zheng Shou; Umapada Pal; Dimosthenis Karatzas; Xiang Bai |
| Title |
ICDAR 2023 Competition on Video Text Reading for Dense and Small Text |
Type |
Conference Article |
| Year |
2023 |
Publication |
17th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
|
| Volume |
14188 |
Issue |
|
Pages |
405–419 |
| Keywords |
Video Text Spotting; Small Text; Text Tracking; Dense Text |
| Abstract |
Recently, video text detection, tracking and recognition in natural scenes are becoming very popular in the computer vision community. However, most existing algorithms and benchmarks focus on common text cases (e.g., normal size, density) and single scenario, while ignore extreme video texts challenges, i.e., dense and small text in various scenarios. In this competition report, we establish a video text reading benchmark, named DSText, which focuses on dense and small text reading challenge in the video with various scenarios. Compared with the previous datasets, the proposed dataset mainly include three new challenges: 1) Dense video texts, new challenge for video text spotter. 2) High-proportioned small texts. 3) Various new scenarios, e.g., ‘Game’, ‘Sports’, etc. The proposed DSText includes 100 video clips from 12 open scenarios, supporting two tasks (i.e., video text tracking (Task 1) and end-to-end video text spotting (Task2)). During the competition period (opened on 15th February, 2023 and closed on 20th March, 2023), a total of 24 teams participated in the three proposed tasks with around 30 valid submissions, respectively. In this article, we describe detailed statistical information of the dataset, tasks, evaluation protocols and the results summaries of the ICDAR 2023 on DSText competition. Moreover, we hope the benchmark will promise the video text research in the community. |
| Address |
San Jose; CA; USA; August 2023 |
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| 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 @ WZL2023 |
Serial |
3898 |
| Permanent link to this record |
| |
|
| |
| Author |
Wenjuan Gong; Yue Zhang; Wei Wang; Peng Cheng; Jordi Gonzalez |
| Title |
Meta-MMFNet: Meta-learning-based Multi-model Fusion Network for Micro-expression Recognition |
Type |
Journal Article |
| Year |
2023 |
Publication |
ACM Transactions on Multimedia Computing, Communications, and Applications |
Abbreviated Journal |
TMCCA |
| Volume |
20 |
Issue |
2 |
Pages |
1–20 |
| Keywords |
|
| Abstract |
Despite its wide applications in criminal investigations and clinical communications with patients suffering from autism, automatic micro-expression recognition remains a challenging problem because of the lack of training data and imbalanced classes problems. In this study, we proposed a meta-learning-based multi-model fusion network (Meta-MMFNet) to solve the existing problems. The proposed method is based on the metric-based meta-learning pipeline, which is specifically designed for few-shot learning and is suitable for model-level fusion. The frame difference and optical flow features were fused, deep features were extracted from the fused feature, and finally in the meta-learning-based framework, weighted sum model fusion method was applied for micro-expression classification. Meta-MMFNet achieved better results than state-of-the-art methods on four datasets. The code is available at https://github.com/wenjgong/meta-fusion-based-method. |
| Address |
|
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
ISE |
Approved |
no |
| Call Number |
Admin @ si @ GZW2023 |
Serial |
3862 |
| Permanent link to this record |
| |
|
| |
| Author |
Wenwen Fu; Zhihong An; Wendong Huang; Haoran Sun; Wenjuan Gong; Jordi Gonzalez |
| Title |
A Spatio-Temporal Spotting Network with Sliding Windows for Micro-Expression Detection |
Type |
Journal Article |
| Year |
2023 |
Publication |
Electronics |
Abbreviated Journal |
ELEC |
| Volume |
12 |
Issue |
18 |
Pages |
3947 |
| Keywords |
micro-expression spotting; sliding window; key frame extraction |
| Abstract |
Micro-expressions reveal underlying emotions and are widely applied in political psychology, lie detection, law enforcement and medical care. Micro-expression spotting aims to detect the temporal locations of facial expressions from video sequences and is a crucial task in micro-expression recognition. In this study, the problem of micro-expression spotting is formulated as micro-expression classification per frame. We propose an effective spotting model with sliding windows called the spatio-temporal spotting network. The method involves a sliding window detection mechanism, combines the spatial features from the local key frames and the global temporal features and performs micro-expression spotting. The experiments are conducted on the CAS(ME)2 database and the SAMM Long Videos database, and the results demonstrate that the proposed method outperforms the state-of-the-art method by 30.58% for the CAS(ME)2 and 23.98% for the SAMM Long Videos according to overall F-scores. |
| Address |
|
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
ISE |
Approved |
no |
| Call Number |
Admin @ si @ FAH2023 |
Serial |
3864 |
| Permanent link to this record |
