| Records |
| Author |
Mohamed Ali Souibgui; Pau Torras; Jialuo Chen; Alicia Fornes |
| Title |
An Evaluation of Handwritten Text Recognition Methods for Historical Ciphered Manuscripts |
Type  |
Conference Article |
| Year |
2023 |
Publication |
7th International Workshop on Historical Document Imaging and Processing |
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| Volume |
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Issue |
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Pages |
7-12 |
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| Abstract |
This paper investigates the effectiveness of different deep learning HTR families, including LSTM, Seq2Seq, and transformer-based approaches with self-supervised pretraining, in recognizing ciphered manuscripts from different historical periods and cultures. The goal is to identify the most suitable method or training techniques for recognizing ciphered manuscripts and to provide insights into the challenges and opportunities in this field of research. We evaluate the performance of these models on several datasets of ciphered manuscripts and discuss their results. This study contributes to the development of more accurate and efficient methods for recognizing historical manuscripts for the preservation and dissemination of our cultural heritage. |
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HIP |
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DAG |
Approved |
no |
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Admin @ si @ STC2023 |
Serial |
3849 |
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| Author |
Pau Torras; Mohamed Ali Souibgui; Sanket Biswas; Alicia Fornes |
| Title |
Segmentation-Free Alignment of Arbitrary Symbol Transcripts to Images |
Type |
Conference Article |
| Year |
2023 |
Publication |
Document Analysis and Recognition – ICDAR 2023 Workshops |
Abbreviated Journal |
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| Volume |
14193 |
Issue |
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Pages |
83-93 |
| Keywords |
Historical Manuscripts; Symbol Alignment |
| Abstract |
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 |
Serial |
3850 |
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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 |
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Issue |
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Pages |
7204-7213 |
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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 |
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LAMP |
Approved |
no |
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Admin @ si @ XZF2023 |
Serial |
3854 |
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| Author |
Patricia Suarez; Dario Carpio; Angel Sappa |
| Title |
A Deep Learning Based Approach for Synthesizing Realistic Depth Maps |
Type |
Conference Article |
| Year |
2023 |
Publication |
22nd International Conference on Image Analysis and Processing |
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| Volume |
14234 |
Issue |
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Pages |
369–380 |
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This paper presents a novel cycle generative adversarial network (CycleGAN) architecture for synthesizing high-quality depth maps from a given monocular image. The proposed architecture uses multiple loss functions, including cycle consistency, contrastive, identity, and least square losses, to enable the generation of realistic and high-fidelity depth maps. The proposed approach addresses this challenge by synthesizing depth maps from RGB images without requiring paired training data. Comparisons with several state-of-the-art approaches are provided showing the proposed approach overcome other approaches both in terms of quantitative metrics and visual quality. |
| Address |
Udine; Italia; Setember 2023 |
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LNCS |
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ICIAP |
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MSIAU |
Approved |
no |
| Call Number |
Admin @ si @ SCS2023a |
Serial |
3968 |
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| Author |
Francesc Net; Marc Folia; Pep Casals; Lluis Gomez |
| Title |
Transductive Learning for Near-Duplicate Image Detection in Scanned Photo Collections |
Type |
Conference Article |
| Year |
2023 |
Publication |
17th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
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| Volume |
14191 |
Issue |
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Pages |
3-17 |
| Keywords |
Image deduplication; Near-duplicate images detection; Transductive Learning; Photographic Archives; Deep Learning |
| Abstract |
This paper presents a comparative study of near-duplicate image detection techniques in a real-world use case scenario, where a document management company is commissioned to manually annotate a collection of scanned photographs. Detecting duplicate and near-duplicate photographs can reduce the time spent on manual annotation by archivists. This real use case differs from laboratory settings as the deployment dataset is available in advance, allowing the use of transductive learning. We propose a transductive learning approach that leverages state-of-the-art deep learning architectures such as convolutional neural networks (CNNs) and Vision Transformers (ViTs). Our approach involves pre-training a deep neural network on a large dataset and then fine-tuning the network on the unlabeled target collection with self-supervised learning. The results show that the proposed approach outperforms the baseline methods in the task of near-duplicate image detection in the UKBench and an in-house private dataset. |
| Address |
San Jose; CA; USA; August 2023 |
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ICDAR |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ NFC2023 |
Serial |
3859 |
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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 |
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| Volume |
37 |
Issue |
2 |
Pages |
1940-1948 |
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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 |
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DAG |
Approved |
no |
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Admin @ si @ NBM2023 |
Serial |
3860 |
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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 |
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| Volume |
14187 |
Issue |
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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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ICDAR |
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DAG |
Approved |
no |
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Admin @ si @ BBL2023 |
Serial |
3893 |
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| Author |
Wenwen Yu; Chengquan Zhang; Haoyu Cao; Wei Hua; Bohan Li; Huang Chen; Mingyu Liu; Mingrui Chen; Jianfeng Kuang; Mengjun Cheng; Yuning Du; Shikun Feng; Xiaoguang Hu; Pengyuan Lyu; Kun Yao; Yuechen Yu; Yuliang Liu; Wanxiang Che; Errui Ding; Cheng-Lin Liu; Jiebo Luo; Shuicheng Yan; Min Zhang; Dimosthenis Karatzas; Xing Sun; Jingdong Wang; Xiang Bai |
| Title |
ICDAR 2023 Competition on Structured Text Extraction from Visually-Rich Document Images |
Type |
Conference Article |
| Year |
2023 |
Publication |
17th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
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| Volume |
14188 |
Issue |
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Pages |
536–552 |
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Structured text extraction is one of the most valuable and challenging application directions in the field of Document AI. However, the scenarios of past benchmarks are limited, and the corresponding evaluation protocols usually focus on the submodules of the structured text extraction scheme. In order to eliminate these problems, we organized the ICDAR 2023 competition on Structured text extraction from Visually-Rich Document images (SVRD). We set up two tracks for SVRD including Track 1: HUST-CELL and Track 2: Baidu-FEST, where HUST-CELL aims to evaluate the end-to-end performance of Complex Entity Linking and Labeling, and Baidu-FEST focuses on evaluating the performance and generalization of Zero-shot/Few-shot Structured Text extraction from an end-to-end perspective. Compared to the current document benchmarks, our two tracks of competition benchmark enriches the scenarios greatly and contains more than 50 types of visually-rich document images (mainly from the actual enterprise applications). The competition opened on 30th December, 2022 and closed on 24th March, 2023. There are 35 participants and 91 valid submissions received for Track 1, and 15 participants and 26 valid submissions received for Track 2. In this report we will presents the motivation, competition datasets, task definition, evaluation protocol, and submission summaries. According to the performance of the submissions, we believe there is still a large gap on the expected information extraction performance for complex and zero-shot scenarios. It is hoped that this competition will attract many researchers in the field of CV and NLP, and bring some new thoughts to the field of Document AI. |
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San Jose; CA; USA; August 2023 |
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LNCS |
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ICDAR |
| Notes |
DAG |
Approved |
no |
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Admin @ si @ YZC2023 |
Serial |
3896 |
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| Author |
Wenwen Yu; Mingyu Liu; Mingrui Chen; Ning Lu; Yinlong We; Yuliang Liu; Dimosthenis Karatzas; Xiang Bai |
| Title |
ICDAR 2023 Competition on Reading the Seal Title |
Type |
Conference Article |
| Year |
2023 |
Publication |
17th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
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| Volume |
14188 |
Issue |
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Pages |
522–535 |
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Reading seal title text is a challenging task due to the variable shapes of seals, curved text, background noise, and overlapped text. However, this important element is commonly found in official and financial scenarios, and has not received the attention it deserves in the field of OCR technology. To promote research in this area, we organized ICDAR 2023 competition on reading the seal title (ReST), which included two tasks: seal title text detection (Task 1) and end-to-end seal title recognition (Task 2). We constructed a dataset of 10,000 real seal data, covering the most common classes of seals, and labeled all seal title texts with text polygons and text contents. The competition opened on 30th December, 2022 and closed on 20th March, 2023. The competition attracted 53 participants and received 135 submissions from academia and industry, including 28 participants and 72 submissions for Task 1, and 25 participants and 63 submissions for Task 2, which demonstrated significant interest in this challenging task. In this report, we present an overview of the competition, including the organization, challenges, and results. We describe the dataset and tasks, and summarize the submissions and evaluation results. The results show that significant progress has been made in the field of seal title text reading, and we hope that this competition will inspire further research and development in this important area of OCR technology. |
| Address |
San Jose; CA; USA; August 2023 |
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LNCS |
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ICDAR |
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DAG |
Approved |
no |
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Admin @ si @ YLC2023 |
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3897 |
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| 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 |
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14188 |
Issue |
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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 |
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ICDAR |
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DAG |
Approved |
no |
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Admin @ si @ WZL2023 |
Serial |
3898 |
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| Author |
Soumya Jahagirdar; Minesh Mathew; Dimosthenis Karatzas; CV Jawahar |
| Title |
Watching the News: Towards VideoQA Models that can Read |
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Conference Article |
| Year |
2023 |
Publication |
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer |
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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 |
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WACV |
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DAG |
Approved |
no |
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Admin @ si @ JMK2023 |
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3899 |
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| Author |
Marcos V Conde; Florin Vasluianu; Javier Vazquez; Radu Timofte |
| Title |
Perceptual image enhancement for smartphone real-time applications |
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Conference Article |
| Year |
2023 |
Publication |
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 |
Approved |
no |
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Admin @ si @ CVV2023 |
Serial |
3900 |
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| Author |
Dipam Goswami; J Schuster; Joost Van de Weijer; Didier Stricker |
| Title |
Attribution-aware Weight Transfer: A Warm-Start Initialization for Class-Incremental Semantic Segmentation |
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Conference Article |
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2023 |
Publication |
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision |
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3195-3204 |
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Attribution-aware Weight Transfer: A Warm-Start Initialization for Class-Incremental Semantic Segmentation. D Goswami, R Schuster, J van de Weijer, D Stricker. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023, pp. 3195-3204 |
| Address |
Waikoloa; Hawai; USA; January 2023 |
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WACV |
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LAMP |
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no |
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Admin @ si @ GSW2023 |
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3901 |
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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 |
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Conference Article |
| Year |
2023 |
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2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops |
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166-175 |
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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. |
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Waikoloa; Hawai; USA; January 2023 |
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Admin @ si @ CSJ2023 |
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3902 |
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Stepan Simsa; Milan Sulc; Michal Uricar; Yash Patel; Ahmed Hamdi; Matej Kocian; Matyas Skalicky; Jiri Matas; Antoine Doucet; Mickael Coustaty; Dimosthenis Karatzas |
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DocILE Benchmark for Document Information Localization and Extraction |
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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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14188 |
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147–166 |
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Document AI; Information Extraction; Line Item Recognition; Business Documents; Intelligent Document Processing |
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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. |
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San Jose; CA; USA; August 2023 |
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Admin @ si @ SSU2023 |
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3903 |
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