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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 |
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Title |
Why Is the Winner the Best? |
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Conference Article |
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Year |
2023 |
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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition |
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19955-19966 |
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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. |
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Vancouver; Canada; June 2023 |
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ISE |
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no |
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Admin @ si @ ERW2023 |
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3842 |
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Author |
Eduardo Aguilar; Bogdan Raducanu; Petia Radeva; Joost Van de Weijer |
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Title |
Continual Evidential Deep Learning for Out-of-Distribution Detection |
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Conference Article |
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2023 |
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IEEE/CVF International Conference on Computer Vision (ICCV) Workshops -Visual Continual Learning workshop |
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3444-3454 |
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Uncertainty-based deep learning models have attracted a great deal of interest for their ability to provide accurate and reliable predictions. Evidential deep learning stands out achieving remarkable performance in detecting out-of-distribution (OOD) data with a single deterministic neural network. Motivated by this fact, in this paper we propose the integration of an evidential deep learning method into a continual learning framework in order to perform simultaneously incremental object classification and OOD detection. Moreover, we analyze the ability of vacuity and dissonance to differentiate between in-distribution data belonging to old classes and OOD data. The proposed method, called CEDL, is evaluated on CIFAR-100 considering two settings consisting of 5 and 10 tasks, respectively. From the obtained results, we could appreciate that the proposed method, in addition to provide comparable results in object classification with respect to the baseline, largely outperforms OOD detection compared to several posthoc methods on three evaluation metrics: AUROC, AUPR and FPR95. |
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Paris; France; October 2023 |
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ICCVW |
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LAMP; MILAB |
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no |
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Admin @ si @ ARR2023 |
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3841 |
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Author |
Roberto Morales; Juan Quispe; Eduardo Aguilar |
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Exploring multi-food detection using deep learning-based algorithms |
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Conference Article |
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2023 |
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13th International Conference on Pattern Recognition Systems |
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1-7 |
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People are becoming increasingly concerned about their diet, whether for disease prevention, medical treatment or other purposes. In meals served in restaurants, schools or public canteens, it is not easy to identify the ingredients and/or the nutritional information they contain. Currently, technological solutions based on deep learning models have facilitated the recording and tracking of food consumed based on the recognition of the main dish present in an image. Considering that sometimes there may be multiple foods served on the same plate, food analysis should be treated as a multi-class object detection problem. EfficientDet and YOLOv5 are object detection algorithms that have demonstrated high mAP and real-time performance on general domain data. However, these models have not been evaluated and compared on public food datasets. Unlike general domain objects, foods have more challenging features inherent in their nature that increase the complexity of detection. In this work, we performed a performance evaluation of Efficient-Det and YOLOv5 on three public food datasets: UNIMIB2016, UECFood256 and ChileanFood64. From the results obtained, it can be seen that YOLOv5 provides a significant difference in terms of both mAP and response time compared to EfficientDet in all datasets. Furthermore, YOLOv5 outperforms the state-of-the-art on UECFood256, achieving an improvement of more than 4% in terms of mAP@.50 over the best reported. |
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Guayaquil; Ecuador; July 2023 |
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ICPRS |
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MILAB |
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no |
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Admin @ si @ MQA2023 |
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3843 |
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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 |
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Title |
Text-DIAE: a self-supervised degradation invariant autoencoder for text recognition and document enhancement |
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Conference Article |
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Year |
2023 |
Publication |
Proceedings of the 37th AAAI Conference on Artificial Intelligence |
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37 |
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2 |
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Keywords |
Representation Learning for Vision; CV Applications; CV Language and Vision; ML Unsupervised; Self-Supervised Learning |
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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 |
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DAG |
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no |
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Call Number |
Admin @ si @ SBM2023 |
Serial |
3848 |
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Author |
Mohamed Ali Souibgui; Pau Torras; Jialuo Chen; Alicia Fornes |
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Title |
An Evaluation of Handwritten Text Recognition Methods for Historical Ciphered Manuscripts |
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Conference Article |
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Year |
2023 |
Publication |
7th International Workshop on Historical Document Imaging and Processing |
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7-12 |
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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 |
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no |
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Admin @ si @ STC2023 |
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3849 |
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Author |
Pau Torras; Mohamed Ali Souibgui; Sanket Biswas; Alicia Fornes |
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Title |
Segmentation-Free Alignment of Arbitrary Symbol Transcripts to Images |
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Conference Article |
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Year |
2023 |
Publication |
Document Analysis and Recognition – ICDAR 2023 Workshops |
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14193 |
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83-93 |
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Historical Manuscripts; Symbol Alignment |
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Developing arbitrary symbol recognition systems is a challenging endeavour. Even using content-agnostic architectures such as few-shot models, performance can be substantially improved by providing a number of well-annotated examples into training. In some contexts, transcripts of the symbols are available without any position information associated to them, which enables using line-level recognition architectures. A way of providing this position information to detection-based architectures is finding systems that can align the input symbols with the transcription. In this paper we discuss some symbol alignment techniques that are suitable for low-data scenarios and provide an insight on their perceived strengths and weaknesses. In particular, we study the usage of Connectionist Temporal Classification models, Attention-Based Sequence to Sequence models and we compare them with the results obtained on a few-shot recognition system. |
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ICDAR |
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DAG |
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no |
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Admin @ si @ TSS2023 |
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3850 |
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Author |
JW Xiao; CB Zhang; J. Feng; Xialei Liu; Joost Van de Weijer; MM Cheng |
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Title |
Endpoints Weight Fusion for Class Incremental Semantic Segmentation |
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Conference Article |
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2023 |
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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition |
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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. |
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Vancouver; Canada; June 2023 |
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LAMP |
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no |
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Admin @ si @ XZF2023 |
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3854 |
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Author |
Patricia Suarez; Dario Carpio; Angel Sappa |
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A Deep Learning Based Approach for Synthesizing Realistic Depth Maps |
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Conference Article |
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2023 |
Publication |
22nd International Conference on Image Analysis and Processing |
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14234 |
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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. |
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Udine; Italia; Setember 2023 |
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ICIAP |
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MSIAU |
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no |
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Admin @ si @ SCS2023a |
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3968 |
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Author |
Francesc Net; Marc Folia; Pep Casals; Lluis Gomez |
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Title |
Transductive Learning for Near-Duplicate Image Detection in Scanned Photo Collections |
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Conference Article |
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2023 |
Publication |
17th International Conference on Document Analysis and Recognition |
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14191 |
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3-17 |
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Image deduplication; Near-duplicate images detection; Transductive Learning; Photographic Archives; Deep Learning |
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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. |
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San Jose; CA; USA; August 2023 |
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ICDAR |
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DAG |
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no |
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Admin @ si @ NFC2023 |
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3859 |
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Khanh Nguyen; Ali Furkan Biten; Andres Mafla; Lluis Gomez; Dimosthenis Karatzas |
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Show, Interpret and Tell: Entity-Aware Contextualised Image Captioning in Wikipedia |
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Conference Article |
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2023 |
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Proceedings of the 37th AAAI Conference on Artificial Intelligence |
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37 |
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2 |
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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. |
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Washington; USA; February 2023 |
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AAAI |
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no |
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Admin @ si @ NBM2023 |
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3860 |
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Ayan Banerjee; Sanket Biswas; Josep Llados; Umapada Pal |
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SwinDocSegmenter: An End-to-End Unified Domain Adaptive Transformer for Document Instance Segmentation |
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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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14187 |
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307–325 |
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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 . |
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San Jose; CA; USA; August 2023 |
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DAG |
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no |
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Admin @ si @ BBL2023 |
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3893 |
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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 |
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ICDAR 2023 Competition on Structured Text Extraction from Visually-Rich Document Images |
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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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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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Wenwen Yu; Mingyu Liu; Mingrui Chen; Ning Lu; Yinlong We; Yuliang Liu; Dimosthenis Karatzas; Xiang Bai |
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ICDAR 2023 Competition on Reading the Seal Title |
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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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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. |
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San Jose; CA; USA; August 2023 |
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Weijia Wu; Yuzhong Zhao; Zhuang Li; Jiahong Li; Mike Zheng Shou; Umapada Pal; Dimosthenis Karatzas; Xiang Bai |
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ICDAR 2023 Competition on Video Text Reading for Dense and Small Text |
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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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405–419 |
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Video Text Spotting; Small Text; Text Tracking; Dense Text |
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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. |
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Admin @ si @ WZL2023 |
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3898 |
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Soumya Jahagirdar; Minesh Mathew; Dimosthenis Karatzas; CV Jawahar |
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Watching the News: Towards VideoQA Models that can Read |
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2023 |
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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. |
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Waikoloa; Hawai; USA; January 2023 |
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WACV |
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Admin @ si @ JMK2023 |
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3899 |
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