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| Author | Mohammed Al Rawi; Dimosthenis Karatzas | ||||
| Title | On the Labeling Correctness in Computer Vision Datasets | Type | Conference Article | ||
| Year | 2018 | Publication | Proceedings of the Workshop on Interactive Adaptive Learning, co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases | Abbreviated Journal | |
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| Abstract | Image datasets have heavily been used to build computer vision systems.
These datasets are either manually or automatically labeled, which is a problem as both labeling methods are prone to errors. To investigate this problem, we use a majority voting ensemble that combines the results from several Convolutional Neural Networks (CNNs). Majority voting ensembles not only enhance the overall performance, but can also be used to estimate the confidence level of each sample. We also examined Softmax as another form to estimate posterior probability. We have designed various experiments with a range of different ensembles built from one or different, or temporal/snapshot CNNs, which have been trained multiple times stochastically. We analyzed CIFAR10, CIFAR100, EMNIST, and SVHN datasets and we found quite a few incorrect labels, both in the training and testing sets. We also present detailed confidence analysis on these datasets and we found that the ensemble is better than the Softmax when used estimate the per-sample confidence. This work thus proposes an approach that can be used to scrutinize and verify the labeling of computer vision datasets, which can later be applied to weakly/semi-supervised learning. We propose a measure, based on the Odds-Ratio, to quantify how many of these incorrectly classified labels are actually incorrectly labeled and how many of these are confusing. The proposed methods are easily scalable to larger datasets, like ImageNet, LSUN and SUN, as each CNN instance is trained for 60 epochs; or even faster, by implementing a temporal (snapshot) ensemble. |
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| Area | Expedition | Conference | ECML-PKDDW | ||
Notes  |
DAG; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ RaK2018 | Serial | 3144 | ||
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| Author | Anguelos Nicolaou; Sounak Dey; V.Christlein; A.Maier; Dimosthenis Karatzas | ||||
| Title | Non-deterministic Behavior of Ranking-based Metrics when Evaluating Embeddings | Type | Conference Article | ||
| Year | 2018 | Publication | International Workshop on Reproducible Research in Pattern Recognition | Abbreviated Journal | |
| Volume | 11455 | Issue | Pages | 71-82 | |
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| Abstract | Embedding data into vector spaces is a very popular strategy of pattern recognition methods. When distances between embeddings are quantized, performance metrics become ambiguous. In this paper, we present an analysis of the ambiguity quantized distances introduce and provide bounds on the effect. We demonstrate that it can have a measurable effect in empirical data in state-of-the-art systems. We also approach the phenomenon from a computer security perspective and demonstrate how someone being evaluated by a third party can exploit this ambiguity and greatly outperform a random predictor without even access to the input data. We also suggest a simple solution making the performance metrics, which rely on ranking, totally deterministic and impervious to such exploits. | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Notes |
DAG; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ NDC2018 | Serial | 3178 | ||
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| Author | Ilke Demir; Dena Bazazian; Adriana Romero; Viktoriia Sharmanska; Lyne P. Tchapmi | ||||
| Title | WiCV 2018: The Fourth Women In Computer Vision Workshop | Type | Conference Article | ||
| Year | 2018 | Publication | 4th Women in Computer Vision Workshop | Abbreviated Journal | |
| Volume | Issue | Pages | 1941-19412 | ||
| Keywords | Conferences; Computer vision; Industries; Object recognition; Engineering profession; Collaboration; Machine learning | ||||
| Abstract | We present WiCV 2018 – Women in Computer Vision Workshop to increase the visibility and inclusion of women researchers in computer vision field, organized in conjunction with CVPR 2018. Computer vision and machine learning have made incredible progress over the past years, yet the number of female researchers is still low both in academia and industry. WiCV is organized to raise visibility of female researchers, to increase the collaboration,
and to provide mentorship and give opportunities to femaleidentifying junior researchers in the field. In its fourth year, we are proud to present the changes and improvements over the past years, summary of statistics for presenters and attendees, followed by expectations from future generations. |
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| Address | Salt Lake City; USA; June 2018 | ||||
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| Area | Expedition | Conference | WiCV | ||
| Notes |
DAG; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ DBR2018 | Serial | 3222 | ||
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| Author | Marçal Rusiñol; Lluis Gomez | ||||
| Title | Avances en clasificación de imágenes en los últimos diez años. Perspectivas y limitaciones en el ámbito de archivos fotográficos históricos | Type | Journal | ||
| Year | 2018 | Publication | Revista anual de la Asociación de Archiveros de Castilla y León | Abbreviated Journal | |
| Volume | 21 | Issue | Pages | 161-174 | |
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| Notes |
DAG; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ RuG2018 | Serial | 3239 | ||
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| Author | Raul Gomez; Jaume Gibert; Lluis Gomez; Dimosthenis Karatzas | ||||
| Title | Exploring Hate Speech Detection in Multimodal Publications | Type | Conference Article | ||
| Year | 2020 | Publication | IEEE Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
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| Abstract | In this work we target the problem of hate speech detection in multimodal publications formed by a text and an image. We gather and annotate a large scale dataset from Twitter, MMHS150K, and propose different models that jointly analyze textual and visual information for hate speech detection, comparing them with unimodal detection. We provide quantitative and qualitative results and analyze the challenges of the proposed task. We find that, even though images are useful for the hate speech detection task, current multimodal models cannot outperform models analyzing only text. We discuss why and open the field and the dataset for further research. | ||||
| Address | Aspen; March 2020 | ||||
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| Area | Expedition | Conference | WACV | ||
| Notes |
DAG; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ GGG2020a | Serial | 3280 | ||
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| Author | Y. Patel; Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas; C.V. Jawahar | ||||
| Title | Self-Supervised Visual Representations for Cross-Modal Retrieval | Type | Conference Article | ||
| Year | 2019 | Publication | ACM International Conference on Multimedia Retrieval | Abbreviated Journal | |
| Volume | Issue | Pages | 182–186 | ||
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| Abstract | Cross-modal retrieval methods have been significantly improved in last years with the use of deep neural networks and large-scale annotated datasets such as ImageNet and Places. However, collecting and annotating such datasets requires a tremendous amount of human effort and, besides, their annotations are limited to discrete sets of popular visual classes that may not be representative of the richer semantics found on large-scale cross-modal retrieval datasets. In this paper, we present a self-supervised cross-modal retrieval framework that leverages as training data the correlations between images and text on the entire set of Wikipedia articles. Our method consists in training a CNN to predict: (1) the semantic context of the article in which an image is more probable to appear as an illustration, and (2) the semantic context of its caption. Our experiments demonstrate that the proposed method is not only capable of learning discriminative visual representations for solving vision tasks like classification, but that the learned representations are better for cross-modal retrieval when compared to supervised pre-training of the network on the ImageNet dataset. | ||||
| Address | Otawa; Canada; june 2019 | ||||
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| Area | Expedition | Conference | ICMR | ||
| Notes |
DAG; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ PGR2019 | Serial | 3288 | ||
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| Author | Andres Mafla; Sounak Dey; Ali Furkan Biten; Lluis Gomez; Dimosthenis Karatzas | ||||
| Title | Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual Features | Type | Conference Article | ||
| Year | 2020 | Publication | IEEE Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
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| Abstract | Text contained in an image carries high-level semantics that can be exploited to achieve richer image understanding. In particular, the mere presence of text provides strong guiding content that should be employed to tackle a diversity of computer vision tasks such as image retrieval, fine-grained classification, and visual question answering. In this paper, we address the problem of fine-grained classification and image retrieval by leveraging textual information along with visual cues to comprehend the existing intrinsic relation between the two modalities. The novelty of the proposed model consists of the usage of a PHOC descriptor to construct a bag of textual words along with a Fisher Vector Encoding that captures the morphology of text. This approach provides a stronger multimodal representation for this task and as our experiments demonstrate, it achieves state-of-the-art results on two different tasks, fine-grained classification and image retrieval. | ||||
| Address | Aspen; Colorado; USA; March 2020 | ||||
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| Area | Expedition | Conference | WACV | ||
| Notes |
DAG; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ MDB2020 | Serial | 3334 | ||
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| Author | Chee-Kheng Chng; Yuliang Liu; Yipeng Sun; Chun Chet Ng; Canjie Luo; Zihan Ni; ChuanMing Fang; Shuaitao Zhang; Junyu Han; Errui Ding; Jingtuo Liu; Dimosthenis Karatzas; Chee Seng Chan; Lianwen Jin | ||||
| Title | ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text – RRC-ArT | Type | Conference Article | ||
| Year | 2019 | Publication | 15th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 1571-1576 | ||
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| Abstract | This paper reports the ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text – RRC-ArT that consists of three major challenges: i) scene text detection, ii) scene text recognition, and iii) scene text spotting. A total of 78 submissions from 46 unique teams/individuals were received for this competition. The top performing score of each challenge is as follows: i) T1 – 82.65%, ii) T2.1 – 74.3%, iii) T2.2 – 85.32%, iv) T3.1 – 53.86%, and v) T3.2 – 54.91%. Apart from the results, this paper also details the ArT dataset, tasks description, evaluation metrics and participants' methods. The dataset, the evaluation kit as well as the results are publicly available at the challenge website. | ||||
| Address | Sydney; Australia; September 2019 | ||||
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| Area | Expedition | Conference | ICDAR | ||
| Notes |
DAG; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ CLS2019 | Serial | 3340 | ||
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| Author | Nibal Nayef; Yash Patel; Michal Busta; Pinaki Nath Chowdhury; Dimosthenis Karatzas; Wafa Khlif; Jiri Matas; Umapada Pal; Jean-Christophe Burie; Cheng-lin Liu; Jean-Marc Ogier | ||||
| Title | ICDAR2019 Robust Reading Challenge on Multi-lingual Scene Text Detection and Recognition — RRC-MLT-2019 | Type | Conference Article | ||
| Year | 2019 | Publication | 15th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 1582-1587 | ||
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| Abstract | With the growing cosmopolitan culture of modern cities, the need of robust Multi-Lingual scene Text (MLT) detection and recognition systems has never been more immense. With the goal to systematically benchmark and push the state-of-the-art forward, the proposed competition builds on top of the RRC-MLT-2017 with an additional end-to-end task, an additional language in the real images dataset, a large scale multi-lingual synthetic dataset to assist the training, and a baseline End-to-End recognition method. The real dataset consists of 20,000 images containing text from 10 languages. The challenge has 4 tasks covering various aspects of multi-lingual scene text: (a) text detection, (b) cropped word script classification, (c) joint text detection and script classification and (d) end-to-end detection and recognition. In total, the competition received 60 submissions from the research and industrial communities. This paper presents the dataset, the tasks and the findings of the presented RRC-MLT-2019 challenge. | ||||
| Address | Sydney; Australia; September 2019 | ||||
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| Area | Expedition | Conference | ICDAR | ||
| Notes |
DAG; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ NPB2019 | Serial | 3341 | ||
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| Author | Mohammed Al Rawi; Ernest Valveny | ||||
| Title | Compact and Efficient Multitask Learning in Vision, Language and Speech | Type | Conference Article | ||
| Year | 2019 | Publication | IEEE International Conference on Computer Vision Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | 2933-2942 | ||
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| Abstract | Across-domain multitask learning is a challenging area of computer vision and machine learning due to the intra-similarities among class distributions. Addressing this problem to cope with the human cognition system by considering inter and intra-class categorization and recognition complicates the problem even further. We propose in this work an effective holistic and hierarchical learning by using a text embedding layer on top of a deep learning model. We also propose a novel sensory discriminator approach to resolve the collisions between different tasks and domains. We then train the model concurrently on textual sentiment analysis, speech recognition, image classification, action recognition from video, and handwriting word spotting of two different scripts (Arabic and English). The model we propose successfully learned different tasks across multiple domains. | ||||
| Address | Seul; Korea; October 2019 | ||||
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| Area | Expedition | Conference | ICCVW | ||
| Notes |
DAG; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ RaV2019 | Serial | 3365 | ||
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| Author | Sangeeth Reddy; Minesh Mathew; Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas; C.V. Jawahar | ||||
| Title | RoadText-1K: Text Detection and Recognition Dataset for Driving Videos | Type | Conference Article | ||
| Year | 2020 | Publication | IEEE International Conference on Robotics and Automation | Abbreviated Journal | |
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| Abstract | Perceiving text is crucial to understand semantics of outdoor scenes and hence is a critical requirement to build intelligent systems for driver assistance and self-driving. Most of the existing datasets for text detection and recognition comprise still images and are mostly compiled keeping text in mind. This paper introduces a new ”RoadText-1K” dataset for text in driving videos. The dataset is 20 times larger than the existing largest dataset for text in videos. Our dataset comprises 1000 video clips of driving without any bias towards text and with annotations for text bounding boxes and transcriptions in every frame. State of the art methods for text detection,
recognition and tracking are evaluated on the new dataset and the results signify the challenges in unconstrained driving videos compared to existing datasets. This suggests that RoadText-1K is suited for research and development of reading systems, robust enough to be incorporated into more complex downstream tasks like driver assistance and self-driving. The dataset can be found at http://cvit.iiit.ac.in/research/ projects/cvit-projects/roadtext-1k |
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| Address | Paris; Francia; ??? | ||||
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| Area | Expedition | Conference | ICRA | ||
| Notes |
DAG; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ RMG2020 | Serial | 3400 | ||
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| Author | Raul Gomez; Jaume Gibert; Lluis Gomez; Dimosthenis Karatzas | ||||
| Title | Location Sensitive Image Retrieval and Tagging | Type | Conference Article | ||
| Year | 2020 | Publication | 16th European Conference on Computer Vision | Abbreviated Journal | |
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| Abstract | People from different parts of the globe describe objects and concepts in distinct manners. Visual appearance can thus vary across different geographic locations, which makes location a relevant contextual information when analysing visual data. In this work, we address the task of image retrieval related to a given tag conditioned on a certain location on Earth. We present LocSens, a model that learns to rank triplets of images, tags and coordinates by plausibility, and two training strategies to balance the location influence in the final ranking. LocSens learns to fuse textual and location information of multimodal queries to retrieve related images at different levels of location granularity, and successfully utilizes location information to improve image tagging. | ||||
| Address | Virtual; August 2020 | ||||
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| Area | Expedition | Conference | ECCV | ||
| Notes |
DAG; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ GGG2020b | Serial | 3420 | ||
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| Author | Klara Janousckova; Jiri Matas; Lluis Gomez; Dimosthenis Karatzas | ||||
| Title | Text Recognition – Real World Data and Where to Find Them | Type | Conference Article | ||
| Year | 2020 | Publication | 25th International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 4489-4496 | ||
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| Abstract | We present a method for exploiting weakly annotated images to improve text extraction pipelines. The approach uses an arbitrary end-to-end text recognition system to obtain text region proposals and their, possibly erroneous, transcriptions. The method includes matching of imprecise transcriptions to weak annotations and an edit distance guided neighbourhood search. It produces nearly error-free, localised instances of scene text, which we treat as “pseudo ground truth” (PGT). The method is applied to two weakly-annotated datasets. Training with the extracted PGT consistently improves the accuracy of a state of the art recognition model, by 3.7% on average, across different benchmark datasets (image domains) and 24.5% on one of the weakly annotated datasets 1 1 Acknowledgements. The authors were supported by Czech Technical University student grant SGS20/171/0HK3/3TJ13, the MEYS VVV project CZ.02.1.01/0.010.0J16 019/0000765 Research Center for Informatics, the Spanish Research project TIN2017-89779-P and the CERCA Programme / Generalitat de Catalunya. | ||||
| Address | Virtual; January 2021 | ||||
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| Area | Expedition | Conference | ICPR | ||
| Notes |
DAG; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ JMG2020 | Serial | 3557 | ||
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| Author | Andres Mafla; Ruben Tito; Sounak Dey; Lluis Gomez; Marçal Rusiñol; Ernest Valveny; Dimosthenis Karatzas | ||||
| Title | Real-time Lexicon-free Scene Text Retrieval | Type | Journal Article | ||
| Year | 2021 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 110 | Issue | Pages | 107656 | |
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| Abstract | In this work, we address the task of scene text retrieval: given a text query, the system returns all images containing the queried text. The proposed model uses a single shot CNN architecture that predicts bounding boxes and builds a compact representation of spotted words. In this way, this problem can be modeled as a nearest neighbor search of the textual representation of a query over the outputs of the CNN collected from the totality of an image database. Our experiments demonstrate that the proposed model outperforms previous state-of-the-art, while offering a significant increase in processing speed and unmatched expressiveness with samples never seen at training time. Several experiments to assess the generalization capability of the model are conducted in a multilingual dataset, as well as an application of real-time text spotting in videos. | ||||
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DAG; 600.121; 600.129; 601.338 | Approved | no | ||
| Call Number | Admin @ si @ MTD2021 | Serial | 3493 | ||
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| Author | Mohamed Ali Souibgui; Alicia Fornes; Y.Kessentini; C.Tudor | ||||
| Title | A Few-shot Learning Approach for Historical Encoded Manuscript Recognition | Type | Conference Article | ||
| Year | 2021 | Publication | 25th International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 5413-5420 | ||
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| Abstract | Encoded (or ciphered) manuscripts are a special type of historical documents that contain encrypted text. The automatic recognition of this kind of documents is challenging because: 1) the cipher alphabet changes from one document to another, 2) there is a lack of annotated corpus for training and 3) touching symbols make the symbol segmentation difficult and complex. To overcome these difficulties, we propose a novel method for handwritten ciphers recognition based on few-shot object detection. Our method first detects all symbols of a given alphabet in a line image, and then a decoding step maps the symbol similarity scores to the final sequence of transcribed symbols. By training on synthetic data, we show that the proposed architecture is able to recognize handwritten ciphers with unseen alphabets. In addition, if few labeled pages with the same alphabet are used for fine tuning, our method surpasses existing unsupervised and supervised HTR methods for ciphers recognition. | ||||
| Address | Virtual; January 2021 | ||||
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| Area | Expedition | Conference | ICPR | ||
| Notes |
DAG; 600.121; 600.140 | Approved | no | ||
| Call Number | Admin @ si @ SFK2021 | Serial | 3449 | ||
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