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Author Lluis Gomez; Ali Furkan Biten; Ruben Tito; Andres Mafla; Marçal Rusiñol; Ernest Valveny; Dimosthenis Karatzas
Title Multimodal grid features and cell pointers for scene text visual question answering Type Journal Article
Year 2021 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume (down) 150 Issue Pages 242-249
Keywords
Abstract This paper presents a new model for the task of scene text visual question answering. In this task questions about a given image can only be answered by reading and understanding scene text. Current state of the art models for this task make use of a dual attention mechanism in which one attention module attends to visual features while the other attends to textual features. A possible issue with this is that it makes difficult for the model to reason jointly about both modalities. To fix this problem we propose a new model that is based on an single attention mechanism that attends to multi-modal features conditioned to the question. The output weights of this attention module over a grid of multi-modal spatial features are interpreted as the probability that a certain spatial location of the image contains the answer text to the given question. Our experiments demonstrate competitive performance in two standard datasets with a model that is faster than previous methods at inference time. Furthermore, we also provide a novel analysis of the ST-VQA dataset based on a human performance study. Supplementary material, code, and data is made available through this link.
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Notes DAG; 600.084; 600.121 Approved no
Call Number Admin @ si @ GBT2021 Serial 3620
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Author Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva
Title Multi-face tracking by extended bag-of-tracklets in egocentric photo-streams Type Journal Article
Year 2016 Publication Computer Vision and Image Understanding Abbreviated Journal CVIU
Volume (down) 149 Issue Pages 146-156
Keywords
Abstract Wearable cameras offer a hands-free way to record egocentric images of daily experiences, where social events are of special interest. The first step towards detection of social events is to track the appearance of multiple persons involved in them. In this paper, we propose a novel method to find correspondences of multiple faces in low temporal resolution egocentric videos acquired through a wearable camera. This kind of photo-stream imposes additional challenges to the multi-tracking problem with respect to conventional videos. Due to the free motion of the camera and to its low temporal resolution, abrupt changes in the field of view, in illumination condition and in the target location are highly frequent. To overcome such difficulties, we propose a multi-face tracking method that generates a set of tracklets through finding correspondences along the whole sequence for each detected face and takes advantage of the tracklets redundancy to deal with unreliable ones. Similar tracklets are grouped into the so called extended bag-of-tracklets (eBoT), which is aimed to correspond to a specific person. Finally, a prototype tracklet is extracted for each eBoT, where the occurred occlusions are estimated by relying on a new measure of confidence. We validated our approach over an extensive dataset of egocentric photo-streams and compared it to state of the art methods, demonstrating its effectiveness and robustness.
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Notes MILAB; Approved no
Call Number Admin @ si @ ADR2016b Serial 2742
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Author Gerard Canal; Sergio Escalera; Cecilio Angulo
Title A Real-time Human-Robot Interaction system based on gestures for assistive scenarios Type Journal Article
Year 2016 Publication Computer Vision and Image Understanding Abbreviated Journal CVIU
Volume (down) 149 Issue Pages 65-77
Keywords Gesture recognition; Human Robot Interaction; Dynamic Time Warping; Pointing location estimation
Abstract Natural and intuitive human interaction with robotic systems is a key point to develop robots assisting people in an easy and effective way. In this paper, a Human Robot Interaction (HRI) system able to recognize gestures usually employed in human non-verbal communication is introduced, and an in-depth study of its usability is performed. The system deals with dynamic gestures such as waving or nodding which are recognized using a Dynamic Time Warping approach based on gesture specific features computed from depth maps. A static gesture consisting in pointing at an object is also recognized. The pointed location is then estimated in order to detect candidate objects the user may refer to. When the pointed object is unclear for the robot, a disambiguation procedure by means of either a verbal or gestural dialogue is performed. This skill would lead to the robot picking an object in behalf of the user, which could present difficulties to do it by itself. The overall system — which is composed by a NAO and Wifibot robots, a KinectTM v2 sensor and two laptops — is firstly evaluated in a structured lab setup. Then, a broad set of user tests has been completed, which allows to assess correct performance in terms of recognition rates, easiness of use and response times.
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Publisher Elsevier B.V. Place of Publication Editor
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Notes HuPBA;MILAB; Approved no
Call Number Admin @ si @ CEA2016 Serial 2768
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Author Arka Ujjal Dey; Suman Ghosh; Ernest Valveny; Gaurav Harit
Title Beyond Visual Semantics: Exploring the Role of Scene Text in Image Understanding Type Journal Article
Year 2021 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume (down) 149 Issue Pages 164-171
Keywords
Abstract Images with visual and scene text content are ubiquitous in everyday life. However, current image interpretation systems are mostly limited to using only the visual features, neglecting to leverage the scene text content. In this paper, we propose to jointly use scene text and visual channels for robust semantic interpretation of images. We do not only extract and encode visual and scene text cues, but also model their interplay to generate a contextual joint embedding with richer semantics. The contextual embedding thus generated is applied to retrieval and classification tasks on multimedia images, with scene text content, to demonstrate its effectiveness. In the retrieval framework, we augment our learned text-visual semantic representation with scene text cues, to mitigate vocabulary misses that may have occurred during the semantic embedding. To deal with irrelevant or erroneous recognition of scene text, we also apply query-based attention to our text channel. We show how the multi-channel approach, involving visual semantics and scene text, improves upon state of the art.
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Notes DAG; 600.121 Approved no
Call Number Admin @ si @ DGV2021 Serial 3364
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Author Iban Berganzo-Besga; Hector A. Orengo; Felipe Lumbreras; Paloma Aliende; Monica N. Ramsey
Title Automated detection and classification of multi-cell Phytoliths using Deep Learning-Based Algorithms Type Journal Article
Year 2022 Publication Journal of Archaeological Science Abbreviated Journal JArchSci
Volume (down) 148 Issue Pages 105654
Keywords
Abstract This paper presents an algorithm for automated detection and classification of multi-cell phytoliths, one of the major components of many archaeological and paleoenvironmental deposits. This identification, based on phytolith wave pattern, is made using a pretrained VGG19 deep learning model. This approach has been tested in three key phytolith genera for the study of agricultural origins in Near East archaeology: Avena, Hordeum and Triticum. Also, this classification has been validated at species-level using Triticum boeoticum and dicoccoides images. Due to the diversity of microscopes, cameras and chemical treatments that can influence images of phytolith slides, three types of data augmentation techniques have been implemented: rotation of the images at 45-degree angles, random colour and brightness jittering, and random blur/sharpen. The implemented workflow has resulted in an overall accuracy of 93.68% for phytolith genera, improving previous attempts. The algorithm has also demonstrated its potential to automatize the classification of phytoliths species with an overall accuracy of 100%. The open code and platforms employed to develop the algorithm assure the method's accessibility, reproducibility and reusability.
Address December 2022
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Notes MSIAU; MACO; 600.167 Approved no
Call Number Admin @ si @ BOL2022 Serial 3753
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Author Mohammad Momeny; Ali Asghar Neshat; Ahmad Jahanbakhshi; Majid Mahmoudi; Yiannis Ampatzidis; Petia Radeva
Title Grading and fraud detection of saffron via learning-to-augment incorporated Inception-v4 CNN Type Journal Article
Year 2023 Publication Food Control Abbreviated Journal FC
Volume (down) 147 Issue Pages 109554
Keywords
Abstract Saffron is a well-known product in the food industry. It is one of the spices that are sometimes adulterated with the sole motive of gaining more economic profit. Today, machine vision systems are widely used in controlling the quality of food and agricultural products as a new, non-destructive, and inexpensive approach. In this study, a machine vision system based on deep learning was used to detect fraud and saffron quality. A dataset of 1869 images was created and categorized in 6 classes including: dried saffron stigma using a dryer; dried saffron stigma using pressing method; pure stem of saffron; sunflower; saffron stem mixed with food coloring; and corn silk mixed with food coloring. A Learning-to-Augment incorporated Inception-v4 Convolutional Neural Network (LAII-v4 CNN) was developed for grading and fraud detection of saffron in images captured by smartphones. The best policies of data augmentation were selected with the proposed LAII-v4 CNN using images corrupted by Gaussian, speckle, and impulse noise to address overfitting the model. The proposed LAII-v4 CNN compared with regular CNN-based methods and traditional classifiers. Ensemble of Bagged Decision Trees, Ensemble of Boosted Decision Trees, k-Nearest Neighbor, Random Under-sampling Boosted Trees, and Support Vector Machine were used for classification of the features extracted by Histograms of Oriented Gradients and Local Binary Patterns, and selected by the Principal Component Analysis. The results showed that the proposed LAII-v4 CNN with an accuracy of 99.5% has achieved the best performance by employing batch normalization, Dropout, and leaky ReLU.
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Notes MILAB Approved no
Call Number Admin @ si @ MNJ2023 Serial 3882
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Author Katerine Diaz; Francesc J. Ferri; Aura Hernandez-Sabate
Title An overview of incremental feature extraction methods based on linear subspaces Type Journal Article
Year 2018 Publication Knowledge-Based Systems Abbreviated Journal KBS
Volume (down) 145 Issue Pages 219-235
Keywords
Abstract With the massive explosion of machine learning in our day-to-day life, incremental and adaptive learning has become a major topic, crucial to keep up-to-date and improve classification models and their corresponding feature extraction processes. This paper presents a categorized overview of incremental feature extraction based on linear subspace methods which aim at incorporating new information to the already acquired knowledge without accessing previous data. Specifically, this paper focuses on those linear dimensionality reduction methods with orthogonal matrix constraints based on global loss function, due to the extensive use of their batch approaches versus other linear alternatives. Thus, we cover the approaches derived from Principal Components Analysis, Linear Discriminative Analysis and Discriminative Common Vector methods. For each basic method, its incremental approaches are differentiated according to the subspace model and matrix decomposition involved in the updating process. Besides this categorization, several updating strategies are distinguished according to the amount of data used to update and to the fact of considering a static or dynamic number of classes. Moreover, the specific role of the size/dimension ratio in each method is considered. Finally, computational complexity, experimental setup and the accuracy rates according to published results are compiled and analyzed, and an empirical evaluation is done to compare the best approach of each kind.
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0950-7051 ISBN Medium
Area Expedition Conference
Notes ADAS; 600.118 Approved no
Call Number Admin @ si @ DFH2018 Serial 3090
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Author Joakim Bruslund Haurum; Meysam Madadi; Sergio Escalera; Thomas B. Moeslund
Title Multi-scale hybrid vision transformer and Sinkhorn tokenizer for sewer defect classification Type Journal Article
Year 2022 Publication Automation in Construction Abbreviated Journal AC
Volume (down) 144 Issue Pages 104614
Keywords Sewer Defect Classification; Vision Transformers; Sinkhorn-Knopp; Convolutional Neural Networks; Closed-Circuit Television; Sewer Inspection
Abstract A crucial part of image classification consists of capturing non-local spatial semantics of image content. This paper describes the multi-scale hybrid vision transformer (MSHViT), an extension of the classical convolutional neural network (CNN) backbone, for multi-label sewer defect classification. To better model spatial semantics in the images, features are aggregated at different scales non-locally through the use of a lightweight vision transformer, and a smaller set of tokens was produced through a novel Sinkhorn clustering-based tokenizer using distinct cluster centers. The proposed MSHViT and Sinkhorn tokenizer were evaluated on the Sewer-ML multi-label sewer defect classification dataset, showing consistent performance improvements of up to 2.53 percentage points.
Address Dec 2022
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Notes HuPBA Approved no
Call Number Admin @ si @ BME2022c Serial 3780
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Author Ruben Tito; Dimosthenis Karatzas; Ernest Valveny
Title Hierarchical multimodal transformers for Multi-Page DocVQA Type Journal Article
Year 2023 Publication Pattern Recognition Abbreviated Journal PR
Volume (down) 144 Issue Pages 109834
Keywords
Abstract Document Visual Question Answering (DocVQA) refers to the task of answering questions from document images. Existing work on DocVQA only considers single-page documents. However, in real scenarios documents are mostly composed of multiple pages that should be processed altogether. In this work we extend DocVQA to the multi-page scenario. For that, we first create a new dataset, MP-DocVQA, where questions are posed over multi-page documents instead of single pages. Second, we propose a new hierarchical method, Hi-VT5, based on the T5 architecture, that overcomes the limitations of current methods to process long multi-page documents. The proposed method is based on a hierarchical transformer architecture where the encoder summarizes the most relevant information of every page and then, the decoder takes this summarized information to generate the final answer. Through extensive experimentation, we demonstrate that our method is able, in a single stage, to answer the questions and provide the page that contains the relevant information to find the answer, which can be used as a kind of explainability measure.
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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; 600.155; 600.121 Approved no
Call Number Admin @ si @ TKV2023 Serial 3825
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Author Ruben Tito; Dimosthenis Karatzas; Ernest Valveny
Title Hierarchical multimodal transformers for Multipage DocVQA Type Journal Article
Year 2023 Publication Pattern Recognition Abbreviated Journal PR
Volume (down) 144 Issue 109834 Pages
Keywords
Abstract Existing work on DocVQA only considers single-page documents. However, in real applications documents are mostly composed of multiple pages that should be processed altogether. In this work, we propose a new multimodal hierarchical method Hi-VT5, that overcomes the limitations of current methods to process long multipage documents. In contrast to previous hierarchical methods that focus on different semantic granularity (He et al., 2021) or different subtasks (Zhou et al., 2022) used in image classification. Our method is a hierarchical transformer architecture where the encoder learns to summarize the most relevant information of every page and then, the decoder uses this summarized representation to generate the final answer, following a bottom-up approach. Moreover, due to the lack of multipage DocVQA datasets, we also introduce MP-DocVQA, an extension of SP-DocVQA where questions are posed over multipage documents instead of single pages. Through extensive experimentation, we demonstrate that Hi-VT5 is able, in a single stage, to answer the questions and provide the page that contains the answer, which can be used as a kind of explainability measure.
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Publisher Place of Publication Editor
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Notes DAG Approved no
Call Number Admin @ si @ TKV2023 Serial 3836
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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 (down) 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.
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Publisher Place of Publication Editor
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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
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Author Xavier Soria; Angel Sappa; Patricio Humanante; Arash Akbarinia
Title Dense extreme inception network for edge detection Type Journal Article
Year 2023 Publication Pattern Recognition Abbreviated Journal PR
Volume (down) 139 Issue Pages 109461
Keywords
Abstract Edge detection is the basis of many computer vision applications. State of the art predominantly relies on deep learning with two decisive factors: dataset content and network architecture. Most of the publicly available datasets are not curated for edge detection tasks. Here, we address this limitation. First, we argue that edges, contours and boundaries, despite their overlaps, are three distinct visual features requiring separate benchmark datasets. To this end, we present a new dataset of edges. Second, we propose a novel architecture, termed Dense Extreme Inception Network for Edge Detection (DexiNed), that can be trained from scratch without any pre-trained weights. DexiNed outperforms other algorithms in the presented dataset. It also generalizes well to other datasets without any fine-tuning. The higher quality of DexiNed is also perceptually evident thanks to the sharper and finer edges it outputs.
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Notes MSIAU Approved no
Call Number Admin @ si @ SSH2023 Serial 3982
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Author Josep M. Gonfaus; Marco Pedersoli; Jordi Gonzalez; Andrea Vedaldi; Xavier Roca
Title Factorized appearances for object detection Type Journal Article
Year 2015 Publication Computer Vision and Image Understanding Abbreviated Journal CVIU
Volume (down) 138 Issue Pages 92–101
Keywords Object recognition; Deformable part models; Learning and sharing parts; Discovering discriminative parts
Abstract Deformable object models capture variations in an object’s appearance that can be represented as image deformations. Other effects such as out-of-plane rotations, three-dimensional articulations, and self-occlusions are often captured by considering mixture of deformable models, one per object aspect. A more scalable approach is representing instead the variations at the level of the object parts, applying the concept of a mixture locally. Combining a few part variations can in fact cheaply generate a large number of global appearances.

A limited version of this idea was proposed by Yang and Ramanan [1], for human pose dectection. In this paper we apply it to the task of generic object category detection and extend it in several ways. First, we propose a model for the relationship between part appearances more general than the tree of Yang and Ramanan [1], which is more suitable for generic categories. Second, we treat part locations as well as their appearance as latent variables so that training does not need part annotations but only the object bounding boxes. Third, we modify the weakly-supervised learning of Felzenszwalb et al. and Girshick et al. [2], [3] to handle a significantly more complex latent structure.
Our model is evaluated on standard object detection benchmarks and is found to improve over existing approaches, yielding state-of-the-art results for several object categories.
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Notes ISE; 600.063; 600.078 Approved no
Call Number Admin @ si @ GPG2015 Serial 2705
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Author Muhammad Anwer Rao; Fahad Shahbaz Khan; Joost Van de Weijer; Matthieu Molinier; Jorma Laaksonen
Title Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification Type Journal Article
Year 2018 Publication ISPRS Journal of Photogrammetry and Remote Sensing Abbreviated Journal ISPRS J
Volume (down) 138 Issue Pages 74-85
Keywords Remote sensing; Deep learning; Scene classification; Local Binary Patterns; Texture analysis
Abstract Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene
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Notes LAMP; 600.109; 600.106; 600.120 Approved no
Call Number Admin @ si @ RKW2018 Serial 3158
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Author Giuseppe Pezzano; Oliver Diaz; Vicent Ribas Ripoll; Petia Radeva
Title CoLe-CNN+: Context learning – Convolutional neural network for COVID-19-Ground-Glass-Opacities detection and segmentation Type Journal Article
Year 2021 Publication Computers in Biology and Medicine Abbreviated Journal CBM
Volume (down) 136 Issue Pages 104689
Keywords
Abstract The most common tool for population-wide COVID-19 identification is the Reverse Transcription-Polymerase Chain Reaction test that detects the presence of the virus in the throat (or sputum) in swab samples. This test has a sensitivity between 59% and 71%. However, this test does not provide precise information regarding the extension of the pulmonary infection. Moreover, it has been proven that through the reading of a computed tomography (CT) scan, a clinician can provide a more complete perspective of the severity of the disease. Therefore, we propose a comprehensive system for fully-automated COVID-19 detection and lesion segmentation from CT scans, powered by deep learning strategies to support decision-making process for the diagnosis of COVID-19.
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Notes MILAB; no menciona Approved no
Call Number Admin @ si @ PDR2021 Serial 3635
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