Eduardo Aguilar, & Petia Radeva. (2020). Uncertainty-aware integration of local and flat classifiers for food recognition. PRL - Pattern Recognition Letters, 136, 237–243.
Abstract: Food image recognition has recently attracted the attention of many researchers, due to the challenging problem it poses, the ease collection of food images, and its numerous applications to health and leisure. In real applications, it is necessary to analyze and recognize thousands of different foods. For this purpose, we propose a novel prediction scheme based on a class hierarchy that considers local classifiers, in addition to a flat classifier. In order to make a decision about which approach to use, we define different criteria that take into account both the analysis of the Epistemic Uncertainty estimated from the ‘children’ classifiers and the prediction from the ‘parent’ classifier. We evaluate our proposal using three Uncertainty estimation methods, tested on two public food datasets. The results show that the proposed method reduces parent-child error propagation in hierarchical schemes and improves classification results compared to the single flat classifier, meanwhile maintains good performance regardless the Uncertainty estimation method chosen.
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Carola Figueroa Flores, David Berga, Joost Van de Weijer, & Bogdan Raducanu. (2021). Saliency for free: Saliency prediction as a side-effect of object recognition. PRL - Pattern Recognition Letters, 150, 1–7.
Abstract: Saliency is the perceptual capacity of our visual system to focus our attention (i.e. gaze) on relevant objects instead of the background. So far, computational methods for saliency estimation required the explicit generation of a saliency map, process which is usually achieved via eyetracking experiments on still images. This is a tedious process that needs to be repeated for each new dataset. In the current paper, we demonstrate that is possible to automatically generate saliency maps without ground-truth. In our approach, saliency maps are learned as a side effect of object recognition. Extensive experiments carried out on both real and synthetic datasets demonstrated that our approach is able to generate accurate saliency maps, achieving competitive results when compared with supervised methods.
Keywords: Saliency maps; Unsupervised learning; Object recognition
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Arka Ujjal Dey, Suman Ghosh, Ernest Valveny, & Gaurav Harit. (2021). Beyond Visual Semantics: Exploring the Role of Scene Text in Image Understanding. PRL - Pattern Recognition Letters, 149, 164–171.
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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Kai Wang, Joost Van de Weijer, & Luis Herranz. (2021). ACAE-REMIND for online continual learning with compressed feature replay. PRL - Pattern Recognition Letters, 150, 122–129.
Abstract: Online continual learning aims to learn from a non-IID stream of data from a number of different tasks, where the learner is only allowed to consider data once. Methods are typically allowed to use a limited buffer to store some of the images in the stream. Recently, it was found that feature replay, where an intermediate layer representation of the image is stored (or generated) leads to superior results than image replay, while requiring less memory. Quantized exemplars can further reduce the memory usage. However, a drawback of these methods is that they use a fixed (or very intransigent) backbone network. This significantly limits the learning of representations that can discriminate between all tasks. To address this problem, we propose an auxiliary classifier auto-encoder (ACAE) module for feature replay at intermediate layers with high compression rates. The reduced memory footprint per image allows us to save more exemplars for replay. In our experiments, we conduct task-agnostic evaluation under online continual learning setting and get state-of-the-art performance on ImageNet-Subset, CIFAR100 and CIFAR10 dataset.
Keywords: online continual learning; autoencoders; vector quantization
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Daniel Hernandez, Alejandro Chacon, Antonio Espinosa, David Vazquez, Juan Carlos Moure, & Antonio Lopez. (2016). Embedded real-time stereo estimation via Semi-Global Matching on the GPU. In 16th International Conference on Computational Science (Vol. 80, pp. 143–153).
Abstract: Dense, robust and real-time computation of depth information from stereo-camera systems is a computationally demanding requirement for robotics, advanced driver assistance systems (ADAS) and autonomous vehicles. Semi-Global Matching (SGM) is a widely used algorithm that propagates consistency constraints along several paths across the image. This work presents a real-time system producing reliable disparity estimation results on the new embedded energy-efficient GPU devices. Our design runs on a Tegra X1 at 41 frames per second for an image size of 640x480, 128 disparity levels, and using 4 path directions for the SGM method.
Keywords: Autonomous Driving; Stereo; CUDA; 3d reconstruction
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Victor Campmany, Sergio Silva, Antonio Espinosa, Juan Carlos Moure, David Vazquez, & Antonio Lopez. (2016). GPU-based pedestrian detection for autonomous driving. In 16th International Conference on Computational Science (Vol. 80, pp. 2377–2381).
Abstract: We propose a real-time pedestrian detection system for the embedded Nvidia Tegra X1 GPU-CPU hybrid platform. The pipeline is composed by the following state-of-the-art algorithms: Histogram of Local Binary Patterns (LBP) and Histograms of Oriented Gradients (HOG) features extracted from the input image; Pyramidal Sliding Window technique for foreground segmentation; and Support Vector Machine (SVM) for classification. Results show a 8x speedup in the target Tegra X1 platform and a better performance/watt ratio than desktop CUDA platforms in study.
Keywords: Pedestrian detection; Autonomous Driving; CUDA
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Miguel Oliveira, Victor Santos, Angel Sappa, P. Dias, & A. Moreira. (2016). Incremental texture mapping for autonomous driving. RAS - Robotics and Autonomous Systems, 84, 113–128.
Abstract: Autonomous vehicles have a large number of on-board sensors, not only for providing coverage all around the vehicle, but also to ensure multi-modality in the observation of the scene. Because of this, it is not trivial to come up with a single, unique representation that feeds from the data given by all these sensors. We propose an algorithm which is capable of mapping texture collected from vision based sensors onto a geometric description of the scenario constructed from data provided by 3D sensors. The algorithm uses a constrained Delaunay triangulation to produce a mesh which is updated using a specially devised sequence of operations. These enforce a partial configuration of the mesh that avoids bad quality textures and ensures that there are no gaps in the texture. Results show that this algorithm is capable of producing fine quality textures.
Keywords: Scene reconstruction; Autonomous driving; Texture mapping
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Thanh Ha Do, Salvatore Tabbone, & Oriol Ramos Terrades. (2016). Sparse representation over learned dictionary for symbol recognition. SP - Signal Processing, 125, 36–47.
Abstract: In this paper we propose an original sparse vector model for symbol retrieval task. More speci cally, we apply the K-SVD algorithm for learning a visual dictionary based on symbol descriptors locally computed around interest points. Results on benchmark datasets show that the obtained sparse representation is competitive related to state-of-the-art methods. Moreover, our sparse representation is invariant to rotation and scale transforms and also robust to degraded images and distorted symbols. Thereby, the learned visual dictionary is able to represent instances of unseen classes of symbols.
Keywords: Symbol Recognition; Sparse Representation; Learned Dictionary; Shape Context; Interest Points
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David Berga, Xose R. Fernandez-Vidal, Xavier Otazu, V. Leboran, & Xose M. Pardo. (2019). Psychophysical evaluation of individual low-level feature influences on visual attention. VR - Vision Research, 154, 60–79.
Abstract: In this study we provide the analysis of eye movement behavior elicited by low-level feature distinctiveness with a dataset of synthetically-generated image patterns. Design of visual stimuli was inspired by the ones used in previous psychophysical experiments, namely in free-viewing and visual searching tasks, to provide a total of 15 types of stimuli, divided according to the task and feature to be analyzed. Our interest is to analyze the influences of low-level feature contrast between a salient region and the rest of distractors, providing fixation localization characteristics and reaction time of landing inside the salient region. Eye-tracking data was collected from 34 participants during the viewing of a 230 images dataset. Results show that saliency is predominantly and distinctively influenced by: 1. feature type, 2. feature contrast, 3. temporality of fixations, 4. task difficulty and 5. center bias. This experimentation proposes a new psychophysical basis for saliency model evaluation using synthetic images.
Keywords: Visual attention; Psychophysics; Saliency; Task; Context; Contrast; Center bias; Low-level; Synthetic; Dataset
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I. Sorodoc, S. Pezzelle, A. Herbelot, Mariella Dimiccoli, & R. Bernardi. (2018). Learning quantification from images: A structured neural architecture. NLE - Natural Language Engineering, 24(3), 363–392.
Abstract: Major advances have recently been made in merging language and vision representations. Most tasks considered so far have confined themselves to the processing of objects and lexicalised relations amongst objects (content words). We know, however, that humans (even pre-school children) can abstract over raw multimodal data to perform certain types of higher level reasoning, expressed in natural language by function words. A case in point is given by their ability to learn quantifiers, i.e. expressions like few, some and all. From formal semantics and cognitive linguistics, we know that quantifiers are relations over sets which, as a simplification, we can see as proportions. For instance, in most fish are red, most encodes the proportion of fish which are red fish. In this paper, we study how well current neural network strategies model such relations. We propose a task where, given an image and a query expressed by an object–property pair, the system must return a quantifier expressing which proportions of the queried object have the queried property. Our contributions are twofold. First, we show that the best performance on this task involves coupling state-of-the-art attention mechanisms with a network architecture mirroring the logical structure assigned to quantifiers by classic linguistic formalisation. Second, we introduce a new balanced dataset of image scenarios associated with quantification queries, which we hope will foster further research in this area.
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Ajian Liu, Xuan Li, Jun Wan, Yanyan Liang, Sergio Escalera, Hugo Jair Escalante, et al. (2020). Cross-ethnicity Face Anti-spoofing Recognition Challenge: A Review. BIO - IET Biometrics, 10(1), 24–43.
Abstract: Face anti-spoofing is critical to prevent face recognition systems from a security breach. The biometrics community has %possessed achieved impressive progress recently due the excellent performance of deep neural networks and the availability of large datasets. Although ethnic bias has been verified to severely affect the performance of face recognition systems, it still remains an open research problem in face anti-spoofing. Recently, a multi-ethnic face anti-spoofing dataset, CASIA-SURF CeFA, has been released with the goal of measuring the ethnic bias. It is the largest up to date cross-ethnicity face anti-spoofing dataset covering 3 ethnicities, 3 modalities, 1,607 subjects, 2D plus 3D attack types, and the first dataset including explicit ethnic labels among the recently released datasets for face anti-spoofing. We organized the Chalearn Face Anti-spoofing Attack Detection Challenge which consists of single-modal (e.g., RGB) and multi-modal (e.g., RGB, Depth, Infrared (IR)) tracks around this novel resource to boost research aiming to alleviate the ethnic bias. Both tracks have attracted 340 teams in the development stage, and finally 11 and 8 teams have submitted their codes in the single-modal and multi-modal face anti-spoofing recognition challenges, respectively. All the results were verified and re-ran by the organizing team, and the results were used for the final ranking. This paper presents an overview of the challenge, including its design, evaluation protocol and a summary of results. We analyze the top ranked solutions and draw conclusions derived from the competition. In addition we outline future work directions.
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Hannes Mueller, Andre Groeger, Jonathan Hersh, Andrea Matranga, & Joan Serrat. (2021). Monitoring war destruction from space using machine learning. PNAS - Proceedings of the National Academy of Sciences of the United States of America, 118(23), e2025400118.
Abstract: Existing data on building destruction in conflict zones rely on eyewitness reports or manual detection, which makes it generally scarce, incomplete, and potentially biased. This lack of reliable data imposes severe limitations for media reporting, humanitarian relief efforts, human-rights monitoring, reconstruction initiatives, and academic studies of violent conflict. This article introduces an automated method of measuring destruction in high-resolution satellite images using deep-learning techniques combined with label augmentation and spatial and temporal smoothing, which exploit the underlying spatial and temporal structure of destruction. As a proof of concept, we apply this method to the Syrian civil war and reconstruct the evolution of damage in major cities across the country. Our approach allows generating destruction data with unprecedented scope, resolution, and frequency—and makes use of the ever-higher frequency at which satellite imagery becomes available.
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Beata Megyesi, Bernhard Esslinger, Alicia Fornes, Nils Kopal, Benedek Lang, George Lasry, et al. (2020). Decryption of historical manuscripts: the DECRYPT project. CRYPT - Cryptologia, 44(6), 545–559.
Abstract: Many historians and linguists are working individually and in an uncoordinated fashion on the identification and decryption of historical ciphers. This is a time-consuming process as they often work without access to automatic methods and processes that can accelerate the decipherment. At the same time, computer scientists and cryptologists are developing algorithms to decrypt various cipher types without having access to a large number of original ciphertexts. In this paper, we describe the DECRYPT project aiming at the creation of resources and tools for historical cryptology by bringing the expertise of various disciplines together for collecting data, exchanging methods for faster progress to transcribe, decrypt and contextualize historical encrypted manuscripts. We present our goals and work-in progress of a general approach for analyzing historical encrypted manuscripts using standardized methods and a new set of state-of-the-art tools. We release the data and tools as open-source hoping that all mentioned disciplines would benefit and contribute to the research infrastructure of historical cryptology.
Keywords: automatic decryption; cipher collection; historical cryptology; image transcription
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David Berga, & Xavier Otazu. (2020). Modeling Bottom-Up and Top-Down Attention with a Neurodynamic Model of V1. NEUCOM - Neurocomputing, 417, 270–289.
Abstract: Previous studies suggested that lateral interactions of V1 cells are responsible, among other visual effects, of bottom-up visual attention (alternatively named visual salience or saliency). Our objective is to mimic these connections with a neurodynamic network of firing-rate neurons in order to predict visual attention. Early visual subcortical processes (i.e. retinal and thalamic) are functionally simulated. An implementation of the cortical magnification function is included to define the retinotopical projections towards V1, processing neuronal activity for each distinct view during scene observation. Novel computational definitions of top-down inhibition (in terms of inhibition of return, oculomotor and selection mechanisms), are also proposed to predict attention in Free-Viewing and Visual Search tasks. Results show that our model outpeforms other biologically inspired models of saliency prediction while predicting visual saccade sequences with the same model. We also show how temporal and spatial characteristics of saccade amplitude and inhibition of return can improve prediction of saccades, as well as how distinct search strategies (in terms of feature-selective or category-specific inhibition) can predict attention at distinct image contexts.
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Debora Gil, Antonio Esteban Lansaque, Agnes Borras, & Carles Sanchez. (2019). Enhancing virtual bronchoscopy with intra-operative data using a multi-objective GAN. IJCAR - International Journal of Computer Assisted Radiology and Surgery, 7(1).
Abstract: This manuscript has been withdrawn by bioRxiv due to upload of an incorrect version of the manuscript by the authors. Therefore, this manuscript should not be cited as reference for this project.
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