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Yaxing Wang, Abel Gonzalez-Garcia, Luis Herranz, & Joost Van de Weijer. (2021). Controlling biases and diversity in diverse image-to-image translation. CVIU - Computer Vision and Image Understanding, 202, 103082.
Abstract: JCR 2019 Q2, IF=3.121
The task of unpaired image-to-image translation is highly challenging due to the lack of explicit cross-domain pairs of instances. We consider here diverse image translation (DIT), an even more challenging setting in which an image can have multiple plausible translations. This is normally achieved by explicitly disentangling content and style in the latent representation and sampling different styles codes while maintaining the image content. Despite the success of current DIT models, they are prone to suffer from bias. In this paper, we study the problem of bias in image-to-image translation. Biased datasets may add undesired changes (e.g. change gender or race in face images) to the output translations as a consequence of the particular underlying visual distribution in the target domain. In order to alleviate the effects of this problem we propose the use of semantic constraints that enforce the preservation of desired image properties. Our proposed model is a step towards unbiased diverse image-to-image translation (UDIT), and results in less unwanted changes in the translated images while still performing the wanted transformation. Experiments on several heavily biased datasets show the effectiveness of the proposed techniques in different domains such as faces, objects, and scenes.
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Kai Wang, Luis Herranz, & Joost Van de Weijer. (2021). Continual learning in cross-modal retrieval. In 2nd CLVISION workshop (pp. 3628–3638).
Abstract: Multimodal representations and continual learning are two areas closely related to human intelligence. The former considers the learning of shared representation spaces where information from different modalities can be compared and integrated (we focus on cross-modal retrieval between language and visual representations). The latter studies how to prevent forgetting a previously learned task when learning a new one. While humans excel in these two aspects, deep neural networks are still quite limited. In this paper, we propose a combination of both problems into a continual cross-modal retrieval setting, where we study how the catastrophic interference caused by new tasks impacts the embedding spaces and their cross-modal alignment required for effective retrieval. We propose a general framework that decouples the training, indexing and querying stages. We also identify and study different factors that may lead to forgetting, and propose tools to alleviate it. We found that the indexing stage pays an important role and that simply avoiding reindexing the database with updated embedding networks can lead to significant gains. We evaluated our methods in two image-text retrieval datasets, obtaining significant gains with respect to the fine tuning baseline.
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Cristina Palmero, Javier Selva, Sorina Smeureanu, Julio C. S. Jacques Junior, Albert Clapes, Alexa Mosegui, et al. (2021). Context-Aware Personality Inference in Dyadic Scenarios: Introducing the UDIVA Dataset. In IEEE Winter Conference on Applications of Computer Vision (pp. 1–12).
Abstract: This paper introduces UDIVA, a new non-acted dataset of face-to-face dyadic interactions, where interlocutors perform competitive and collaborative tasks with different behavior elicitation and cognitive workload. The dataset consists of 90.5 hours of dyadic interactions among 147 participants distributed in 188 sessions, recorded using multiple audiovisual and physiological sensors. Currently, it includes sociodemographic, self- and peer-reported personality, internal state, and relationship profiling from participants. As an initial analysis on UDIVA, we propose a
transformer-based method for self-reported personality inference in dyadic scenarios, which uses audiovisual data and different sources of context from both interlocutors to
regress a target person’s personality traits. Preliminary results from an incremental study show consistent improvements when using all available context information.
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Lei Kang, Pau Riba, Marcal Rusinol, Alicia Fornes, & Mauricio Villegas. (2021). Content and Style Aware Generation of Text-line Images for Handwriting Recognition. TPAMI - IEEE Transactions on Pattern Analysis and Machine Intelligence, .
Abstract: Handwritten Text Recognition has achieved an impressive performance in public benchmarks. However, due to the high inter- and intra-class variability between handwriting styles, such recognizers need to be trained using huge volumes of manually labeled training data. To alleviate this labor-consuming problem, synthetic data produced with TrueType fonts has been often used in the training loop to gain volume and augment the handwriting style variability. However, there is a significant style bias between synthetic and real data which hinders the improvement of recognition performance. To deal with such limitations, we propose a generative method for handwritten text-line images, which is conditioned on both visual appearance and textual content. Our method is able to produce long text-line samples with diverse handwriting styles. Once properly trained, our method can also be adapted to new target data by only accessing unlabeled text-line images to mimic handwritten styles and produce images with any textual content. Extensive experiments have been done on making use of the generated samples to boost Handwritten Text Recognition performance. Both qualitative and quantitative results demonstrate that the proposed approach outperforms the current state of the art.
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Michael Teutsch, Angel Sappa, & Riad I. Hammoud. (2021). Computer Vision in the Infrared Spectrum: Challenges and Approaches (Vol. 10).
Abstract: Human visual perception is limited to the visual-optical spectrum. Machine vision is not. Cameras sensitive to the different infrared spectra can enhance the abilities of autonomous systems and visually perceive the environment in a holistic way. Relevant scene content can be made visible especially in situations, where sensors of other modalities face issues like a visual-optical camera that needs a source of illumination. As a consequence, not only human mistakes can be avoided by increasing the level of automation, but also machine-induced errors can be reduced that, for example, could make a self-driving car crash into a pedestrian under difficult illumination conditions. Furthermore, multi-spectral sensor systems with infrared imagery as one modality are a rich source of information and can provably increase the robustness of many autonomous systems. Applications that can benefit from utilizing infrared imagery range from robotics to automotive and from biometrics to surveillance. In this book, we provide a brief yet concise introduction to the current state-of-the-art of computer vision and machine learning in the infrared spectrum. Based on various popular computer vision tasks such as image enhancement, object detection, or object tracking, we first motivate each task starting from established literature in the visual-optical spectrum. Then, we discuss the differences between processing images and videos in the visual-optical spectrum and the various infrared spectra. An overview of the current literature is provided together with an outlook for each task. Furthermore, available and annotated public datasets and common evaluation methods and metrics are presented. In a separate chapter, popular applications that can greatly benefit from the use of infrared imagery as a data source are presented and discussed. Among them are automatic target recognition, video surveillance, or biometrics including face recognition. Finally, we conclude with recommendations for well-fitting sensor setups and data processing algorithms for certain computer vision tasks. We address this book to prospective researchers and engineers new to the field but also to anyone who wants to get introduced to the challenges and the approaches of computer vision using infrared images or videos. Readers will be able to start their work directly after reading the book supported by a highly comprehensive backlog of recent and relevant literature as well as related infrared datasets including existing evaluation frameworks. Together with consistently decreasing costs for infrared cameras, new fields of application appear and make computer vision in the infrared spectrum a great opportunity to face nowadays scientific and engineering challenges.
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Henry Velesaca, Patricia Suarez, Raul Mira, & Angel Sappa. (2021). Computer Vision based Food Grain Classification: a Comprehensive Survey. CEA - Computers and Electronics in Agriculture, 187, 106287.
Abstract: This manuscript presents a comprehensive survey on recent computer vision based food grain classification techniques. It includes state-of-the-art approaches intended for different grain varieties. The approaches proposed in the literature are analyzed according to the processing stages considered in the classification pipeline, making it easier to identify common techniques and comparisons. Additionally, the type of images considered by each approach (i.e., images from the: visible, infrared, multispectral, hyperspectral bands) together with the strategy used to generate ground truth data (i.e., real and synthetic images) are reviewed. Finally, conclusions highlighting future needs and challenges are presented.
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Domicele Jonauskaite, Lucia Camenzind, C. Alejandro Parraga, Cecile N Diouf, Mathieu Mercapide Ducommun, Lauriane Müller, et al. (2021). Colour-emotion associations in individuals with red-green colour blindness. PeerJ, 9, e11180.
Abstract: Colours and emotions are associated in languages and traditions. Some of us may convey sadness by saying feeling blue or by wearing black clothes at funerals. The first example is a conceptual experience of colour and the second example is an immediate perceptual experience of colour. To investigate whether one or the other type of experience more strongly drives colour-emotion associations, we tested 64 congenitally red-green colour-blind men and 66 non-colour-blind men. All participants associated 12 colours, presented as terms or patches, with 20 emotion concepts, and rated intensities of the associated emotions. We found that colour-blind and non-colour-blind men associated similar emotions with colours, irrespective of whether colours were conveyed via terms (r = .82) or patches (r = .80). The colour-emotion associations and the emotion intensities were not modulated by participants' severity of colour blindness. Hinting at some additional, although minor, role of actual colour perception, the consistencies in associations for colour terms and patches were higher in non-colour-blind than colour-blind men. Together, these results suggest that colour-emotion associations in adults do not require immediate perceptual colour experiences, as conceptual experiences are sufficient.
Keywords: Affect; Chromotherapy; Colour cognition; Colour vision deficiency; Cross-modal correspondences; Daltonism; Deuteranopia; Dichromatic; Emotion; Protanopia.
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Giuseppe Pezzano, Vicent Ribas Ripoll, & Petia Radeva. (2021). CoLe-CNN: Context-learning convolutional neural network with adaptive loss function for lung nodule segmentation. CMPB - Computer Methods and Programs in Biomedicine, 198, 105792.
Abstract: Background and objective:An accurate segmentation of lung nodules in computed tomography images is a crucial step for the physical characterization of the tumour. Being often completely manually accomplished, nodule segmentation turns to be a tedious and time-consuming procedure and this represents a high obstacle in clinical practice. In this paper, we propose a novel Convolutional Neural Network for nodule segmentation that combines a light and efficient architecture with innovative loss function and segmentation strategy. Methods:In contrast to most of the standard end-to-end architectures for nodule segmentation, our network learns the context of the nodules by producing two masks representing all the background and secondary-important elements in the Computed Tomography scan. The nodule is detected by subtracting the context from the original scan image. Additionally, we introduce an asymmetric loss function that automatically compensates for potential errors in the nodule annotations. We trained and tested our Neural Network on the public LIDC-IDRI database, compared it with the state of the art and run a pseudo-Turing test between four radiologists and the network. Results:The results proved that the behaviour of the algorithm is very near to the human performance and its segmentation masks are almost indistinguishable from the ones made by the radiologists. Our method clearly outperforms the state of the art on CT nodule segmentation in terms of F1 score and IoU of and respectively. Conclusions: The main structure of the network ensures all the properties of the UNet architecture, while the Multi Convolutional Layers give a more accurate pattern recognition. The newly adopted solutions also increase the details on the border of the nodule, even under the noisiest conditions. This method can be applied now for single CT slice nodule segmentation and it represents a starting point for the future development of a fully automatic 3D segmentation software.
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Giuseppe Pezzano, Oliver Diaz, Vicent Ribas Ripoll, & Petia Radeva. (2021). CoLe-CNN+: Context learning – Convolutional neural network for COVID-19-Ground-Glass-Opacities detection and segmentation. CBM - Computers in Biology and Medicine, 136, 104689.
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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Jose Luis Gomez, Gabriel Villalonga, & Antonio Lopez. (2021). Co-Training for Deep Object Detection: Comparing Single-Modal and Multi-Modal Approaches. SENS - Sensors, 21(9), 3185.
Abstract: Top-performing computer vision models are powered by convolutional neural networks (CNNs). Training an accurate CNN highly depends on both the raw sensor data and their associated ground truth (GT). Collecting such GT is usually done through human labeling, which is time-consuming and does not scale as we wish. This data-labeling bottleneck may be intensified due to domain shifts among image sensors, which could force per-sensor data labeling. In this paper, we focus on the use of co-training, a semi-supervised learning (SSL) method, for obtaining self-labeled object bounding boxes (BBs), i.e., the GT to train deep object detectors. In particular, we assess the goodness of multi-modal co-training by relying on two different views of an image, namely, appearance (RGB) and estimated depth (D). Moreover, we compare appearance-based single-modal co-training with multi-modal. Our results suggest that in a standard SSL setting (no domain shift, a few human-labeled data) and under virtual-to-real domain shift (many virtual-world labeled data, no human-labeled data) multi-modal co-training outperforms single-modal. In the latter case, by performing GAN-based domain translation both co-training modalities are on par, at least when using an off-the-shelf depth estimation model not specifically trained on the translated images.
Keywords: co-training; multi-modality; vision-based object detection; ADAS; self-driving
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AN Ruchai, VI Kober, KA Dorofeev, VN Karnaukhov, & Mikhail Mozerov. (2021). Classification of breast abnormalities using a deep convolutional neural network and transfer learning. Journal of Communications Technology and Electronics, 66(6), 778–783.
Abstract: A new algorithm for classification of breast pathologies in digital mammography using a convolutional neural network and transfer learning is proposed. The following pretrained neural networks were chosen: MobileNetV2, InceptionResNetV2, Xception, and ResNetV2. All mammographic images were pre-processed to improve classification reliability. Transfer training was carried out using additional data augmentation and fine-tuning. The performance of the proposed algorithm for classification of breast pathologies in terms of accuracy on real data is discussed and compared with that of state-of-the-art algorithms on the available MIAS database.
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Mireia Sole, Joan Blanco, Debora Gil, Oliver Valero, Alvaro Pascual, B. Cardenas, et al. (2021). Chromosomal positioning in spermatogenic cells is influenced by chromosomal factors associated with gene activity, bouquet formation, and meiotic sex-chromosome inactivation. Chromosoma, 130, 163–175.
Abstract: Chromosome territoriality is not random along the cell cycle and it is mainly governed by intrinsic chromosome factors and gene expression patterns. Conversely, very few studies have explored the factors that determine chromosome territoriality and its influencing factors during meiosis. In this study, we analysed chromosome positioning in murine spermatogenic cells using three-dimensionally fluorescence in situ hybridization-based methodology, which allows the analysis of the entire karyotype. The main objective of the study was to decipher chromosome positioning in a radial axis (all analysed germ-cell nuclei) and longitudinal axis (only spermatozoa) and to identify the chromosomal factors that regulate such an arrangement. Results demonstrated that the radial positioning of chromosomes during spermatogenesis was cell-type specific and influenced by chromosomal factors associated to gene activity. Chromosomes with specific features that enhance transcription (high GC content, high gene density and high numbers of predicted expressed genes) were preferentially observed in the inner part of the nucleus in virtually all cell types. Moreover, the position of the sex chromosomes was influenced by their transcriptional status, from the periphery of the nucleus when its activity was repressed (pachytene) to a more internal position when it is partially activated (spermatid). At pachytene, chromosome positioning was also influenced by chromosome size due to the bouquet formation. Longitudinal chromosome positioning in the sperm nucleus was not random either, suggesting the importance of ordered longitudinal positioning for the release and activation of the paternal genome after fertilisation.
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Xim Cerda-Company, Olivier Penacchio, & Xavier Otazu. (2021). Chromatic Induction in Migraine. VISION, 37.
Abstract: The human visual system is not a colorimeter. The perceived colour of a region does not only depend on its colour spectrum, but also on the colour spectra and geometric arrangement of neighbouring regions, a phenomenon called chromatic induction. Chromatic induction is thought to be driven by lateral interactions: the activity of a central neuron is modified by stimuli outside its classical receptive field through excitatory–inhibitory mechanisms. As there is growing evidence of an excitation/inhibition imbalance in migraine, we compared chromatic induction in migraine and control groups. As hypothesised, we found a difference in the strength of induction between the two groups, with stronger induction effects in migraine. On the other hand, given the increased prevalence of visual phenomena in migraine with aura, we also hypothesised that the difference between migraine and control would be more important in migraine with aura than in migraine without aura. Our experiments did not support this hypothesis. Taken together, our results suggest a link between excitation/inhibition imbalance and increased induction effects.
Keywords: migraine; vision; colour; colour perception; chromatic induction; psychophysics
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Diana Ramirez Cifuentes, Ana Freire, Ricardo Baeza Yates, Nadia Sanz Lamora, Aida Alvarez, Alexandre Gonzalez, et al. (2021). Characterization of Anorexia Nervosa on Social Media: Textual, Visual, Relational, Behavioral, and Demographical Analysis. JMIR - Journal of Medical Internet Research, 23(7), e25925.
Abstract: Background: Eating disorders are psychological conditions characterized by unhealthy eating habits. Anorexia nervosa (AN) is defined as the belief of being overweight despite being dangerously underweight. The psychological signs involve emotional and behavioral issues. There is evidence that signs and symptoms can manifest on social media, wherein both harmful and beneficial content is shared daily.
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Ozge Mercanoglu Sincan, Julio C. S. Jacques Junior, Sergio Escalera, & Hacer Yalim Keles. (2021). ChaLearn LAP Large Scale Signer Independent Isolated Sign Language Recognition Challenge: Design, Results and Future Research. In Conference on Computer Vision and Pattern Recognition Workshops (pp. 3467–3476).
Abstract: The performances of Sign Language Recognition (SLR) systems have improved considerably in recent years. However, several open challenges still need to be solved to allow SLR to be useful in practice. The research in the field is in its infancy in regards to the robustness of the models to a large diversity of signs and signers, and to fairness of the models to performers from different demographics. This work summarises the ChaLearn LAP Large Scale Signer Independent Isolated SLR Challenge, organised at CVPR 2021 with the goal of overcoming some of the aforementioned challenges. We analyse and discuss the challenge design, top winning solutions and suggestions for future research. The challenge attracted 132 participants in the RGB track and 59 in the RGB+Depth track, receiving more than 1.5K submissions in total. Participants were evaluated using a new large-scale multi-modal Turkish Sign Language (AUTSL) dataset, consisting of 226 sign labels and 36,302 isolated sign video samples performed by 43 different signers. Winning teams achieved more than 96% recognition rate, and their approaches benefited from pose/hand/face estimation, transfer learning, external data, fusion/ensemble of modalities and different strategies to model spatio-temporal information. However, methods still fail to distinguish among very similar signs, in particular those sharing similar hand trajectories.
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