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Author |
Petia Radeva |
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Title |
Uncertainty Modeling within an End-to-end Framework for Food Image Analysis |
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Conference Article |
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2020 |
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1st DELTA |
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MILAB |
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Admin @ si @ Rad2020 |
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3527 |
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Martin Menchon; Estefania Talavera; Jose M. Massa; Petia Radeva |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Behavioural Pattern Discovery from Collections of Egocentric Photo-Streams |
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Conference Article |
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2020 |
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ECCV Workshops |
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12538 |
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469-484 |
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The automatic discovery of behaviour is of high importance when aiming to assess and improve the quality of life of people. Egocentric images offer a rich and objective description of the daily life of the camera wearer. This work proposes a new method to identify a person’s patterns of behaviour from collected egocentric photo-streams. Our model characterizes time-frames based on the context (place, activities and environment objects) that define the images composition. Based on the similarity among the time-frames that describe the collected days for a user, we propose a new unsupervised greedy method to discover the behavioural pattern set based on a novel semantic clustering approach. Moreover, we present a new score metric to evaluate the performance of the proposed algorithm. We validate our method on 104 days and more than 100k images extracted from 7 users. Results show that behavioural patterns can be discovered to characterize the routine of individuals and consequently their lifestyle. |
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Virtual; August 2020 |
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ECCVW |
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MILAB; no proj |
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Admin @ si @ MTM2020 |
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3528 |
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Mariona Caros; Maite Garolera; Petia Radeva; Xavier Giro |
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Title |
Automatic Reminiscence Therapy for Dementia |
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Conference Article |
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2020 |
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10th ACM International Conference on Multimedia Retrieval |
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383-387 |
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With people living longer than ever, the number of cases with dementia such as Alzheimer's disease increases steadily. It affects more than 46 million people worldwide, and it is estimated that in 2050 more than 100 million will be affected. While there are not effective treatments for these terminal diseases, therapies such as reminiscence, that stimulate memories from the past are recommended. Currently, reminiscence therapy takes place in care homes and is guided by a therapist or a carer. In this work, we present an AI-based solution to automatize the reminiscence therapy, which consists in a dialogue system that uses photos as input to generate questions. We run a usability case study with patients diagnosed of mild cognitive impairment that shows they found the system very entertaining and challenging. Overall, this paper presents how reminiscence therapy can be automatized by using machine learning, and deployed to smartphones and laptops, making the therapy more accessible to every person affected by dementia. |
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Virtual; October 2020 |
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ICRM |
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Admin @ si @ CGR2020 |
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3529 |
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Author |
Esmitt Ramirez; Carles Sanchez; Debora Gil |
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Title |
Localizing Pulmonary Lesions Using Fuzzy Deep Learning |
Type |
Conference Article |
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2019 |
Publication |
21st International Symposium on Symbolic and Numeric Algorithms for Scientific Computing |
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290-294 |
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The usage of medical images is part of the clinical daily in several healthcare centers around the world. Particularly, Computer Tomography (CT) images are an important key in the early detection of suspicious lung lesions. The CT image exploration allows the detection of lung lesions before any invasive procedure (e.g. bronchoscopy, biopsy). The effective localization of lesions is performed using different image processing and computer vision techniques. Lately, the usage of deep learning models into medical imaging from detection to prediction shown that is a powerful tool for Computer-aided software. In this paper, we present an approach to localize pulmonary lung lesion using fuzzy deep learning. Our approach uses a simple convolutional neural network based using the LIDC-IDRI dataset. Each image is divided into patches associated a probability vector (fuzzy) according their belonging to anatomical structures on a CT. We showcase our approach as part of a full CAD system to exploration, planning, guiding and detection of pulmonary lesions. |
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Timisoara; Rumania; September 2019 |
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SYNASC |
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IAM; 600.145; 600.140; 601.337; 601.323 |
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Admin @ si @ RSG2019 |
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3531 |
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Cristina Palmero; Javier Selva; Sorina Smeureanu; Julio C. S. Jacques Junior; Albert Clapes; Alexa Mosegui; Zejian Zhang; David Gallardo; Georgina Guilera; David Leiva; Sergio Escalera |
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Title |
Context-Aware Personality Inference in Dyadic Scenarios: Introducing the UDIVA Dataset |
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Conference Article |
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2021 |
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IEEE Winter Conference on Applications of Computer Vision |
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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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Virtual; January 2021 |
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WACV |
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HUPBA |
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Admin @ si @ PSS2021 |
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3532 |
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Julio C. S. Jacques Junior; Agata Lapedriza; Cristina Palmero; Xavier Baro; Sergio Escalera |
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Title |
Person Perception Biases Exposed: Revisiting the First Impressions Dataset |
Type |
Conference Article |
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2021 |
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IEEE Winter Conference on Applications of Computer Vision |
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13-21 |
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This work revisits the ChaLearn First Impressions database, annotated for personality perception using pairwise comparisons via crowdsourcing. We analyse for the first time the original pairwise annotations, and reveal existing person perception biases associated to perceived attributes like gender, ethnicity, age and face attractiveness.
We show how person perception bias can influence data labelling of a subjective task, which has received little attention from the computer vision and machine learning communities by now. We further show that the mechanism used to convert pairwise annotations to continuous values may magnify the biases if no special treatment is considered. The findings of this study are relevant for the computer vision community that is still creating new datasets on subjective tasks, and using them for practical applications, ignoring these perceptual biases. |
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Virtual; January 2021 |
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HUPBA |
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Admin @ si @ JLP2021 |
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3533 |
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Soumick Chatterjee; Fatima Saad; Chompunuch Sarasaen; Suhita Ghosh; Rupali Khatun; Petia Radeva; Georg Rose; Sebastian Stober; Oliver Speck; Andreas Nürnberger |
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Title |
Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images |
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Miscellaneous |
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2020 |
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Arxiv |
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CoRR abs/2006.02570
The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread and has challenged different sectors. One of the most effective ways to limit its spread is the early and accurate diagnosis of infected patients. Medical imaging such as X-ray and Computed Tomography (CT) combined with the potential of Artificial Intelligence (AI) plays an essential role in supporting the medical staff in the diagnosis process. Thereby, the use of five different deep learning models (ResNet18, ResNet34, InceptionV3, InceptionResNetV2, and DenseNet161) and their Ensemble have been used in this paper, to classify COVID-19, pneumoniæ and healthy subjects using Chest X-Ray. Multi-label classification was performed to predict multiple pathologies for each patient, if present. Foremost, the interpretability of each of the networks was thoroughly studied using techniques like occlusion, saliency, input X gradient, guided backpropagation, integrated gradients, and DeepLIFT. The mean Micro-F1 score of the models for COVID-19 classifications ranges from 0.66 to 0.875, and is 0.89 for the Ensemble of the network models. The qualitative results depicted the ResNets to be the most interpretable model. |
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MILAB |
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Admin @ si @ CSS2020 |
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3534 |
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Estefania Talavera; Andreea Glavan; Alina Matei; Petia Radeva |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Eating Habits Discovery in Egocentric Photo-streams |
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Miscellaneous |
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2020 |
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Arxiv |
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CoRR abs/2009.07646
Eating habits are learned throughout the early stages of our lives. However, it is not easy to be aware of how our food-related routine affects our healthy living. In this work, we address the unsupervised discovery of nutritional habits from egocentric photo-streams. We build a food-related behavioural pattern discovery model, which discloses nutritional routines from the activities performed throughout the days. To do so, we rely on Dynamic-Time-Warping for the evaluation of similarity among the collected days. Within this framework, we present a simple, but robust and fast novel classification pipeline that outperforms the state-of-the-art on food-related image classification with a weighted accuracy and F-score of 70% and 63%, respectively. Later, we identify days composed of nutritional activities that do not describe the habits of the person as anomalies in the daily life of the user with the Isolation Forest method. Furthermore, we show an application for the identification of food-related scenes when the camera wearer eats in isolation. Results have shown the good performance of the proposed model and its relevance to visualize the nutritional habits of individuals. |
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MILAB |
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Admin @ si @ TGM2020 |
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3536 |
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Shiqi Yang; Yaxing Wang; Joost Van de Weijer; Luis Herranz |
![download PDF file pdf](img/file_PDF.gif)
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Unsupervised Domain Adaptation without Source Data by Casting a BAIT |
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2020 |
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Arxiv |
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arXiv:2010.12427
Unsupervised domain adaptation (UDA) aims to transfer the knowledge learned from a labeled source domain to an unlabeled target domain. Existing UDA methods require access to source data during adaptation, which may not be feasible in some real-world applications. In this paper, we address the source-free unsupervised domain adaptation (SFUDA) problem, where only the source model is available during the adaptation. We propose a method named BAIT to address SFUDA. Specifically, given only the source model, with the source classifier head fixed, we introduce a new learnable classifier. When adapting to the target domain, class prototypes of the new added classifier will act as a bait. They will first approach the target features which deviate from prototypes of the source classifier due to domain shift. Then those target features are pulled towards the corresponding prototypes of the source classifier, thus achieving feature alignment with the source classifier in the absence of source data. Experimental results show that the proposed method achieves state-of-the-art performance on several benchmark datasets compared with existing UDA and SFUDA methods. |
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LAMP; 600.120 |
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Admin @ si @ YWW2020 |
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3539 |
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Carola Figueroa Flores; Bogdan Raducanu; David Berga; Joost Van de Weijer |
![download PDF file pdf](img/file_PDF.gif)
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Hallucinating Saliency Maps for Fine-Grained Image Classification for Limited Data Domains |
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2021 |
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16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications |
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163-171 |
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arXiv:2007.12562
Most of the saliency methods are evaluated on their ability to generate saliency maps, and not on their functionality in a complete vision pipeline, like for instance, image classification. In the current paper, we propose an approach which does not require explicit saliency maps to improve image classification, but they are learned implicitely, during the training of an end-to-end image classification task. We show that our approach obtains similar results as the case when the saliency maps are provided explicitely. Combining RGB data with saliency maps represents a significant advantage for object recognition, especially for the case when training data is limited. We validate our method on several datasets for fine-grained classification tasks (Flowers, Birds and Cars). In addition, we show that our saliency estimation method, which is trained without any saliency groundtruth data, obtains competitive results on real image saliency benchmark (Toronto), and outperforms deep saliency models with synthetic images (SID4VAM). |
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Virtual; February 2021 |
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LAMP |
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Admin @ si @ FRB2021c |
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3540 |
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Shiqi Yang; Kai Wang; Luis Herranz; Joost Van de Weijer |
![download PDF file pdf](img/file_PDF.gif)
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Simple and effective localized attribute representations for zero-shot learning |
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2020 |
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Arxiv |
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arXiv:2006.05938
Zero-shot learning (ZSL) aims to discriminate images from unseen classes by exploiting relations to seen classes via their semantic descriptions. Some recent papers have shown the importance of localized features together with fine-tuning the feature extractor to obtain discriminative and transferable features. However, these methods require complex attention or part detection modules to perform explicit localization in the visual space. In contrast, in this paper we propose localizing representations in the semantic/attribute space, with a simple but effective pipeline where localization is implicit. Focusing on attribute representations, we show that our method obtains state-of-the-art performance on CUB and SUN datasets, and also achieves competitive results on AWA2 dataset, outperforming generally more complex methods with explicit localization in the visual space. Our method can be implemented easily, which can be used as a new baseline for zero shot-learning. In addition, our localized representations are highly interpretable as attribute-specific heatmaps. |
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LAMP; 600.120 |
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Admin @ si @ YWH2020 |
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3542 |
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Kai Wang; Luis Herranz; Joost Van de Weijer |
![download PDF file pdf](img/file_PDF.gif)
![goto web page (via DOI) doi](img/doi.gif)
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Continual learning in cross-modal retrieval |
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Conference Article |
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2021 |
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2nd CLVISION workshop |
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3628-3638 |
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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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Virtual; June 2021 |
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LAMP; 600.120; 600.141; 600.147; 601.379 |
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Admin @ si @ WHW2021 |
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3566 |
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Vincenzo Lomonaco; Lorenzo Pellegrini; Andrea Cossu; Antonio Carta; Gabriele Graffieti; Tyler L. Hayes; Matthias De Lange; Marc Masana; Jary Pomponi; Gido van de Ven; Martin Mundt; Qi She; Keiland Cooper; Jeremy Forest; Eden Belouadah; Simone Calderara; German I. Parisi; Fabio Cuzzolin; Andreas Tolias; Simone Scardapane; Luca Antiga; Subutai Amhad; Adrian Popescu; Christopher Kanan; Joost Van de Weijer; Tinne Tuytelaars; Davide Bacciu; Davide Maltoni |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
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Avalanche: an End-to-End Library for Continual Learning |
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Conference Article |
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2021 |
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34th IEEE Conference on Computer Vision and Pattern Recognition Workshops |
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3595-3605 |
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Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning. Recently, we have witnessed a renewed and fast-growing interest in continual learning, especially within the deep learning community. However, algorithmic solutions are often difficult to re-implement, evaluate and port across different settings, where even results on standard benchmarks are hard to reproduce. In this work, we propose Avalanche, an open-source end-to-end library for continual learning research based on PyTorch. Avalanche is designed to provide a shared and collaborative codebase for fast prototyping, training, and reproducible evaluation of continual learning algorithms. |
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Virtual; June 2021 |
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LAMP; 600.120 |
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Admin @ si @ LPC2021 |
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3567 |
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Mikel Menta; Adriana Romero; Joost Van de Weijer |
![download PDF file pdf](img/file_PDF.gif)
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Learning to adapt class-specific features across domains for semantic segmentation |
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Miscellaneous |
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2020 |
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Arxiv |
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arXiv:2001.08311
Recent advances in unsupervised domain adaptation have shown the effectiveness of adversarial training to adapt features across domains, endowing neural networks with the capability of being tested on a target domain without requiring any training annotations in this domain. The great majority of existing domain adaptation models rely on image translation networks, which often contain a huge amount of domain-specific parameters. Additionally, the feature adaptation step often happens globally, at a coarse level, hindering its applicability to tasks such as semantic segmentation, where details are of crucial importance to provide sharp results. In this thesis, we present a novel architecture, which learns to adapt features across domains by taking into account per class information. To that aim, we design a conditional pixel-wise discriminator network, whose output is conditioned on the segmentation masks. Moreover, following recent advances in image translation, we adopt the recently introduced StarGAN architecture as image translation backbone, since it is able to perform translations across multiple domains by means of a single generator network. Preliminary results on a segmentation task designed to assess the effectiveness of the proposed approach highlight the potential of the model, improving upon strong baselines and alternative designs. |
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LAMP; 600.120 |
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Admin @ si @ MRW2020 |
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3545 |
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Giovanni Maria Farinella; Petia Radeva; Jose Braz |
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Proceedings of the 15th International Joint Conference on Computer Vision; Imaging and Computer Graphics Theory and Applications |
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2020 |
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Proceedings of the 15th International Joint Conference on Computer Vision; Imaging and Computer Graphics Theory and Applications; VISIGRAPP 2020 |
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4 |
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MILAB |
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Admin @ si @ FRB2020a |
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3546 |
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