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Author | Giuseppe Pezzano; Vicent Ribas Ripoll; Petia Radeva | ||||
Title | CoLe-CNN: Context-learning convolutional neural network with adaptive loss function for lung nodule segmentation | Type | Journal Article | ||
Year | 2021 | Publication | Computer Methods and Programs in Biomedicine | Abbreviated Journal | CMPB |
Volume | 198 | Issue | Pages | 105792 | |
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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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Notes | MILAB; no proj | Approved | no | ||
Call Number | Admin @ si @ PRR2021 | Serial | 3530 | ||
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Author | Esmitt Ramirez; Carles Sanchez; Debora Gil | ||||
Title | Localizing Pulmonary Lesions Using Fuzzy Deep Learning | Type | Conference Article | ||
Year | 2019 | Publication | 21st International Symposium on Symbolic and Numeric Algorithms for Scientific Computing | Abbreviated Journal | |
Volume | Issue | Pages | 290-294 | ||
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Abstract | 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. | ||||
Address | Timisoara; Rumania; September 2019 | ||||
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Area | Expedition | Conference | SYNASC | ||
Notes | IAM; 600.145; 600.140; 601.337; 601.323 | Approved | no | ||
Call Number | Admin @ si @ RSG2019 | Serial | 3531 | ||
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Author | 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 | ||||
Title | Context-Aware Personality Inference in Dyadic Scenarios: Introducing the UDIVA Dataset | Type | Conference Article | ||
Year | 2021 | Publication | IEEE Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 1-12 | ||
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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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Address | Virtual; January 2021 | ||||
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Area | Expedition | Conference | WACV | ||
Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ PSS2021 | Serial | 3532 | ||
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Author | Julio C. S. Jacques Junior; Agata Lapedriza; Cristina Palmero; Xavier Baro; Sergio Escalera | ||||
Title | Person Perception Biases Exposed: Revisiting the First Impressions Dataset | Type | Conference Article | ||
Year | 2021 | Publication | IEEE Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 13-21 | ||
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Abstract | 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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Address | Virtual; January 2021 | ||||
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Area | Expedition | Conference | WACV | ||
Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ JLP2021 | Serial | 3533 | ||
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Author | Soumick Chatterjee; Fatima Saad; Chompunuch Sarasaen; Suhita Ghosh; Rupali Khatun; Petia Radeva; Georg Rose; Sebastian Stober; Oliver Speck; Andreas Nürnberger | ||||
Title | Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images | Type | Miscellaneous | ||
Year | 2020 | Publication | Arxiv | Abbreviated Journal | |
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Abstract | 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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Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ CSS2020 | Serial | 3534 | ||
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Author | Estefania Talavera; Andreea Glavan; Alina Matei; Petia Radeva | ||||
Title | Eating Habits Discovery in Egocentric Photo-streams | Type | Miscellaneous | ||
Year | 2020 | Publication | Arxiv | Abbreviated Journal | |
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Abstract | 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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Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ TGM2020 | Serial | 3536 | ||
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Author | Marc Masana; Xialei Liu; Bartlomiej Twardowski; Mikel Menta; Andrew Bagdanov; Joost Van de Weijer | ||||
Title | Class-incremental learning: survey and performance evaluation | Type | Journal Article | ||
Year | 2022 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
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Abstract | For future learning systems incremental learning is desirable, because it allows for: efficient resource usage by eliminating the need to retrain from scratch at the arrival of new data; reduced memory usage by preventing or limiting the amount of data required to be stored -- also important when privacy limitations are imposed; and learning that more closely resembles human learning. The main challenge for incremental learning is catastrophic forgetting, which refers to the precipitous drop in performance on previously learned tasks after learning a new one. Incremental learning of deep neural networks has seen explosive growth in recent years. Initial work focused on task incremental learning, where a task-ID is provided at inference time. Recently we have seen a shift towards class-incremental learning where the learner must classify at inference time between all classes seen in previous tasks without recourse to a task-ID. In this paper, we provide a complete survey of existing methods for incremental learning, and in particular we perform an extensive experimental evaluation on twelve class-incremental methods. We consider several new experimental scenarios, including a comparison of class-incremental methods on multiple large-scale datasets, investigation into small and large domain shifts, and comparison on various network architectures. | ||||
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Notes | LAMP; 600.120 | Approved | no | ||
Call Number | Admin @ si @ MLT2022 | Serial | 3538 | ||
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Author | Shiqi Yang; Yaxing Wang; Joost Van de Weijer; Luis Herranz | ||||
Title | Unsupervised Domain Adaptation without Source Data by Casting a BAIT | Type | Miscellaneous | ||
Year | 2020 | Publication | Arxiv | Abbreviated Journal | |
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Abstract | 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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Notes | LAMP; 600.120 | Approved | no | ||
Call Number | Admin @ si @ YWW2020 | Serial | 3539 | ||
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Author | Carola Figueroa Flores; Bogdan Raducanu; David Berga; Joost Van de Weijer | ||||
Title | Hallucinating Saliency Maps for Fine-Grained Image Classification for Limited Data Domains | Type | Conference Article | ||
Year | 2021 | Publication | 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications | Abbreviated Journal | |
Volume | 4 | Issue | Pages | 163-171 | |
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Abstract | 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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Address | Virtual; February 2021 | ||||
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Area | Expedition | Conference | VISAPP | ||
Notes | LAMP | Approved | no | ||
Call Number | Admin @ si @ FRB2021c | Serial | 3540 | ||
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Author | Shiqi Yang; Kai Wang; Luis Herranz; Joost Van de Weijer | ||||
Title | Simple and effective localized attribute representations for zero-shot learning | Type | Miscellaneous | ||
Year | 2020 | Publication | Arxiv | Abbreviated Journal | |
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Abstract | 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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Notes | LAMP; 600.120 | Approved | no | ||
Call Number | Admin @ si @ YWH2020 | Serial | 3542 | ||
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Author | Sudeep Katakol; Basem Elbarashy; Luis Herranz; Joost Van de Weijer; Antonio Lopez | ||||
Title | Distributed Learning and Inference with Compressed Images | Type | Journal Article | ||
Year | 2021 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
Volume | 30 | Issue | Pages | 3069 - 3083 | |
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Abstract | Modern computer vision requires processing large amounts of data, both while training the model and/or during inference, once the model is deployed. Scenarios where images are captured and processed in physically separated locations are increasingly common (e.g. autonomous vehicles, cloud computing). In addition, many devices suffer from limited resources to store or transmit data (e.g. storage space, channel capacity). In these scenarios, lossy image compression plays a crucial role to effectively increase the number of images collected under such constraints. However, lossy compression entails some undesired degradation of the data that may harm the performance of the downstream analysis task at hand, since important semantic information may be lost in the process. Moreover, we may only have compressed images at training time but are able to use original images at inference time, or vice versa, and in such a case, the downstream model suffers from covariate shift. In this paper, we analyze this phenomenon, with a special focus on vision-based perception for autonomous driving as a paradigmatic scenario. We see that loss of semantic information and covariate shift do indeed exist, resulting in a drop in performance that depends on the compression rate. In order to address the problem, we propose dataset restoration, based on image restoration with generative adversarial networks (GANs). Our method is agnostic to both the particular image compression method and the downstream task; and has the advantage of not adding additional cost to the deployed models, which is particularly important in resource-limited devices. The presented experiments focus on semantic segmentation as a challenging use case, cover a broad range of compression rates and diverse datasets, and show how our method is able to significantly alleviate the negative effects of compression on the downstream visual task. | ||||
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Notes | LAMP; ADAS; 600.120; 600.118 | Approved | no | ||
Call Number | Admin @ si @ KEH2021 | Serial | 3543 | ||
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Author | Kai Wang; Luis Herranz; Joost Van de Weijer | ||||
Title | Continual learning in cross-modal retrieval | Type | Conference Article | ||
Year | 2021 | Publication | 2nd CLVISION workshop | Abbreviated Journal | |
Volume | Issue | Pages | 3628-3638 | ||
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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. | ||||
Address | Virtual; June 2021 | ||||
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Area | Expedition | Conference | CVPRW | ||
Notes | LAMP; 600.120; 600.141; 600.147; 601.379 | Approved | no | ||
Call Number | Admin @ si @ WHW2021 | Serial | 3566 | ||
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Author | 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 | ||||
Title | Avalanche: an End-to-End Library for Continual Learning | Type | Conference Article | ||
Year | 2021 | Publication | 34th IEEE Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
Volume | Issue | Pages | 3595-3605 | ||
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Abstract | 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. | ||||
Address | Virtual; June 2021 | ||||
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Area | Expedition | Conference | CVPRW | ||
Notes | LAMP; 600.120 | Approved | no | ||
Call Number | Admin @ si @ LPC2021 | Serial | 3567 | ||
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Author | Mikel Menta; Adriana Romero; Joost Van de Weijer | ||||
Title | Learning to adapt class-specific features across domains for semantic segmentation | Type | Miscellaneous | ||
Year | 2020 | Publication | Arxiv | Abbreviated Journal | |
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Abstract | 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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Notes | LAMP; 600.120 | Approved | no | ||
Call Number | Admin @ si @ MRW2020 | Serial | 3545 | ||
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Author | Giovanni Maria Farinella; Petia Radeva; Jose Braz | ||||
Title | Proceedings of the 15th International Joint Conference on Computer Vision; Imaging and Computer Graphics Theory and Applications | Type | Book Whole | ||
Year | 2020 | Publication | Proceedings of the 15th International Joint Conference on Computer Vision; Imaging and Computer Graphics Theory and Applications; VISIGRAPP 2020 | Abbreviated Journal | |
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Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ FRB2020a | Serial | 3546 | ||
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