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Author |
Mert Kilickaya; Joost van de Weijer; Yuki M. Asano |
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Title |
Towards Label-Efficient Incremental Learning: A Survey |
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Miscellaneous |
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2023 |
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Arxiv |
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The current dominant paradigm when building a machine learning model is to iterate over a dataset over and over until convergence. Such an approach is non-incremental, as it assumes access to all images of all categories at once. However, for many applications, non-incremental learning is unrealistic. To that end, researchers study incremental learning, where a learner is required to adapt to an incoming stream of data with a varying distribution while preventing forgetting of past knowledge. Significant progress has been made, however, the vast majority of works focus on the fully supervised setting, making these algorithms label-hungry thus limiting their real-life deployment. To that end, in this paper, we make the first attempt to survey recently growing interest in label-efficient incremental learning. We identify three subdivisions, namely semi-, few-shot- and self-supervised learning to reduce labeling efforts. Finally, we identify novel directions that can further enhance label-efficiency and improve incremental learning scalability. Project website: this https URL. |
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LAMP |
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no |
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Admin @ si @ KWA2023 |
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3994 |
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Author |
Jorge Bernal; Fernando Vilariño; F. Javier Sanchez |
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Title |
Towards Intelligent Systems for Colonoscopy |
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Book Chapter |
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2011 |
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Colonoscopy |
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1 |
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257-282 |
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In this chapter we present tools that can be used to build intelligent systems for colonoscopy.
The idea is, by using methods based on computer vision and artificial intelligence, add significant value to the colonoscopy procedure. Intelligent systems are being used to assist in other medical interventions |
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Intech |
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Paul Miskovitz |
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978-953-307-568-6 |
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800 |
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MV;SIAI |
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no |
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IAM @ iam @ BVS2011 |
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1697 |
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Author |
Yael Tudela; Ana Garcia Rodriguez; Gloria Fernandez Esparrach; Jorge Bernal |
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Title |
Towards Fine-Grained Polyp Segmentation and Classification |
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Conference Article |
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Year |
2023 |
Publication |
Workshop on Clinical Image-Based Procedures |
Abbreviated Journal |
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14242 |
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32-42 |
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Medical image segmentation; Colorectal Cancer; Vision Transformer; Classification |
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Abstract |
Colorectal cancer is one of the main causes of cancer death worldwide. Colonoscopy is the gold standard screening tool as it allows lesion detection and removal during the same procedure. During the last decades, several efforts have been made to develop CAD systems to assist clinicians in lesion detection and classification. Regarding the latter, and in order to be used in the exploration room as part of resect and discard or leave-in-situ strategies, these systems must identify correctly all different lesion types. This is a challenging task, as the data used to train these systems presents great inter-class similarity, high class imbalance, and low representation of clinically relevant histology classes such as serrated sessile adenomas.
In this paper, a new polyp segmentation and classification method, Swin-Expand, is introduced. Based on Swin-Transformer, it uses a simple and lightweight decoder. The performance of this method has been assessed on a novel dataset, comprising 1126 high-definition images representing the three main histological classes. Results show a clear improvement in both segmentation and classification performance, also achieving competitive results when tested in public datasets. These results confirm that both the method and the data are important to obtain more accurate polyp representations. |
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Vancouver; October 2023 |
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MICCAIW |
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ISE |
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no |
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Admin @ si @ TGF2023 |
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3837 |
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Author |
Ruben Ballester; Xavier Arnal Clemente; Carles Casacuberta; Meysam Madadi; Ciprian Corneanu |
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Title |
Towards explaining the generalization gap in neural networks using topological data analysis |
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Miscellaneous |
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Year |
2022 |
Publication |
Arxiv |
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Understanding how neural networks generalize on unseen data is crucial for designing more robust and reliable models. In this paper, we study the generalization gap of neural networks using methods from topological data analysis. For this purpose, we compute homological persistence diagrams of weighted graphs constructed from neuron activation correlations after a training phase, aiming to capture patterns that are linked to the generalization capacity of the network. We compare the usefulness of different numerical summaries from persistence diagrams and show that a combination of some of them can accurately predict and partially explain the generalization gap without the need of a test set. Evaluation on two computer vision recognition tasks (CIFAR10 and SVHN) shows competitive generalization gap prediction when compared against state-of-the-art methods. |
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HUPBA; no menciona |
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no |
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Call Number |
Admin @ si @ BAC2022 |
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3821 |
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Author |
Francesco Pelosin; Saurav Jha; Andrea Torsello; Bogdan Raducanu; Joost Van de Weijer |
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Title |
Towards exemplar-free continual learning in vision transformers: an account of attention, functional and weight regularization |
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Conference Article |
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Year |
2022 |
Publication |
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
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Learning systems; Weight measurement; Image recognition; Surgery; Benchmark testing; Transformers; Stability analysis |
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In this paper, we investigate the continual learning of Vision Transformers (ViT) for the challenging exemplar-free scenario, with special focus on how to efficiently distill the knowledge of its crucial self-attention mechanism (SAM). Our work takes an initial step towards a surgical investigation of SAM for designing coherent continual learning methods in ViTs. We first carry out an evaluation of established continual learning regularization techniques. We then examine the effect of regularization when applied to two key enablers of SAM: (a) the contextualized embedding layers, for their ability to capture well-scaled representations with respect to the values, and (b) the prescaled attention maps, for carrying value-independent global contextual information. We depict the perks of each distilling strategy on two image recognition benchmarks (CIFAR100 and ImageNet-32) – while (a) leads to a better overall accuracy, (b) helps enhance the rigidity by maintaining competitive performances. Furthermore, we identify the limitation imposed by the symmetric nature of regularization losses. To alleviate this, we propose an asymmetric variant and apply it to the pooled output distillation (POD) loss adapted for ViTs. Our experiments confirm that introducing asymmetry to POD boosts its plasticity while retaining stability across (a) and (b). Moreover, we acknowledge low forgetting measures for all the compared methods, indicating that ViTs might be naturally inclined continual learners. 1 |
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New Orleans; USA; June 2022 |
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CVPRW |
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LAMP; 600.147 |
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no |
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Call Number |
Admin @ si @ PJT2022 |
Serial |
3784 |
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Author |
Lichao Zhang |
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Title |
Towards end-to-end Networks for Visual Tracking in RGB and TIR Videos |
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Book Whole |
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Year |
2019 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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In the current work, we identify several problems of current tracking systems. The lack of large-scale labeled datasets hampers the usage of deep learning, especially end-to-end training, for tracking in TIR images. Therefore, many methods for tracking on TIR data are still based on hand-crafted features. This situation also happens in multi-modal tracking, e.g. RGB-T tracking. Another reason, which hampers the development of RGB-T tracking, is that there exists little research on the fusion mechanisms for combining information from RGB and TIR modalities. One of the crucial components of most trackers is the update module. For the currently existing end-to-end tracking architecture, e.g, Siamese trackers, the online model update is still not taken into consideration at the training stage. They use no-update or a linear update strategy during the inference stage. While such a hand-crafted approach to updating has led to improved results, its simplicity limits the potential gain likely to be obtained by learning to update.
To address the data-scarcity for TIR and RGB-T tracking, we use image-to-image translation to generate a large-scale synthetic TIR dataset. This dataset allows us to perform end-to-end training for TIR tracking. Furthermore, we investigate several fusion mechanisms for RGB-T tracking. The multi-modal trackers are also trained in an end-to-end manner on the synthetic data. To improve the standard online update, we pose the updating step as an optimization problem which can be solved by training a neural network. Our approach thereby reduces the hand-crafted components in the tracking pipeline and sets a further step in the direction of a complete end-to-end trained tracking network which also considers updating during optimization. |
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November 2019 |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Editor |
Joost Van de Weijer;Abel Gonzalez;Fahad Shahbaz Khan |
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978-84-1210011-1-9 |
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Notes |
LAMP; 600.141; 600.120 |
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no |
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Call Number |
Admin @ si @ Zha2019 |
Serial |
3393 |
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Author |
Estefania Talavera; Petia Radeva; Nicolai Petkov |
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Title |
Towards Emotion Retrieval in Egocentric PhotoStream |
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Miscellaneous |
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Year |
2019 |
Publication |
Arxiv |
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Abstract |
CoRR abs/1905.04107
The availability and use of egocentric data are rapidly increasing due to the growing use of wearable cameras. Our aim is to study the effect (positive, neutral or negative) of egocentric images or events on an observer. Given egocentric photostreams capturing the wearer's days, we propose a method that aims to assign sentiment to events extracted from egocentric photostreams. Such moments can be candidates to retrieve according to their possibility of representing a positive experience for the camera's wearer. The proposed approach obtained a classification accuracy of 75% on the test set, with a deviation of 8%. Our model makes a step forward opening the door to sentiment recognition in egocentric photostreams. |
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MILAB; no proj |
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no |
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Admin @ si @ TRP2019 |
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3381 |
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Author |
Estefania Talavera; Alexandre Cola; Nicolai Petkov; Petia Radeva |
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Title |
Towards Egocentric Person Re-identification and Social Pattern Analysis. |
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Book Chapter |
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Year |
2019 |
Publication |
Frontiers in Artificial Intelligence and Applications |
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310 |
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203 - 211 |
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CoRR abs/1905.04073
Wearable cameras capture a first-person view of the daily activities of the camera wearer, offering a visual diary of the user behaviour. Detection of the appearance of people the camera user interacts with for social interactions analysis is of high interest. Generally speaking, social events, lifestyle and health are highly correlated, but there is a lack of tools to monitor and analyse them. We consider that egocentric vision provides a tool to obtain information and understand users social interactions. We propose a model that enables us to evaluate and visualize social traits obtained by analysing social interactions appearance within egocentric photostreams. Given sets of egocentric images, we detect the appearance of faces within the days of the camera wearer, and rely on clustering algorithms to group their feature descriptors in order to re-identify persons. Recurrence of detected faces within photostreams allows us to shape an idea of the social pattern of behaviour of the user. We validated our model over several weeks recorded by different camera wearers. Our findings indicate that social profiles are potentially useful for social behaviour interpretation. |
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MILAB; no proj |
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no |
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Call Number |
Admin @ si @ TCP2019 |
Serial |
3377 |
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Author |
Parichehr Behjati Ardakani |
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Title |
Towards Efficient and Robust Convolutional Neural Networks for Single Image Super-Resolution |
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Book Whole |
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Year |
2022 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Single image super-resolution (SISR) is an important task in image processing which aims to enhance the resolution of imaging systems. Recently, SISR has witnessed great strides with the rapid development of deep learning. Recent advances in SISR are mostly devoted to designing deeper and wider networks to enhance their representation learning capacity. However, as the depth of networks increases, deep learning-based methods are faced with the challenge of computational complexity in practice. Moreover, most existing methods rarely leverage the intermediate features and also do not discriminate the computation of features by their frequencial components, thereby achieving relatively low performance. Aside from the aforementioned problems, another desired ability is to upsample images to arbitrary scales using a single model. Most current SISR methods train a dedicated model for each target resolution, losing generality and increasing memory requirements. In this thesis, we address the aforementioned issues and propose solutions to them: i) We present a novel frequency-based enhancement block which treats different frequencies in a heterogeneous way and also models inter-channel dependencies, which consequently enrich the output feature. Thus it helps the network generate more discriminative representations by explicitly recovering finer details. ii) We introduce OverNet which contains two main parts: a lightweight feature extractor that follows a novel recursive framework of skip and dense connections to reduce low-level feature degradation, and an overscaling module that generates an accurate SR image by internally constructing an overscaled intermediate representation of the output features. Then, to solve the problem of reconstruction at arbitrary scale factors, we introduce a novel multi-scale loss, that allows the simultaneous training of all scale factors using a single model. iii) We propose a directional variance attention network which leverages a novel attention mechanism to enhance features in different channels and spatial regions. Moreover, we introduce a novel procedure for using attention mechanisms together with residual blocks to facilitate the preservation of finer details. Finally, we demonstrate that our approaches achieve considerably better performance than previous state-of-the-art methods, in terms of both quantitative and visual quality. |
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April, 2022 |
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Ph.D. thesis |
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Jordi Gonzalez;Xavier Roca;Pau Rodriguez |
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978-84-124793-1-7 |
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ISE |
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no |
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Call Number |
Admin @ si @ Beh2022 |
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3713 |
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Author |
Alina Matei; Andreea Glavan; Petia Radeva; Estefania Talavera |
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Title |
Towards Eating Habits Discovery in Egocentric Photo-Streams |
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Journal Article |
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2021 |
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IEEE Access |
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ACCESS |
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9 |
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17495-17506 |
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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 behavioral 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; no proj |
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no |
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Admin @ si @ MGR2021 |
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3637 |
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Author |
Josep M. Gonfaus |
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Title |
Towards Deep Image Understanding: From pixels to semantics |
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Book Whole |
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2012 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Understanding the content of the images is one of the greatest challenges of computer vision. Recognition of objects appearing in images, identifying and interpreting their actions are the main purposes of Image Understanding. This thesis seeks to identify what is present in a picture by categorizing and locating all the objects in the scene.
Images are composed by pixels, and one possibility consists of assigning to each pixel an object category, which is commonly known as semantic segmentation. By incorporating information as a contextual cue, we are able to resolve the ambiguity within categories at the pixel-level. We propose three levels of scale in order to resolve such ambiguity.
Another possibility to represent the objects is the object detection task. In this case, the aim is to recognize and localize the whole object by accurately placing a bounding box around it. We present two new approaches. The first one is focused on improving the object representation of deformable part models with the concept of factorized appearances. The second approach addresses the issue of reducing the computational cost for multi-class recognition. The results given have been validated on several commonly used datasets, reaching international recognition and state-of-the-art within the field |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Jordi Gonzalez;Theo Gevers |
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ISE |
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no |
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Admin @ si @ Gon2012 |
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2208 |
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Permanent link to this record |
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Author |
Jorge Bernal; F. Javier Sanchez; Fernando Vilariño |
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Title |
Towards Automatic Polyp Detection with a Polyp Appearance Model |
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Journal Article |
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Year |
2012 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
45 |
Issue |
9 |
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3166-3182 |
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Colonoscopy,PolypDetection,RegionSegmentation,SA-DOVA descriptot |
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Abstract |
This work aims at the automatic polyp detection by using a model of polyp appearance in the context of the analysis of colonoscopy videos. Our method consists of three stages: region segmentation, region description and region classification. The performance of our region segmentation method guarantees that if a polyp is present in the image, it will be exclusively and totally contained in a single region. The output of the algorithm also defines which regions can be considered as non-informative. We define as our region descriptor the novel Sector Accumulation-Depth of Valleys Accumulation (SA-DOVA), which provides a necessary but not sufficient condition for the polyp presence. Finally, we classify our segmented regions according to the maximal values of the SA-DOVA descriptor. Our preliminary classification results are promising, especially when classifying those parts of the image that do not contain a polyp inside. |
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Elsevier |
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0031-3203 |
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800 |
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IbPRIA |
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MV;SIAI |
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no |
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Admin @ si @ BSV2012; IAM @ iam |
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1997 |
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Author |
Mirko Arnold; Anarta Ghosh; Glen Doherty; Hugh Mulcahy; Stephen Patchett; Gerard Lacey |
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Title |
Towards Automatic Direct Observation of Procedure and Skill (DOPS) in Colonoscopy |
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Conference Article |
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2013 |
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Proceedings of the International Conference on Computer Vision Theory and Applications |
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48-53 |
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800 |
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VISIGRAPP |
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MV |
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no |
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fernando @ fernando @ |
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2427 |
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Author |
Pau Cano; Debora Gil; Eva Musulen |
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Title |
Towards automatic detection of helicobacter pylori in histological samples of gastric tissue |
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Conference Article |
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2023 |
Publication |
IEEE International Symposium on Biomedical Imaging |
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Cartagena de Indias; Colombia; April 2023 |
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ISBI |
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IAM |
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no |
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Admin @ si @ CGM2023 |
Serial |
3953 |
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Author |
Naila Murray; Sandra Skaff; Luca Marchesotti; Florent Perronnin |
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Title |
Towards Automatic Concept Transfer |
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Conference Article |
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Year |
2011 |
Publication |
Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Non-Photorealistic Animation and Rendering |
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167.176 |
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Keywords |
chromatic modeling, color concepts, color transfer, concept transfer |
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Abstract |
This paper introduces a novel approach to automatic concept transfer; examples of concepts are “romantic”, “earthy”, and “luscious”. The approach modifies the color content of an input image given only a concept specified by a user in natural language, thereby requiring minimal user input. This approach is particularly useful for users who are aware of the message they wish to convey in the transferred image while being unsure of the color combination needed to achieve the corresponding transfer. The user may adjust the intensity level of the concept transfer to his/her liking with a single parameter. The proposed approach uses a convex clustering algorithm, with a novel pruning mechanism, to automatically set the complexity of models of chromatic content. It also uses the Earth-Mover's Distance to compute a mapping between the models of the input image and the target chromatic concept. Results show that our approach yields transferred images which effectively represent concepts, as confirmed by a user study. |
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ACM Press |
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978-1-4503-0907-3 |
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NPAR |
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CIC |
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no |
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Call Number |
Admin @ si @ MSM2011 |
Serial |
1866 |
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