| Records |
Author  |
Albert Suso; Pau Riba; Oriol Ramos Terrades; Josep Llados |
| Title |
A Self-supervised Inverse Graphics Approach for Sketch Parametrization |
Type |
Conference Article |
| Year |
2021 |
Publication |
16th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
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| Volume |
12916 |
Issue |
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Pages |
28-42 |
| Keywords |
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| Abstract |
The study of neural generative models of handwritten text and human sketches is a hot topic in the computer vision field. The landmark SketchRNN provided a breakthrough by sequentially generating sketches as a sequence of waypoints, and more recent articles have managed to generate fully vector sketches by coding the strokes as Bézier curves. However, the previous attempts with this approach need them all a ground truth consisting in the sequence of points that make up each stroke, which seriously limits the datasets the model is able to train in. In this work, we present a self-supervised end-to-end inverse graphics approach that learns to embed each image to its best fit of Bézier curves. The self-supervised nature of the training process allows us to train the model in a wider range of datasets, but also to perform better after-training predictions by applying an overfitting process on the input binary image. We report qualitative an quantitative evaluations on the MNIST and the Quick, Draw! datasets. |
| Address |
Lausanne; Suissa; September 2021 |
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LNCS |
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Conference |
ICDAR |
| Notes |
DAG; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ SRR2021 |
Serial |
3675 |
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| Author |
Albert Tatjer; Bhalaji Nagarajan; Ricardo Marques; Petia Radeva |
| Title |
CCLM: Class-Conditional Label Noise Modelling |
Type |
Conference Article |
| Year |
2023 |
Publication |
11th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
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| Volume |
14062 |
Issue |
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Pages |
3-14 |
| Keywords |
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| Abstract |
The performance of deep neural networks highly depends on the quality and volume of the training data. However, cost-effective labelling processes such as crowdsourcing and web crawling often lead to data with noisy (i.e., wrong) labels. Making models robust to this label noise is thus of prime importance. A common approach is using loss distributions to model the label noise. However, the robustness of these methods highly depends on the accuracy of the division of training set into clean and noisy samples. In this work, we dive in this research direction highlighting the existing problem of treating this distribution globally and propose a class-conditional approach to split the clean and noisy samples. We apply our approach to the popular DivideMix algorithm and show how the local treatment fares better with respect to the global treatment of loss distribution. We validate our hypothesis on two popular benchmark datasets and show substantial improvements over the baseline experiments. We further analyze the effectiveness of the proposal using two different metrics – Noise Division Accuracy and Classiness. |
| Address |
Alicante; Spain; June 2023 |
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LNCS |
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Conference |
IbPRIA |
| Notes |
MILAB |
Approved |
no |
| Call Number |
Admin @ si @ TNM2023 |
Serial |
3925 |
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| Author |
Albin Soutif; Antonio Carta; Andrea Cossu; Julio Hurtado; Hamed Hemati; Vincenzo Lomonaco; Joost Van de Weijer |
| Title |
A Comprehensive Empirical Evaluation on Online Continual Learning |
Type |
Conference Article |
| Year |
2023 |
Publication |
Visual Continual Learning (ICCV-W) |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Abstract |
Online continual learning aims to get closer to a live learning experience by learning directly on a stream of data with temporally shifting distribution and by storing a minimum amount of data from that stream. In this empirical evaluation, we evaluate various methods from the literature that tackle online continual learning. More specifically, we focus on the class-incremental setting in the context of image classification, where the learner must learn new classes incrementally from a stream of data. We compare these methods on the Split-CIFAR100 and Split-TinyImagenet benchmarks, and measure their average accuracy, forgetting, stability, and quality of the representations, to evaluate various aspects of the algorithm at the end but also during the whole training period. We find that most methods suffer from stability and underfitting issues. However, the learned representations are comparable to i.i.d. training under the same computational budget. No clear winner emerges from the results and basic experience replay, when properly tuned and implemented, is a very strong baseline. We release our modular and extensible codebase at this https URL based on the avalanche framework to reproduce our results and encourage future research. |
| Address |
Paris; France; October 2023 |
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Conference |
ICCVW |
| Notes |
LAMP |
Approved |
no |
| Call Number |
Admin @ si @ SCC2023 |
Serial |
3938 |
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| Author |
Albin Soutif; Antonio Carta; Joost Van de Weijer |
| Title |
Improving Online Continual Learning Performance and Stability with Temporal Ensembles |
Type |
Conference Article |
| Year |
2023 |
Publication |
2nd Conference on Lifelong Learning Agents |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Abstract |
Neural networks are very effective when trained on large datasets for a large number of iterations. However, when they are trained on non-stationary streams of data and in an online fashion, their performance is reduced (1) by the online setup, which limits the availability of data, (2) due to catastrophic forgetting because of the non-stationary nature of the data. Furthermore, several recent works (Caccia et al., 2022; Lange et al., 2023) arXiv:2205.13452 showed that replay methods used in continual learning suffer from the stability gap, encountered when evaluating the model continually (rather than only on task boundaries). In this article, we study the effect of model ensembling as a way to improve performance and stability in online continual learning. We notice that naively ensembling models coming from a variety of training tasks increases the performance in online continual learning considerably. Starting from this observation, and drawing inspirations from semi-supervised learning ensembling methods, we use a lightweight temporal ensemble that computes the exponential moving average of the weights (EMA) at test time, and show that it can drastically increase the performance and stability when used in combination with several methods from the literature. |
| Address |
Montreal; Canada; August 2023 |
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Conference |
COLLAS |
| Notes |
LAMP |
Approved |
no |
| Call Number |
Admin @ si @ SCW2023 |
Serial |
3922 |
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| Author |
Albin Soutif; Marc Masana; Joost Van de Weijer; Bartlomiej Twardowski |
| Title |
On the importance of cross-task features for class-incremental learning |
Type |
Conference Article |
| Year |
2021 |
Publication |
Theory and Foundation of continual learning workshop of ICML |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Abstract |
In class-incremental learning, an agent with limited resources needs to learn a sequence of classification tasks, forming an ever growing classification problem, with the constraint of not being able to access data from previous tasks. The main difference with task-incremental learning, where a task-ID is available at inference time, is that the learner also needs to perform crosstask discrimination, i.e. distinguish between classes that have not been seen together. Approaches to tackle this problem are numerous and mostly make use of an external memory (buffer) of non-negligible size. In this paper, we ablate the learning of crosstask features and study its influence on the performance of basic replay strategies used for class-IL. We also define a new forgetting measure for class-incremental learning, and see that forgetting is not the principal cause of low performance. Our experimental results show that future algorithms for class-incremental learning should not only prevent forgetting, but also aim to improve the quality of the cross-task features. This is especially important when the number of classes per task is small. |
| Address |
Virtual; July 2021 |
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Conference |
ICMLW |
| Notes |
LAMP |
Approved |
no |
| Call Number |
Admin @ si @ SMW2021 |
Serial |
3588 |
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| Author |
Alejandro Ariza-Casabona; Bartlomiej Twardowski; Tri Kurniawan Wijaya |
| Title |
Exploiting Graph Structured Cross-Domain Representation for Multi-domain Recommendation |
Type |
Conference Article |
| Year |
2023 |
Publication |
European Conference on Information Retrieval – ECIR 2023: Advances in Information Retrieval |
Abbreviated Journal |
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| Volume |
13980 |
Issue |
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Pages |
49–65 |
| Keywords |
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| Abstract |
Multi-domain recommender systems benefit from cross-domain representation learning and positive knowledge transfer. Both can be achieved by introducing a specific modeling of input data (i.e. disjoint history) or trying dedicated training regimes. At the same time, treating domains as separate input sources becomes a limitation as it does not capture the interplay that naturally exists between domains. In this work, we efficiently learn multi-domain representation of sequential users’ interactions using graph neural networks. We use temporal intra- and inter-domain interactions as contextual information for our method called MAGRec (short for Multi-dom Ain Graph-based Recommender). To better capture all relations in a multi-domain setting, we learn two graph-based sequential representations simultaneously: domain-guided for recent user interest, and general for long-term interest. This approach helps to mitigate the negative knowledge transfer problem from multiple domains and improve overall representation. We perform experiments on publicly available datasets in different scenarios where MAGRec consistently outperforms state-of-the-art methods. Furthermore, we provide an ablation study and discuss further extensions of our method. |
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LNCS |
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Conference |
ECIR |
| Notes |
LAMP |
Approved |
no |
| Call Number |
Admin @ si @ ATK2023 |
Serial |
3933 |
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| Author |
Alejandro Cartas; Jordi Luque; Petia Radeva; Carlos Segura; Mariella Dimiccoli |
| Title |
Seeing and Hearing Egocentric Actions: How Much Can We Learn? |
Type |
Conference Article |
| Year |
2019 |
Publication |
IEEE International Conference on Computer Vision Workshops |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
4470-4480 |
| Keywords |
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| Abstract |
Our interaction with the world is an inherently multimodal experience. However, the understanding of human-to-object interactions has historically been addressed focusing on a single modality. In particular, a limited number of works have considered to integrate the visual and audio modalities for this purpose. In this work, we propose a multimodal approach for egocentric action recognition in a kitchen environment that relies on audio and visual information. Our model combines a sparse temporal sampling strategy with a late fusion of audio, spatial, and temporal streams. Experimental results on the EPIC-Kitchens dataset show that multimodal integration leads to better performance than unimodal approaches. In particular, we achieved a 5.18% improvement over the state of the art on verb classification. |
| Address |
Seul; Korea; October 2019 |
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Conference |
ICCVW |
| Notes |
MILAB; no proj |
Approved |
no |
| Call Number |
Admin @ si @ CLR2019b |
Serial |
3385 |
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| Author |
Alejandro Cartas; Mariella Dimiccoli; Petia Radeva |
| Title |
Batch-based activity recognition from egocentric photo-streams |
Type |
Conference Article |
| Year |
2017 |
Publication |
1st International workshop on Egocentric Perception, Interaction and Computing |
Abbreviated Journal |
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Issue |
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Pages |
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| Abstract |
Activity recognition from long unstructured egocentric photo-streams has several applications in assistive technology such as health monitoring and frailty detection, just to name a few. However, one of its main technical challenges is to deal with the low frame rate of wearable photo-cameras, which causes abrupt appearance changes between consecutive frames. In consequence, important discriminatory low-level features from motion such as optical flow cannot be estimated. In this paper, we present a batch-driven approach for training a deep learning architecture that strongly rely on Long short-term units to tackle this problem. We propose two different implementations of the same approach that process a photo-stream sequence using batches of fixed size with the goal of capturing the temporal evolution of high-level features. The main difference between these implementations is that one explicitly models consecutive batches by overlapping them. Experimental results over a public dataset acquired by three users demonstrate the validity of the proposed architectures to exploit the temporal evolution of convolutional features over time without relying on event boundaries. |
| Address |
Venice; Italy; October 2017; |
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Conference |
ICCV - EPIC |
| Notes |
MILAB; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ CDR2017 |
Serial |
3023 |
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| Author |
Alejandro Cartas; Petia Radeva; Mariella Dimiccoli |
| Title |
Modeling long-term interactions to enhance action recognition |
Type |
Conference Article |
| Year |
2021 |
Publication |
25th International Conference on Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
10351-10358 |
| Keywords |
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| Abstract |
In this paper, we propose a new approach to under-stand actions in egocentric videos that exploits the semantics of object interactions at both frame and temporal levels. At the frame level, we use a region-based approach that takes as input a primary region roughly corresponding to the user hands and a set of secondary regions potentially corresponding to the interacting objects and calculates the action score through a CNN formulation. This information is then fed to a Hierarchical LongShort-Term Memory Network (HLSTM) that captures temporal dependencies between actions within and across shots. Ablation studies thoroughly validate the proposed approach, showing in particular that both levels of the HLSTM architecture contribute to performance improvement. Furthermore, quantitative comparisons show that the proposed approach outperforms the state-of-the-art in terms of action recognition on standard benchmarks,without relying on motion information |
| Address |
January 2021 |
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Conference |
ICPR |
| Notes |
MILAB; |
Approved |
no |
| Call Number |
Admin @ si @ CRD2021 |
Serial |
3626 |
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| Author |
Alejandro Gonzalez Alzate; Gabriel Villalonga; German Ros; David Vazquez; Antonio Lopez |
| Title |
3D-Guided Multiscale Sliding Window for Pedestrian Detection |
Type |
Conference Article |
| Year |
2015 |
Publication |
Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 |
Abbreviated Journal |
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| Volume |
9117 |
Issue |
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Pages |
560-568 |
| Keywords |
Pedestrian Detection |
| Abstract |
The most relevant modules of a pedestrian detector are the candidate generation and the candidate classification. The former aims at presenting image windows to the latter so that they are classified as containing a pedestrian or not. Much attention has being paid to the classification module, while candidate generation has mainly relied on (multiscale) sliding window pyramid. However, candidate generation is critical for achieving real-time. In this paper we assume a context of autonomous driving based on stereo vision. Accordingly, we evaluate the effect of taking into account the 3D information (derived from the stereo) in order to prune the hundred of thousands windows per image generated by classical pyramidal sliding window. For our study we use a multimodal (RGB, disparity) and multi-descriptor (HOG, LBP, HOG+LBP) holistic ensemble based on linear SVM. Evaluation on data from the challenging KITTI benchmark suite shows the effectiveness of using 3D information to dramatically reduce the number of candidate windows, even improving the overall pedestrian detection accuracy. |
| Address |
Santiago de Compostela; España; June 2015 |
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ACDC |
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Conference |
IbPRIA |
| Notes |
ADAS; 600.076; 600.057; 600.054 |
Approved |
no |
| Call Number |
ADAS @ adas @ GVR2015 |
Serial |
2585 |
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| Author |
Alejandro Gonzalez Alzate; Gabriel Villalonga; Jiaolong Xu; David Vazquez; Jaume Amores; Antonio Lopez |
| Title |
Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection |
Type |
Conference Article |
| Year |
2015 |
Publication |
IEEE Intelligent Vehicles Symposium IV2015 |
Abbreviated Journal |
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Issue |
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Pages |
356-361 |
| Keywords |
Pedestrian Detection |
| Abstract |
Despite recent significant advances, pedestrian detection continues to be an extremely challenging problem in real scenarios. In order to develop a detector that successfully operates under these conditions, it becomes critical to leverage upon multiple cues, multiple imaging modalities and a strong multi-view classifier that accounts for different pedestrian views and poses. In this paper we provide an extensive evaluation that gives insight into how each of these aspects (multi-cue, multimodality and strong multi-view classifier) affect performance both individually and when integrated together. In the multimodality component we explore the fusion of RGB and depth maps obtained by high-definition LIDAR, a type of modality that is only recently starting to receive attention. As our analysis reveals, although all the aforementioned aspects significantly help in improving the performance, the fusion of visible spectrum and depth information allows to boost the accuracy by a much larger margin. The resulting detector not only ranks among the top best performers in the challenging KITTI benchmark, but it is built upon very simple blocks that are easy to implement and computationally efficient. These simple blocks can be easily replaced with more sophisticated ones recently proposed, such as the use of convolutional neural networks for feature representation, to further improve the accuracy. |
| Address |
Seoul; Corea; June 2015 |
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Conference |
IV |
| Notes |
ADAS; 600.076; 600.057; 600.054 |
Approved |
no |
| Call Number |
ADAS @ adas @ GVX2015 |
Serial |
2625 |
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| Author |
Alejandro Gonzalez Alzate; Sebastian Ramos; David Vazquez; Antonio Lopez; Jaume Amores |
| Title |
Spatiotemporal Stacked Sequential Learning for Pedestrian Detection |
Type |
Conference Article |
| Year |
2015 |
Publication |
Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 |
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Issue |
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Pages |
3-12 |
| Keywords |
SSL; Pedestrian Detection |
| Abstract |
Pedestrian classifiers decide which image windows contain a pedestrian. In practice, such classifiers provide a relatively high response at neighbor windows overlapping a pedestrian, while the responses around potential false positives are expected to be lower. An analogous reasoning applies for image sequences. If there is a pedestrian located within a frame, the same pedestrian is expected to appear close to the same location in neighbor frames. Therefore, such a location has chances of receiving high classification scores during several frames, while false positives are expected to be more spurious. In this paper we propose to exploit such correlations for improving the accuracy of base pedestrian classifiers. In particular, we propose to use two-stage classifiers which not only rely on the image descriptors required by the base classifiers but also on the response of such base classifiers in a given spatiotemporal neighborhood. More specifically, we train pedestrian classifiers using a stacked sequential learning (SSL) paradigm. We use a new pedestrian dataset we have acquired from a car to evaluate our proposal at different frame rates. We also test on a well known dataset: Caltech. The obtained results show that our SSL proposal boosts detection accuracy significantly with a minimal impact on the computational cost. Interestingly, SSL improves more the accuracy at the most dangerous situations, i.e. when a pedestrian is close to the camera. |
| Address |
Santiago de Compostela; España; June 2015 |
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ACDC |
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IbPRIA |
| Notes |
ADAS; 600.057; 600.054; 600.076 |
Approved |
no |
| Call Number |
GRV2015; ADAS @ adas @ GRV2015 |
Serial |
2454 |
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| Author |
Alejandro Tabas; Emili Balaguer-Ballester; Laura Igual |
| Title |
Spatial Discriminant ICA for RS-fMRI characterisation |
Type |
Conference Article |
| Year |
2014 |
Publication |
4th International Workshop on Pattern Recognition in Neuroimaging |
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Pages |
1-4 |
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| Abstract |
Resting-State fMRI (RS-fMRI) is a brain imaging technique useful for exploring functional connectivity. A major point of interest in RS-fMRI analysis is to isolate connectivity patterns characterising disorders such as for instance ADHD. Such characterisation is usually performed in two steps: first, all connectivity patterns in the data are extracted by means of Independent Component Analysis (ICA); second, standard statistical tests are performed over the extracted patterns to find differences between control and clinical groups. In this work we introduce a novel, single-step, approach for this problem termed Spatial Discriminant ICA. The algorithm can efficiently isolate networks of functional connectivity characterising a clinical group by combining ICA and a new variant of the Fisher’s Linear Discriminant also introduced in this work. As the characterisation is carried out in a single step, it potentially provides for a richer characterisation of inter-class differences. The algorithm is tested using synthetic and real fMRI data, showing promising results in both experiments. |
| Address |
Tübingen; June 2014 |
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978-1-4799-4150-6 |
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PRNI |
| Notes |
OR;MILAB |
Approved |
no |
| Call Number |
Admin @ si @ TBI2014 |
Serial |
2493 |
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| Author |
Aleksandr Setkov; Fabio Martinez Carillo; Michele Gouiffes; Christian Jacquemin; Maria Vanrell; Ramon Baldrich |
| Title |
DAcImPro: A Novel Database of Acquired Image Projections and Its Application to Object Recognition |
Type |
Conference Article |
| Year |
2015 |
Publication |
Advances in Visual Computing. Proceedings of 11th International Symposium, ISVC 2015 Part II |
Abbreviated Journal |
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9475 |
Issue |
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Pages |
463-473 |
| Keywords |
Projector-camera systems; Feature descriptors; Object recognition |
| Abstract |
Projector-camera systems are designed to improve the projection quality by comparing original images with their captured projections, which is usually complicated due to high photometric and geometric variations. Many research works address this problem using their own test data which makes it extremely difficult to compare different proposals. This paper has two main contributions. Firstly, we introduce a new database of acquired image projections (DAcImPro) that, covering photometric and geometric conditions and providing data for ground-truth computation, can serve to evaluate different algorithms in projector-camera systems. Secondly, a new object recognition scenario from acquired projections is presented, which could be of a great interest in such domains, as home video projections and public presentations. We show that the task is more challenging than the classical recognition problem and thus requires additional pre-processing, such as color compensation or projection area selection. |
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Springer International Publishing |
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LNCS |
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0302-9743 |
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978-3-319-27862-9 |
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ISVC |
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CIC |
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no |
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Admin @ si @ SMG2015 |
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2736 |
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| Author |
Alex Falcon; Swathikiran Sudhakaran; Giuseppe Serra; Sergio Escalera; Oswald Lanz |
| Title |
Relevance-based Margin for Contrastively-trained Video Retrieval Models |
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Conference Article |
| Year |
2022 |
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ICMR '22: Proceedings of the 2022 International Conference on Multimedia Retrieval |
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146-157 |
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| Abstract |
Video retrieval using natural language queries has attracted increasing interest due to its relevance in real-world applications, from intelligent access in private media galleries to web-scale video search. Learning the cross-similarity of video and text in a joint embedding space is the dominant approach. To do so, a contrastive loss is usually employed because it organizes the embedding space by putting similar items close and dissimilar items far. This framework leads to competitive recall rates, as they solely focus on the rank of the groundtruth items. Yet, assessing the quality of the ranking list is of utmost importance when considering intelligent retrieval systems, since multiple items may share similar semantics, hence a high relevance. Moreover, the aforementioned framework uses a fixed margin to separate similar and dissimilar items, treating all non-groundtruth items as equally irrelevant. In this paper we propose to use a variable margin: we argue that varying the margin used during training based on how much relevant an item is to a given query, i.e. a relevance-based margin, easily improves the quality of the ranking lists measured through nDCG and mAP. We demonstrate the advantages of our technique using different models on EPIC-Kitchens-100 and YouCook2. We show that even if we carefully tuned the fixed margin, our technique (which does not have the margin as a hyper-parameter) would still achieve better performance. Finally, extensive ablation studies and qualitative analysis support the robustness of our approach. Code will be released at \urlhttps://github.com/aranciokov/RelevanceMargin-ICMR22. |
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Newwark, NJ, USA, 27 June 2022 |
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ICMR |
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HuPBA; no menciona |
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no |
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Admin @ si @ FSS2022 |
Serial |
3808 |
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