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Filip Szatkowski; Mateusz Pyla; Marcin Przewięzlikowski; Sebastian Cygert; Bartłomiej Twardowski; Tomasz Trzcinski |
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Title |
Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-Free Continual Learning |
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Conference Article |
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2023 |
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Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops |
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3512-3517 |
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In this work, we investigate exemplar-free class incremental learning (CIL) with knowledge distillation (KD) as a regularization strategy, aiming to prevent forgetting. KD-based methods are successfully used in CIL, but they often struggle to regularize the model without access to exemplars of the training data from previous tasks. Our analysis reveals that this issue originates from substantial representation shifts in the teacher network when dealing with out-of-distribution data. This causes large errors in the KD loss component, leading to performance degradation in CIL. Inspired by recent test-time adaptation methods, we introduce Teacher Adaptation (TA), a method that concurrently updates the teacher and the main model during incremental training. Our method seamlessly integrates with KD-based CIL approaches and allows for consistent enhancement of their performance across multiple exemplar-free CIL benchmarks. |
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Paris; France; October 2023 |
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ICCVW |
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LAMP |
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Admin @ si @ |
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3944 |
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Fei Wang; Kai Wang; Joost Van de Weijer |
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Title |
ScrollNet: DynamicWeight Importance for Continual Learning |
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2023 |
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Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops |
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3345-3355 |
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The principle underlying most existing continual learning (CL) methods is to prioritize stability by penalizing changes in parameters crucial to old tasks, while allowing for plasticity in other parameters. The importance of weights for each task can be determined either explicitly through learning a task-specific mask during training (e.g., parameter isolation-based approaches) or implicitly by introducing a regularization term (e.g., regularization-based approaches). However, all these methods assume that the importance of weights for each task is unknown prior to data exposure. In this paper, we propose ScrollNet as a scrolling neural network for continual learning. ScrollNet can be seen as a dynamic network that assigns the ranking of weight importance for each task before data exposure, thus achieving a more favorable stability-plasticity tradeoff during sequential task learning by reassigning this ranking for different tasks. Additionally, we demonstrate that ScrollNet can be combined with various CL methods, including regularization-based and replay-based approaches. Experimental results on CIFAR100 and TinyImagenet datasets show the effectiveness of our proposed method. |
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Paris; France; October 2023 |
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Admin @ si @ WWW2023 |
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3945 |
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Soumya Jahagirdar; Minesh Mathew; Dimosthenis Karatzas; CV Jawahar |
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Title |
Understanding Video Scenes Through Text: Insights from Text-Based Video Question Answering |
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2023 |
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Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops |
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Researchers have extensively studied the field of vision and language, discovering that both visual and textual content is crucial for understanding scenes effectively. Particularly, comprehending text in videos holds great significance, requiring both scene text understanding and temporal reasoning. This paper focuses on exploring two recently introduced datasets, NewsVideoQA and M4-ViteVQA, which aim to address video question answering based on textual content. The NewsVideoQA dataset contains question-answer pairs related to the text in news videos, while M4- ViteVQA comprises question-answer pairs from diverse categories like vlogging, traveling, and shopping. We provide an analysis of the formulation of these datasets on various levels, exploring the degree of visual understanding and multi-frame comprehension required for answering the questions. Additionally, the study includes experimentation with BERT-QA, a text-only model, which demonstrates comparable performance to the original methods on both datasets, indicating the shortcomings in the formulation of these datasets. Furthermore, we also look into the domain adaptation aspect by examining the effectiveness of training on M4-ViteVQA and evaluating on NewsVideoQA and vice-versa, thereby shedding light on the challenges and potential benefits of out-of-domain training. |
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Paris; France; October 2023 |
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DAG |
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Admin @ si @ JMK2023 |
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3946 |
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Dawid Rymarczyk; Joost van de Weijer; Bartosz Zielinski; Bartlomiej Twardowski |
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ICICLE: Interpretable Class Incremental Continual Learning. Dawid Rymarczyk |
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2023 |
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20th IEEE International Conference on Computer Vision |
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1887-1898 |
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Continual learning enables incremental learning of new tasks without forgetting those previously learned, resulting in positive knowledge transfer that can enhance performance on both new and old tasks. However, continual learning poses new challenges for interpretability, as the rationale behind model predictions may change over time, leading to interpretability concept drift. We address this problem by proposing Interpretable Class-InCremental LEarning (ICICLE), an exemplar-free approach that adopts a prototypical part-based approach. It consists of three crucial novelties: interpretability regularization that distills previously learned concepts while preserving user-friendly positive reasoning; proximity-based prototype initialization strategy dedicated to the fine-grained setting; and task-recency bias compensation devoted to prototypical parts. Our experimental results demonstrate that ICICLE reduces the interpretability concept drift and outperforms the existing exemplar-free methods of common class-incremental learning when applied to concept-based models. |
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Paris; France; October 2023 |
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LAMP |
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Admin @ si @ RWZ2023 |
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3947 |
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Jordy Van Landeghem; Ruben Tito; Lukasz Borchmann; Michal Pietruszka; Pawel Joziak; Rafal Powalski; Dawid Jurkiewicz; Mickael Coustaty; Bertrand Anckaert; Ernest Valveny; Matthew Blaschko; Sien Moens; Tomasz Stanislawek |
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Title |
Document Understanding Dataset and Evaluation (DUDE) |
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Conference Article |
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2023 |
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20th IEEE International Conference on Computer Vision |
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19528-19540 |
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We call on the Document AI (DocAI) community to re-evaluate current methodologies and embrace the challenge of creating more practically-oriented benchmarks. Document Understanding Dataset and Evaluation (DUDE) seeks to remediate the halted research progress in understanding visually-rich documents (VRDs). We present a new dataset with novelties related to types of questions, answers, and document layouts based on multi-industry, multi-domain, and multi-page VRDs of various origins and dates. Moreover, we are pushing the boundaries of current methods by creating multi-task and multi-domain evaluation setups that more accurately simulate real-world situations where powerful generalization and adaptation under low-resource settings are desired. DUDE aims to set a new standard as a more practical, long-standing benchmark for the community, and we hope that it will lead to future extensions and contributions that address real-world challenges. Finally, our work illustrates the importance of finding more efficient ways to model language, images, and layout in DocAI. |
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Paris; France; October 2023 |
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ICCV |
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DAG |
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no |
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Admin @ si @ LTB2023 |
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3948 |
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Author |
Yuyang Liu; Yang Cong; Dipam Goswami; Xialei Liu; Joost Van de Weijer |
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Title |
Augmented Box Replay: Overcoming Foreground Shift for Incremental Object Detection |
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Conference Article |
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Year |
2023 |
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20th IEEE International Conference on Computer Vision |
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11367-11377 |
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In incremental learning, replaying stored samples from previous tasks together with current task samples is one of the most efficient approaches to address catastrophic forgetting. However, unlike incremental classification, image replay has not been successfully applied to incremental object detection (IOD). In this paper, we identify the overlooked problem of foreground shift as the main reason for this. Foreground shift only occurs when replaying images of previous tasks and refers to the fact that their background might contain foreground objects of the current task. To overcome this problem, a novel and efficient Augmented Box Replay (ABR) method is developed that only stores and replays foreground objects and thereby circumvents the foreground shift problem. In addition, we propose an innovative Attentive RoI Distillation loss that uses spatial attention from region-of-interest (RoI) features to constrain current model to focus on the most important information from old model. ABR significantly reduces forgetting of previous classes while maintaining high plasticity in current classes. Moreover, it considerably reduces the storage requirements when compared to standard image replay. Comprehensive experiments on Pascal-VOC and COCO datasets support the state-of-the-art performance of our model. |
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Paris; France; October 2023 |
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ICCV |
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LAMP |
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no |
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Admin @ si @ LCG2023 |
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3949 |
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Author |
Eduardo Aguilar; Bogdan Raducanu; Petia Radeva; Joost Van de Weijer |
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Title |
Continual Evidential Deep Learning for Out-of-Distribution Detection |
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Conference Article |
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2023 |
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Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops |
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3444-3454 |
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Uncertainty-based deep learning models have attracted a great deal of interest for their ability to provide accurate and reliable predictions. Evidential deep learning stands out achieving remarkable performance in detecting out-ofdistribution (OOD) data with a single deterministic neural network. Motivated by this fact, in this paper we propose the integration of an evidential deep learning method into a continual learning framework in order to perform simultaneously incremental object classification and OOD detection. Moreover, we analyze the ability of vacuity and dissonance to differentiate between in-distribution data belonging to old classes and OOD data. The proposed method 1, called CEDL, is evaluated on CIFAR-100 considering two settings consisting of 5 and 10 tasks, respectively. From the obtained results, we could appreciate that the proposed method, in addition to provide comparable results in object classification with respect to the baseline, largely outperforms OOD detection compared to several posthoc methods on three evaluation metrics: AUROC, AUPR and FPR95. |
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Paris; France; October 2023 |
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ICCVW |
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LAMP; MILAB |
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Admin @ si @ ARR2023 |
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3974 |
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Author |
A. Martinez; Jordi Vitria; J. Lopez |
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Title |
Visual Recognition of Surroundings: A robot that knows where it is. |
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1997 |
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Intelligence Artificielle et Complexite. |
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Paris. |
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OR;MV |
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BCNPCL @ bcnpcl @ MVL1997 |
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59 |
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Gemma Sanchez; Josep Llados; Enric Marti |
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Segmentation and analysis of linial texture in plans |
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1997 |
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Intelligence Artificielle et Complexité. |
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Structural Texture, Voronoi, Hierarchical Clustering, String Matching. |
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The problem of texture segmentation and interpretation is one of the main concerns in the field of document analysis. Graphical documents often contain areas characterized by a structural texture whose recognition allows both the document understanding, and its storage in a more compact way. In this work, we focus on structural linial textures of regular repetition contained in plan documents. Starting from an atributed graph which represents the vectorized input image, we develop a method to segment textured areas and recognize their placement rules. We wish to emphasize that the searched textures do not follow a predefined pattern. Minimal closed loops of the input graph are computed, and then hierarchically clustered. In this hierarchical clustering, a distance function between two closed loops is defined in terms of their areas difference and boundary resemblance computed by a string matching procedure. Finally it is noted that, when the texture consists of isolated primitive elements, the same method can be used after computing a Voronoi Tesselation of the input graph. |
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Paris, France |
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Paris |
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DAG;IAM; |
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IAM @ iam @ SLM1997 |
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1649 |
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David Roche; Debora Gil; Jesus Giraldo |
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Using statistical inference for designing termination conditions ensuring convergence of Evolutionary Algorithms |
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2011 |
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11th European Conference on Artificial Life |
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A main challenge in Evolutionary Algorithms (EAs) is determining a termination condition ensuring stabilization close to the optimum in real-world applications. Although for known test functions distribution-based quantities are good candidates (as far as suitable parameters are used), in real-world problems an open question still remains unsolved. How can we estimate an upper-bound for the termination condition value ensuring a given accuracy for the (unknown) EA solution?
We claim that the termination problem would be fully solved if we defined a quantity (depending only on the EA output) behaving like the solution accuracy. The open question would be, then, satisfactorily answered if we had a model relating both quantities, since accuracy could be predicted from the alternative quantity. We present a statistical inference framework addressing two topics: checking the correlation between the two quantities and defining a regression model for predicting (at a given confidence level) accuracy values from the EA output. |
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Paris, France |
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IAM @ iam @ RGG2011b |
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1678 |
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X. Orriols; Lluis Barcelo; X. Binefa |
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An Appearance-Based Method for Parametric Video Registration. |
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2001 |
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Admin @ si @ OBB2001b |
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Pejman Rasti; Tonis Uiboupin; Sergio Escalera; Gholamreza Anbarjafari |
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Convolutional Neural Network Super Resolution for Face Recognition in Surveillance Monitoring |
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2016 |
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9th Conference on Articulated Motion and Deformable Objects |
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Palma de Mallorca; Spain; July 2016 |
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AMDO |
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HuPBA;MILAB |
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Admin @ si @ RUE2016 |
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2846 |
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Dennis H. Lundtoft; Kamal Nasrollahi; Thomas B. Moeslund; Sergio Escalera |
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Spatiotemporal Facial Super-Pixels for Pain Detection |
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2016 |
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9th Conference on Articulated Motion and Deformable Objects |
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Facial images; Super-pixels; Spatiotemporal filters; Pain detection |
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Best student paper award.
Pain detection using facial images is of critical importance in many Health applications. Since pain is a spatiotemporal process, recent works on this topic employ facial spatiotemporal features to detect pain. These systems extract such features from the entire area of the face. In this paper, we show that by employing super-pixels we can divide the face into three regions, in a way that only one of these regions (about one third of the face) contributes to the pain estimation and the other two regions can be discarded. The experimental results on the UNBCMcMaster database show that the proposed system using this single region outperforms state-of-the-art systems in detecting no-pain scenarios, while it reaches comparable results in detecting weak and severe pain scenarios. |
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Palma de Mallorca; Spain; July 2016 |
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Admin @ si @ LNM2016 |
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2847 |
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Author |
Oualid M. Benkarim; Petia Radeva; Laura Igual |
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Title |
Label Consistent Multiclass Discriminative Dictionary Learning for MRI Segmentation |
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Conference Article |
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2014 |
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8th Conference on Articulated Motion and Deformable Objects |
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8563 |
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138-147 |
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MRI segmentation; sparse representation; discriminative dic- tionary learning; multiclass classi cation |
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Abstract |
The automatic segmentation of multiple subcortical structures in brain Magnetic Resonance Images (MRI) still remains a challenging task. In this paper, we address this problem using sparse representation and discriminative dictionary learning, which have shown promising results in compression, image denoising and recently in MRI segmentation. Particularly, we use multiclass dictionaries learned from a set of brain atlases to simultaneously segment multiple subcortical structures.
We also impose dictionary atoms to be specialized in one given class using label consistent K-SVD, which can alleviate the bias produced by unbalanced libraries, present when dealing with small structures. The proposed method is compared with other state of the art approaches for the segmentation of the Basal Ganglia of 35 subjects of a public dataset.
The promising results of the segmentation method show the eciency of the multiclass discriminative dictionary learning algorithms in MRI segmentation problems. |
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Palma de Mallorca; July 2014 |
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Springer International Publishing |
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0302-9743 |
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978-3-319-08848-8 |
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AMDO |
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MILAB; OR |
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Admin @ si @ BRI2014 |
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2494 |
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Author |
X. Varona; Jordi Gonzalez; Xavier Roca; Juan J. Villanueva |
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Automatic Selection of Keyframes for Activity Recognition. |
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2000 |
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International Workshop on Articulated Motion and Deformable Objects ( AMDO&rsquo), 173–181. |
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Palma de Mallorca. |
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ISE @ ise @ VGR2000b |
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