| Records |
| Author |
Valeriya Khan; Sebastian Cygert; Bartlomiej Twardowski; Tomasz Trzcinski |
| Title |
Looking Through the Past: Better Knowledge Retention for Generative Replay in Continual Learning |
Type |
Conference Article |
Year  |
2023 |
Publication |
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops |
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3496-3500 |
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In this work, we improve the generative replay in a continual learning setting. We notice that in VAE-based generative replay, the generated features are quite far from the original ones when mapped to the latent space. Therefore, we propose modifications that allow the model to learn and generate complex data. More specifically, we incorporate the distillation in latent space between the current and previous models to reduce feature drift. Additionally, a latent matching for the reconstruction and original data is proposed to improve generated features alignment. Further, based on the observation that the reconstructions are better for preserving knowledge, we add the cycling of generations through the previously trained model to make them closer to the original data. Our method outperforms other generative replay methods in various scenarios. |
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LAMP |
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no |
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Admin @ si @ KCT2023 |
Serial |
3942 |
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| Author |
Damian Sojka; Sebastian Cygert; Bartlomiej Twardowski; Tomasz Trzcinski |
| Title |
AR-TTA: A Simple Method for Real-World Continual Test-Time Adaptation |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops |
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3491-3495 |
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Test-time adaptation is a promising research direction that allows the source model to adapt itself to changes in data distribution without any supervision. Yet, current methods are usually evaluated on benchmarks that are only a simplification of real-world scenarios. Hence, we propose to validate test-time adaptation methods using the recently introduced datasets for autonomous driving, namely CLAD-C and SHIFT. We observe that current test-time adaptation methods struggle to effectively handle varying degrees of domain shift, often resulting in degraded performance that falls below that of the source model. We noticed that the root of the problem lies in the inability to preserve the knowledge of the source model and adapt to dynamically changing, temporally correlated data streams. Therefore, we enhance well-established self-training framework by incorporating a small memory buffer to increase model stability and at the same time perform dynamic adaptation based on the intensity of domain shift. The proposed method, named AR-TTA, outperforms existing approaches on both synthetic and more real-world benchmarks and shows robustness across a variety of TTA scenarios. |
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Paris; France; October 2023 |
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ICCVW |
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LAMP |
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no |
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Admin @ si @ SCT2023 |
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3943 |
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| Author |
Filip Szatkowski; Mateusz Pyla; Marcin Przewięzlikowski; Sebastian Cygert; Bartłomiej Twardowski; Tomasz Trzcinski |
| Title |
Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-Free Continual Learning |
Type |
Conference Article |
| Year |
2023 |
Publication |
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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no |
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Admin @ si @ |
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3944 |
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| Author |
Fei Wang; Kai Wang; Joost Van de Weijer |
| Title |
ScrollNet: DynamicWeight Importance for Continual Learning |
Type |
Conference Article |
| Year |
2023 |
Publication |
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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ICCVW |
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LAMP |
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no |
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Admin @ si @ WWW2023 |
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3945 |
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| Author |
Soumya Jahagirdar; Minesh Mathew; Dimosthenis Karatzas; CV Jawahar |
| Title |
Understanding Video Scenes Through Text: Insights from Text-Based Video Question Answering |
Type |
Conference Article |
| Year |
2023 |
Publication |
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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ICCVW |
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DAG |
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no |
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Admin @ si @ JMK2023 |
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3946 |
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| Author |
Dawid Rymarczyk; Joost van de Weijer; Bartosz Zielinski; Bartlomiej Twardowski |
| Title |
ICICLE: Interpretable Class Incremental Continual Learning. Dawid Rymarczyk |
Type |
Conference Article |
| Year |
2023 |
Publication |
20th IEEE International Conference on Computer Vision |
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Pages |
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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ICCV |
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LAMP |
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no |
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Admin @ si @ RWZ2023 |
Serial |
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 |
| Title |
Document Understanding Dataset and Evaluation (DUDE) |
Type |
Conference Article |
| Year |
2023 |
Publication |
20th IEEE International Conference on Computer Vision |
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Pages |
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 |
Serial |
3948 |
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| Author |
Yuyang Liu; Yang Cong; Dipam Goswami; Xialei Liu; Joost Van de Weijer |
| Title |
Augmented Box Replay: Overcoming Foreground Shift for Incremental Object Detection |
Type |
Conference Article |
| Year |
2023 |
Publication |
20th IEEE International Conference on Computer Vision |
Abbreviated Journal |
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Pages |
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 |
Guillermo Torres; Debora Gil; Antoni Rosell; S. Mena; Carles Sanchez |
| Title |
Virtual Radiomics Biopsy for the Histological Diagnosis of Pulmonary Nodules |
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Conference Article |
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2023 |
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37th International Congress and Exhibition is organized by Computer Assisted Radiology and Surgery |
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Pòster |
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Munich; Germany; June 2023 |
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CARS |
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IAM |
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no |
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Admin @ si @ TGR2023a |
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3950 |
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Sonia Baeza; Debora Gil; Carles Sanchez; Guillermo Torres; Ignasi Garcia Olive; Ignasi Guasch; Samuel Garcia Reina; Felipe Andreo; Jose Luis Mate; Jose Luis Vercher; Antonio Rosell |
| Title |
Biopsia virtual radiomica para el diagnóstico histológico de nódulos pulmonares – Resultados intermedios del proyecto Radiolung |
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2023 |
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SEPAR |
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Granada; Spain; June 2023 |
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IAM |
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no |
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Admin @ si @ BGS2023 |
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3951 |
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Debora Gil; Guillermo Torres; Carles Sanchez |
| Title |
Transforming radiomic features into radiological words |
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2023 |
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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 @ GTS2023 |
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3952 |
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| Author |
Pau Cano; Debora Gil; Eva Musulen |
| Title |
Towards automatic detection of helicobacter pylori in histological samples of gastric tissue |
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2023 |
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IEEE International Symposium on Biomedical Imaging |
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Cartagena de Indias; Colombia; April 2023 |
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IAM |
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Admin @ si @ CGM2023 |
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3953 |
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Guillermo Torres; Debora Gil; Antonio Rosell; Sonia Baeza; Carles Sanchez |
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A radiomic biopsy for virtual histology of pulmonary nodules |
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2023 |
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IEEE International Symposium on Biomedical Imaging |
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Cartagena de Indias; Colombia; April 2023 |
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IAM |
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Admin @ si @ TGR2023b |
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3954 |
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Jun Wan; Guodong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z Li |
| Title |
Advances in Face Presentation Attack Detection |
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2023 |
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Advances in Face Presentation Attack Detection |
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HUPBA |
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no |
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Admin @ si @ WGE2023a |
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3955 |
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Jun Wan; Guodong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z Li |
| Title |
Face Presentation Attack Detection (PAD) Challenges |
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2023 |
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Advances in Face Presentation Attack Detection |
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17–35 |
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In recent years, the security of face recognition systems has been increasingly threatened. Face Anti-spoofing (FAS) is essential to secure face recognition systems primarily from various attacks. In order to attract researchers and push forward the state of the art in Face Presentation Attack Detection (PAD), we organized three editions of Face Anti-spoofing Workshop and Competition at CVPR 2019, CVPR 2020, and ICCV 2021, which have attracted more than 800 teams from academia and industry, and greatly promoted the algorithms to overcome many challenging problems. In this chapter, we introduce the detailed competition process, including the challenge phases, timeline and evaluation metrics. Along with the workshop, we will introduce the corresponding dataset for each competition including data acquisition details, data processing, statistics, and evaluation protocol. Finally, we provide the available link to download the datasets used in the challenges. |
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SLCV |
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HUPBA |
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no |
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Admin @ si @ WGE2023b |
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3956 |
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