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| Author | Reza Azad; Maryam Asadi-Aghbolaghi; Mahmood Fathy; Sergio Escalera | ||||
Title  |
Attention Deeplabv3+: Multi-level Context Attention Mechanism for Skin Lesion Segmentation | Type | Conference Article | ||
| Year | 2020 | Publication | Bioimage computation workshop | Abbreviated Journal | |
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| Address | Virtual; August 2020 | ||||
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| Area | Expedition | Conference | ECCVW | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ AAF2020 | Serial | 3520 | ||
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| Author | Kai Wang; Fei Yang; Joost Van de Weijer | ||||
| Title |
Attention Distillation: self-supervised vision transformer students need more guidance | Type | Conference Article | ||
| Year | 2022 | Publication | 33rd British Machine Vision Conference | Abbreviated Journal | |
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| Abstract | Self-supervised learning has been widely applied to train high-quality vision transformers. Unleashing their excellent performance on memory and compute constraint devices is therefore an important research topic. However, how to distill knowledge from one self-supervised ViT to another has not yet been explored. Moreover, the existing self-supervised knowledge distillation (SSKD) methods focus on ConvNet based architectures are suboptimal for ViT knowledge distillation. In this paper, we study knowledge distillation of self-supervised vision transformers (ViT-SSKD). We show that directly distilling information from the crucial attention mechanism from teacher to student can significantly narrow the performance gap between both. In experiments on ImageNet-Subset and ImageNet-1K, we show that our method AttnDistill outperforms existing self-supervised knowledge distillation (SSKD) methods and achieves state-of-the-art k-NN accuracy compared with self-supervised learning (SSL) methods learning from scratch (with the ViT-S model). We are also the first to apply the tiny ViT-T model on self-supervised learning. Moreover, AttnDistill is independent of self-supervised learning algorithms, it can be adapted to ViT based SSL methods to improve the performance in future research. | ||||
| Address | London; UK; November 2022 | ||||
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| Area | Expedition | Conference | BMVC | ||
| Notes | LAMP; 600.147 | Approved | no | ||
| Call Number | Admin @ si @ WYW2022 | Serial | 3793 | ||
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| Author | J. Elder; Fadi Dornaika; Y. Hou; R. Goldstein | ||||
| Title |
Attentive wide-field sensing for visual telepresence and surveillance | Type | Book Chapter | ||
| Year | 2005 | Publication | L. Itti, G. Rees and J. Tsotsos (editors), Neurobiology of Attention, Academic Press / Elsevier | Abbreviated Journal | |
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| Notes | Approved | no | |||
| Call Number | Admin @ si @ EDH2005 | Serial | 604 | ||
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| Author | Shiqi Yang; Yaxing Wang; Kai Wang; Shangling Jui; Joost Van de Weijer | ||||
| Title |
Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation | Type | Conference Article | ||
| Year | 2022 | Publication | 36th Conference on Neural Information Processing Systems | Abbreviated Journal | |
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| Abstract | We propose a simple but effective source-free domain adaptation (SFDA) method.
Treating SFDA as an unsupervised clustering problem and following the intuition that local neighbors in feature space should have more similar predictions than other features, we propose to optimize an objective of prediction consistency. This objective encourages local neighborhood features in feature space to have similar predictions while features farther away in feature space have dissimilar predictions, leading to efficient feature clustering and cluster assignment simultaneously. For efficient training, we seek to optimize an upper-bound of the objective resulting in two simple terms. Furthermore, we relate popular existing methods in domain adaptation, source-free domain adaptation and contrastive learning via the perspective of discriminability and diversity. The experimental results prove the superiority of our method, and our method can be adopted as a simple but strong baseline for future research in SFDA. Our method can be also adapted to source-free open-set and partial-set DA which further shows the generalization ability of our method. |
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| Address | Virtual; November 2022 | ||||
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| Area | Expedition | Conference | NEURIPS | ||
| Notes | LAMP; 600.147 | Approved | no | ||
| Call Number | Admin @ si @ YWW2022a | Serial | 3792 | ||
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| Author | Lluis Pere de las Heras; Oriol Ramos Terrades; Josep Llados | ||||
| Title |
Attributed Graph Grammar for floor plan analysis | Type | Conference Article | ||
| Year | 2015 | Publication | 13th International Conference on Document Analysis and Recognition ICDAR2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 726 - 730 | ||
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| Abstract | In this paper, we propose the use of an Attributed Graph Grammar as unique framework to model and recognize the structure of floor plans. This grammar represents a building as a hierarchical composition of structurally and semantically related elements, where common representations are learned stochastically from annotated data. Given an input image, the parsing consists on constructing that graph representation that better agrees with the probabilistic model defined by the grammar. The proposed method provides several advantages with respect to the traditional floor plan analysis techniques. It uses an unsupervised statistical approach for detecting walls that adapts to different graphical notations and relaxes strong structural assumptions such are straightness and orthogonality. Moreover, the independence between the knowledge model and the parsing implementation allows the method to learn automatically different building configurations and thus, to cope the existing variability. These advantages are clearly demonstrated by comparing it with the most recent floor plan interpretation techniques on 4 datasets of real floor plans with different notations. | ||||
| Address | Nancy; France; August 2015 | ||||
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.077; 600.061 | Approved | no | ||
| Call Number | Admin @ si @ HRL2015b | Serial | 2727 | ||
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| Author | Dipam Goswami; J Schuster; Joost Van de Weijer; Didier Stricker | ||||
| Title |
Attribution-aware Weight Transfer: A Warm-Start Initialization for Class-Incremental Semantic Segmentation | Type | Conference Article | ||
| Year | 2023 | Publication | Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 3195-3204 | ||
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| Abstract | Attribution-aware Weight Transfer: A Warm-Start Initialization for Class-Incremental Semantic Segmentation. D Goswami, R Schuster, J van de Weijer, D Stricker. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023, pp. 3195-3204 | ||||
| Address | Waikoloa; Hawai; USA; January 2023 | ||||
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| Area | Expedition | Conference | WACV | ||
| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ GSW2023 | Serial | 3901 | ||
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| Author | Fatemeh Noroozi; Marina Marjanovic; Angelina Njegus; Sergio Escalera; Gholamreza Anbarjafari | ||||
| Title |
Audio-Visual Emotion Recognition in Video Clips | Type | Journal Article | ||
| Year | 2019 | Publication | IEEE Transactions on Affective Computing | Abbreviated Journal | TAC |
| Volume | 10 | Issue | 1 | Pages | 60-75 |
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| Abstract | This paper presents a multimodal emotion recognition system, which is based on the analysis of audio and visual cues. From the audio channel, Mel-Frequency Cepstral Coefficients, Filter Bank Energies and prosodic features are extracted. For the visual part, two strategies are considered. First, facial landmarks’ geometric relations, i.e. distances and angles, are computed. Second, we summarize each emotional video into a reduced set of key-frames, which are taught to visually discriminate between the emotions. In order to do so, a convolutional neural network is applied to key-frames summarizing videos. Finally, confidence outputs of all the classifiers from all the modalities are used to define a new feature space to be learned for final emotion label prediction, in a late fusion/stacking fashion. The experiments conducted on the SAVEE, eNTERFACE’05, and RML databases show significant performance improvements by our proposed system in comparison to current alternatives, defining the current state-of-the-art in all three databases. | ||||
| Address | 1 Jan.-March 2019 | ||||
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| Notes | HUPBA; 602.143; 602.133 | Approved | no | ||
| Call Number | Admin @ si @ NMN2017 | Serial | 3011 | ||
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| Author | Marcin Przewiezlikowski; Mateusz Pyla; Bartosz Zielinski; Bartłomiej Twardowski; Jacek Tabor; Marek Smieja | ||||
| Title |
Augmentation-aware Self-supervised Learning with Guided Projector | Type | Miscellaneous | ||
| Year | 2023 | Publication | arxiv | Abbreviated Journal | |
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| Abstract | Self-supervised learning (SSL) is a powerful technique for learning robust representations from unlabeled data. By learning to remain invariant to applied data augmentations, methods such as SimCLR and MoCo are able to reach quality on par with supervised approaches. However, this invariance may be harmful to solving some downstream tasks which depend on traits affected by augmentations used during pretraining, such as color. In this paper, we propose to foster sensitivity to such characteristics in the representation space by modifying the projector network, a common component of self-supervised architectures. Specifically, we supplement the projector with information about augmentations applied to images. In order for the projector to take advantage of this auxiliary conditioning when solving the SSL task, the feature extractor learns to preserve the augmentation information in its representations. Our approach, coined Conditional Augmentation-aware Self-supervised Learning (CASSLE), is directly applicable to typical joint-embedding SSL methods regardless of their objective functions. Moreover, it does not require major changes in the network architecture or prior knowledge of downstream tasks. In addition to an analysis of sensitivity towards different data augmentations, we conduct a series of experiments, which show that CASSLE improves over various SSL methods, reaching state-of-the-art performance in multiple downstream tasks. | ||||
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| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ PPZ2023 | Serial | 3971 | ||
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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 | |
| Volume | Issue | Pages | 11367-11377 | ||
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| Abstract | 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. | ||||
| Address | Paris; France; October 2023 | ||||
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| Area | Expedition | Conference | ICCV | ||
| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ LCG2023 | Serial | 3949 | ||
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| Author | Marçal Rusiñol; J. Chazalon; Katerine Diaz | ||||
| Title |
Augmented Songbook: an Augmented Reality Educational Application for Raising Music Awareness | Type | Journal Article | ||
| Year | 2018 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
| Volume | 77 | Issue | 11 | Pages | 13773-13798 |
| Keywords | Augmented reality; Document image matching; Educational applications | ||||
| Abstract | This paper presents the development of an Augmented Reality mobile application which aims at sensibilizing young children to abstract concepts of music. Such concepts are, for instance, the musical notation or the idea of rhythm. Recent studies in Augmented Reality for education suggest that such technologies have multiple benefits for students, including younger ones. As mobile document image acquisition and processing gains maturity on mobile platforms, we explore how it is possible to build a markerless and real-time application to augment the physical documents with didactic animations and interactive virtual content. Given a standard image processing pipeline, we compare the performance of different local descriptors at two key stages of the process. Results suggest alternatives to the SIFT local descriptors, regarding result quality and computational efficiency, both for document model identification and perspective transform estimation. All experiments are performed on an original and public dataset we introduce here. | ||||
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| Notes | DAG; ADAS; 600.084; 600.121; 600.118; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ RCD2018 | Serial | 2996 | ||
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| Author | Carles Fernandez; Pau Baiget; Xavier Roca; Jordi Gonzalez | ||||
| Title |
Augmenting Video Surveillance Footage with Virtual Agents for Incremental Event Evaluation | Type | Journal Article | ||
| Year | 2011 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 32 | Issue | 6 | Pages | 878–889 |
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| Abstract | The fields of segmentation, tracking and behavior analysis demand for challenging video resources to test, in a scalable manner, complex scenarios like crowded environments or scenes with high semantics. Nevertheless, existing public databases cannot scale the presence of appearing agents, which would be useful to study long-term occlusions and crowds. Moreover, creating these resources is expensive and often too particularized to specific needs. We propose an augmented reality framework to increase the complexity of image sequences in terms of occlusions and crowds, in a scalable and controllable manner. Existing datasets can be increased with augmented sequences containing virtual agents. Such sequences are automatically annotated, thus facilitating evaluation in terms of segmentation, tracking, and behavior recognition. In order to easily specify the desired contents, we propose a natural language interface to convert input sentences into virtual agent behaviors. Experimental tests and validation in indoor, street, and soccer environments are provided to show the feasibility of the proposed approach in terms of robustness, scalability, and semantics. | ||||
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ FBR2011b | Serial | 1723 | ||
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| Author | Zhengying Liu; Isabelle Guyon; Julio C. S. Jacques Junior; Meysam Madadi; Sergio Escalera; Adrien Pavao; Hugo Jair Escalante; Wei-Wei Tu; Zhen Xu; Sebastien Treguer | ||||
| Title |
AutoCV Challenge Design and Baseline Results | Type | Conference Article | ||
| Year | 2019 | Publication | La Conference sur l’Apprentissage Automatique | Abbreviated Journal | |
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| Abstract | We present the design and beta tests of a new machine learning challenge called AutoCV (for Automated Computer Vision), which is the first event in a series of challenges we are planning on the theme of Automated Deep Learning. We target applications for which Deep Learning methods have had great success in the past few years, with the aim of pushing the state of the art in fully automated methods to design the architecture of neural networks and train them without any human intervention. The tasks are restricted to multi-label image classification problems, from domains including medical, areal, people, object, and handwriting imaging. Thus the type of images will vary a lot in scales, textures, and structure. Raw data are provided (no features extracted), but all datasets are formatted in a uniform tensor manner (although images may have fixed or variable sizes within a dataset). The participants's code will be blind tested on a challenge platform in a controlled manner, with restrictions on training and test time and memory limitations. The challenge is part of the official selection of IJCNN 2019. | ||||
| Address | Toulouse; Francia; July 2019 | ||||
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| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ LGJ2019 | Serial | 3323 | ||
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| Author | D. Seron; F. Moreso; C. Gratin; Jordi Vitria; E. Condom | ||||
| Title |
Automated classification of renal interstitium and tubules by local texture analysis and a neural network | Type | Journal Article | ||
| Year | 1996 | Publication | Analytical and Quantitative Cytology and Histology | Abbreviated Journal | |
| Volume | 18 | Issue | 5 | Pages | 410-9, PMID: 8908314 |
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| Notes | OR;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ SMG1996 | Serial | 76 | ||
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| Author | Iban Berganzo-Besga; Hector A. Orengo; Felipe Lumbreras; Paloma Aliende; Monica N. Ramsey | ||||
| Title |
Automated detection and classification of multi-cell Phytoliths using Deep Learning-Based Algorithms | Type | Journal Article | ||
| Year | 2022 | Publication | Journal of Archaeological Science | Abbreviated Journal | JArchSci |
| Volume | 148 | Issue | Pages | 105654 | |
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| Abstract | This paper presents an algorithm for automated detection and classification of multi-cell phytoliths, one of the major components of many archaeological and paleoenvironmental deposits. This identification, based on phytolith wave pattern, is made using a pretrained VGG19 deep learning model. This approach has been tested in three key phytolith genera for the study of agricultural origins in Near East archaeology: Avena, Hordeum and Triticum. Also, this classification has been validated at species-level using Triticum boeoticum and dicoccoides images. Due to the diversity of microscopes, cameras and chemical treatments that can influence images of phytolith slides, three types of data augmentation techniques have been implemented: rotation of the images at 45-degree angles, random colour and brightness jittering, and random blur/sharpen. The implemented workflow has resulted in an overall accuracy of 93.68% for phytolith genera, improving previous attempts. The algorithm has also demonstrated its potential to automatize the classification of phytoliths species with an overall accuracy of 100%. The open code and platforms employed to develop the algorithm assure the method's accessibility, reproducibility and reusability. | ||||
| Address | December 2022 | ||||
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| Notes | MSIAU; MACO; 600.167 | Approved | no | ||
| Call Number | Admin @ si @ BOL2022 | Serial | 3753 | ||
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| Author | Jose A. Garcia; David Masip; Valerio Sbragaglia; Jacopo Aguzzi | ||||
| Title |
Automated Identification and Tracking of Nephrops norvegicus (L.) Using Infrared and Monochromatic Blue Light | Type | Conference Article | ||
| Year | 2016 | Publication | 19th International Conference of the Catalan Association for Artificial Intelligence | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | computer vision; video analysis; object recognition; tracking; behaviour; social; decapod; Nephrops norvegicus | ||||
| Abstract | Automated video and image analysis can be a very efficient tool to analyze
animal behavior based on sociality, especially in hard access environments for researchers. The understanding of this social behavior can play a key role in the sustainable design of capture policies of many species. This paper proposes the use of computer vision algorithms to identify and track a specific specie, the Norway lobster, Nephrops norvegicus, a burrowing decapod with relevant commercial value which is captured by trawling. These animals can only be captured when are engaged in seabed excursions, which are strongly related with their social behavior. This emergent behavior is modulated by the day-night cycle, but their social interactions remain unknown to the scientific community. The paper introduces an identification scheme made of four distinguishable black and white tags (geometric shapes). The project has recorded 15-day experiments in laboratory pools, under monochromatic blue light (472 nm.) and darkness conditions (recorded using Infra Red light). Using this massive image set, we propose a comparative of state-ofthe-art computer vision algorithms to distinguish and track the different animals’ movements. We evaluate the robustness to the high noise presence in the infrared video signals and free out-of-plane rotations due to animal movement. The experiments show promising accuracies under a cross-validation protocol, being adaptable to the automation and analysis of large scale data. In a second contribution, we created an extensive dataset of shapes (46027 different shapes) from four daily experimental video recordings, which will be available to the community. |
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| Address | Barcelona; Spain; October 2016 | ||||
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| Area | Expedition | Conference | CCIA | ||
| Notes | OR;MV; | Approved | no | ||
| Call Number | Admin @ si @ GMS2016 | Serial | 2816 | ||
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