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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 | ||||
| Corporate Author | Thesis | ||||
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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICCV | ||
| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ LCG2023 | Serial | 3949 | ||
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| Author | German Barquero; Sergio Escalera; Cristina Palmero | ||||
| Title | BeLFusion: Latent Diffusion for Behavior-Driven Human Motion Prediction | Type | Conference Article | ||
| Year | 2023 | Publication | IEEE/CVF International Conference on Computer Vision (ICCV) Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | 2317-2327 | ||
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| Abstract | Stochastic human motion prediction (HMP) has generally been tackled with generative adversarial networks and variational autoencoders. Most prior works aim at predicting highly diverse movements in terms of the skeleton joints’ dispersion. This has led to methods predicting fast and motion-divergent movements, which are often unrealistic and incoherent with past motion. Such methods also neglect contexts that need to anticipate diverse low-range behaviors, or actions, with subtle joint displacements. To address these issues, we present BeLFusion, a model that, for the first time, leverages latent diffusion models in HMP to sample from a latent space where behavior is disentangled from pose and motion. As a result, diversity is encouraged from a behavioral perspective. Thanks to our behavior
coupler’s ability to transfer sampled behavior to ongoing motion, BeLFusion’s predictions display a variety of behaviors that are significantly more realistic than the state of the art. To support it, we introduce two metrics, the Area of the Cumulative Motion Distribution, and the Average Pairwise Distance Error, which are correlated to our definition of realism according to a qualitative study with 126 participants. Finally, we prove BeLFusion’s generalization power in a new cross-dataset scenario for stochastic HMP. |
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| Address | 2-6 October 2023. Paris (France) | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICCV | ||
| Notes | HUPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ BEP2023 | Serial | 3829 | ||
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| Author | Hugo Bertiche; Meysam Madadi; Emilio Tylson; Sergio Escalera | ||||
| Title | DeePSD: Automatic Deep Skinning And Pose Space Deformation For 3D Garment Animation | Type | Conference Article | ||
| Year | 2021 | Publication | 19th IEEE International Conference on Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 5471-5480 | ||
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| Abstract | We present a novel solution to the garment animation problem through deep learning. Our contribution allows animating any template outfit with arbitrary topology and geometric complexity. Recent works develop models for garment edition, resizing and animation at the same time by leveraging the support body model (encoding garments as body homotopies). This leads to complex engineering solutions that suffer from scalability, applicability and compatibility. By limiting our scope to garment animation only, we are able to propose a simple model that can animate any outfit, independently of its topology, vertex order or connectivity. Our proposed architecture maps outfits to animated 3D models into the standard format for 3D animation (blend weights and blend shapes matrices), automatically providing of compatibility with any graphics engine. We also propose a methodology to complement supervised learning with an unsupervised physically based learning that implicitly solves collisions and enhances cloth quality. | ||||
| Address | Virtual; October 2021 | ||||
| Corporate Author | Thesis | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICCV | ||
| Notes | HUPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ BMT2021 | Serial | 3606 | ||
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| Author | Fei Yang; Luis Herranz; Yongmei Cheng; Mikhail Mozerov | ||||
| Title | Slimmable compressive autoencoders for practical neural image compression | Type | Conference Article | ||
| Year | 2021 | Publication | 34th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 4996-5005 | ||
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| Abstract | Neural image compression leverages deep neural networks to outperform traditional image codecs in rate-distortion performance. However, the resulting models are also heavy, computationally demanding and generally optimized for a single rate, limiting their practical use. Focusing on practical image compression, we propose slimmable compressive autoencoders (SlimCAEs), where rate (R) and distortion (D) are jointly optimized for different capacities. Once trained, encoders and decoders can be executed at different capacities, leading to different rates and complexities. We show that a successful implementation of SlimCAEs requires suitable capacity-specific RD tradeoffs. Our experiments show that SlimCAEs are highly flexible models that provide excellent rate-distortion performance, variable rate, and dynamic adjustment of memory, computational cost and latency, thus addressing the main requirements of practical image compression. | ||||
| Address | Virtual; June 2021 | ||||
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| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | CVPR | ||
| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ YHC2021 | Serial | 3569 | ||
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| Author | Diego Porres | ||||
| Title | Discriminator Synthesis: On reusing the other half of Generative Adversarial Networks | Type | Conference Article | ||
| Year | 2021 | Publication | Machine Learning for Creativity and Design, Neurips Workshop | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | Generative Adversarial Networks have long since revolutionized the world of computer vision and, tied to it, the world of art. Arduous efforts have gone into fully utilizing and stabilizing training so that outputs of the Generator network have the highest possible fidelity, but little has gone into using the Discriminator after training is complete. In this work, we propose to use the latter and show a way to use the features it has learned from the training dataset to both alter an image and generate one from scratch. We name this method Discriminator Dreaming, and the full code can be found at this https URL. | ||||
| Address | Virtual; December 2021 | ||||
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| Area | Expedition | Conference | NEURIPSW | ||
| Notes | ADAS; 601.365 | Approved | no | ||
| Call Number | Admin @ si @ Por2021 | Serial | 3597 | ||
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| Author | Kai Wang; Fei Yang; Shiqi Yang; Muhammad Atif Butt; Joost Van de Weijer | ||||
| Title | Dynamic Prompt Learning: Addressing Cross-Attention Leakage for Text-Based Image Editing | Type | Conference Article | ||
| Year | 2023 | Publication | 37th Annual Conference on Neural Information Processing Systems | Abbreviated Journal | |
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| Abstract | Poster | ||||
| Address | New Orleans; USA; December 2023 | ||||
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| Area | Expedition | Conference | NEURIPS | ||
| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ WYY2023 | Serial | 3935 | ||
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| Author | Dipam Goswami; Yuyang Liu ; Bartlomiej Twardowski; Joost Van de Weijer | ||||
| Title | FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning | Type | Conference Article | ||
| Year | 2023 | Publication | 37th Annual Conference on Neural Information Processing Systems | Abbreviated Journal | |
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| Abstract | Poster | ||||
| Address | New Orleans; USA; December 2023 | ||||
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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | NEURIPS | ||
| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ GLT2023 | Serial | 3934 | ||
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| Author | C. Gratin; Jordi Vitria; F. Moreso; D. Seron | ||||
| Title | Texture Classification using Neural Networks and Local Granulometries | Type | Conference Article | ||
| Year | 1994 | Publication | EURASIP Workshop, Mathematical Morphology and Its Applications to image Processing, J.Serra and P.Soille, editors | Abbreviated Journal | |
| Volume | Issue | Pages | 309-316 | ||
| Keywords | Neural Networks; Granulometry; Kidney; Texture; Classication | ||||
| Abstract | |||||
| Address | Fointanebleau, France | ||||
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| Notes | OR;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ GVM1994 | Serial | 110 | ||
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| Author | Debora Gil; Oriol Ramos Terrades; Elisa Minchole; Carles Sanchez; Noelia Cubero de Frutos; Marta Diez-Ferrer; Rosa Maria Ortiz; Antoni Rosell | ||||
| Title | Classification of Confocal Endomicroscopy Patterns for Diagnosis of Lung Cancer | Type | Conference Article | ||
| Year | 2017 | Publication | 6th Workshop on Clinical Image-based Procedures: Translational Research in Medical Imaging | Abbreviated Journal | |
| Volume | 10550 | Issue | Pages | 151-159 | |
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| Abstract | Confocal Laser Endomicroscopy (CLE) is an emerging imaging technique that allows the in-vivo acquisition of cell patterns of potentially malignant lesions. Such patterns could discriminate between inflammatory and neoplastic lesions and, thus, serve as a first in-vivo biopsy to discard cases that do not actually require a cell biopsy.
The goal of this work is to explore whether CLE images obtained during videobronchoscopy contain enough visual information to discriminate between benign and malign peripheral lesions for lung cancer diagnosis. To do so, we have performed a pilot comparative study with 12 patients (6 adenocarcinoma and 6 benign-inflammatory) using 2 different methods for CLE pattern analysis: visual analysis by 3 experts and a novel methodology that uses graph methods to find patterns in pre-trained feature spaces. Our preliminary results indicate that although visual analysis can only achieve a 60.2% of accuracy, the accuracy of the proposed unsupervised image pattern classification raises to 84.6%. We conclude that CLE images visual information allow in-vivo detection of neoplastic lesions and graph structural analysis applied to deep-learning feature spaces can achieve competitive results. |
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| Address | Quebec; Canada; September 2017 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CLIP | ||
| Notes | IAM; 600.096; 600.075; 600.145 | Approved | no | ||
| Call Number | Admin @ si @ GRM2017 | Serial | 2957 | ||
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| Author | Asma Bensalah; Antonio Parziale; Giuseppe De Gregorio; Angelo Marcelli; Alicia Fornes; Josep Llados | ||||
| Title | I Can’t Believe It’s Not Better: In-air Movement for Alzheimer Handwriting Synthetic Generation | Type | Conference Article | ||
| Year | 2023 | Publication | 21st International Graphonomics Conference | Abbreviated Journal | |
| Volume | Issue | Pages | 136–148 | ||
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| Abstract | During recent years, there here has been a boom in terms of deep learning use for handwriting analysis and recognition. One main application for handwriting analysis is early detection and diagnosis in the health field. Unfortunately, most real case problems still suffer a scarcity of data, which makes difficult the use of deep learning-based models. To alleviate this problem, some works resort to synthetic data generation. Lately, more works are directed towards guided data synthetic generation, a generation that uses the domain and data knowledge to generate realistic data that can be useful to train deep learning models. In this work, we combine the domain knowledge about the Alzheimer’s disease for handwriting and use it for a more guided data generation. Concretely, we have explored the use of in-air movements for synthetic data generation. | ||||
| Address | Evora; Portugal; October 2023 | ||||
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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | IGS | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ BPG2023 | Serial | 3838 | ||
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| Author | Patricia Suarez; Dario Carpio; Angel Sappa | ||||
| Title | A Deep Learning Based Approach for Synthesizing Realistic Depth Maps | Type | Conference Article | ||
| Year | 2023 | Publication | 22nd International Conference on Image Analysis and Processing | Abbreviated Journal | |
| Volume | 14234 | Issue | Pages | 369–380 | |
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| Abstract | This paper presents a novel cycle generative adversarial network (CycleGAN) architecture for synthesizing high-quality depth maps from a given monocular image. The proposed architecture uses multiple loss functions, including cycle consistency, contrastive, identity, and least square losses, to enable the generation of realistic and high-fidelity depth maps. The proposed approach addresses this challenge by synthesizing depth maps from RGB images without requiring paired training data. Comparisons with several state-of-the-art approaches are provided showing the proposed approach overcome other approaches both in terms of quantitative metrics and visual quality. | ||||
| Address | Udine; Italia; Setember 2023 | ||||
| Corporate Author | Thesis | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICIAP | ||
| Notes | MSIAU | Approved | no | ||
| Call Number | Admin @ si @ SCS2023a | Serial | 3968 | ||
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| Author | Stepan Simsa; Michal Uricar; Milan Sulc; Yash Patel; Ahmed Hamdi; Matej Kocian; Matyas Skalicky; Jiri Matas; Antoine Doucet; Mickael Coustaty; Dimosthenis Karatzas | ||||
| Title | Overview of DocILE 2023: Document Information Localization and Extraction | Type | Conference Article | ||
| Year | 2023 | Publication | International Conference of the Cross-Language Evaluation Forum for European Languages | Abbreviated Journal | |
| Volume | 14163 | Issue | Pages | 276–293 | |
| Keywords | Information Extraction; Computer Vision; Natural Language Processing; Optical Character Recognition; Document Understanding | ||||
| Abstract | This paper provides an overview of the DocILE 2023 Competition, its tasks, participant submissions, the competition results and possible future research directions. This first edition of the competition focused on two Information Extraction tasks, Key Information Localization and Extraction (KILE) and Line Item Recognition (LIR). Both of these tasks require detection of pre-defined categories of information in business documents. The second task additionally requires correctly grouping the information into tuples, capturing the structure laid out in the document. The competition used the recently published DocILE dataset and benchmark that stays open to new submissions. The diversity of the participant solutions indicates the potential of the dataset as the submissions included pure Computer Vision, pure Natural Language Processing, as well as multi-modal solutions and utilized all of the parts of the dataset, including the annotated, synthetic and unlabeled subsets. | ||||
| Address | Thessaloniki; Greece; September 2023 | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | CLEF | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ SUS2023a | Serial | 3924 | ||
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| Author | Francesc Net; Marc Folia; Pep Casals; Lluis Gomez | ||||
| Title | Transductive Learning for Near-Duplicate Image Detection in Scanned Photo Collections | Type | Conference Article | ||
| Year | 2023 | Publication | 17th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | 14191 | Issue | Pages | 3-17 | |
| Keywords | Image deduplication; Near-duplicate images detection; Transductive Learning; Photographic Archives; Deep Learning | ||||
| Abstract | This paper presents a comparative study of near-duplicate image detection techniques in a real-world use case scenario, where a document management company is commissioned to manually annotate a collection of scanned photographs. Detecting duplicate and near-duplicate photographs can reduce the time spent on manual annotation by archivists. This real use case differs from laboratory settings as the deployment dataset is available in advance, allowing the use of transductive learning. We propose a transductive learning approach that leverages state-of-the-art deep learning architectures such as convolutional neural networks (CNNs) and Vision Transformers (ViTs). Our approach involves pre-training a deep neural network on a large dataset and then fine-tuning the network on the unlabeled target collection with self-supervised learning. The results show that the proposed approach outperforms the baseline methods in the task of near-duplicate image detection in the UKBench and an in-house private dataset. | ||||
| Address | San Jose; CA; USA; August 2023 | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ NFC2023 | Serial | 3859 | ||
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| Author | George Tom; Minesh Mathew; Sergi Garcia Bordils; Dimosthenis Karatzas; CV Jawahar | ||||
| Title | Reading Between the Lanes: Text VideoQA on the Road | Type | Conference Article | ||
| Year | 2023 | Publication | 17th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | 14192 | Issue | Pages | 137–154 | |
| Keywords | VideoQA; scene text; driving videos | ||||
| Abstract | Text and signs around roads provide crucial information for drivers, vital for safe navigation and situational awareness. Scene text recognition in motion is a challenging problem, while textual cues typically appear for a short time span, and early detection at a distance is necessary. Systems that exploit such information to assist the driver should not only extract and incorporate visual and textual cues from the video stream but also reason over time. To address this issue, we introduce RoadTextVQA, a new dataset for the task of video question answering (VideoQA) in the context of driver assistance. RoadTextVQA consists of 3, 222 driving videos collected from multiple countries, annotated with 10, 500 questions, all based on text or road signs present in the driving videos. We assess the performance of state-of-the-art video question answering models on our RoadTextVQA dataset, highlighting the significant potential for improvement in this domain and the usefulness of the dataset in advancing research on in-vehicle support systems and text-aware multimodal question answering. The dataset is available at http://cvit.iiit.ac.in/research/projects/cvit-projects/roadtextvqa. | ||||
| Address | San Jose; CA; USA; August 2023 | ||||
| Corporate Author | Thesis | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ TMG2023 | Serial | 3906 | ||
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| Author | Sergi Garcia Bordils; Dimosthenis Karatzas; Marçal Rusiñol | ||||
| Title | Accelerating Transformer-Based Scene Text Detection and Recognition via Token Pruning | Type | Conference Article | ||
| Year | 2023 | Publication | 17th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | 14192 | Issue | Pages | 106-121 | |
| Keywords | Scene Text Detection; Scene Text Recognition; Transformer Acceleration | ||||
| Abstract | Scene text detection and recognition is a crucial task in computer vision with numerous real-world applications. Transformer-based approaches are behind all current state-of-the-art models and have achieved excellent performance. However, the computational requirements of the transformer architecture makes training these methods slow and resource heavy. In this paper, we introduce a new token pruning strategy that significantly decreases training and inference times without sacrificing performance, striking a balance between accuracy and speed. We have applied this pruning technique to our own end-to-end transformer-based scene text understanding architecture. Our method uses a separate detection branch to guide the pruning of uninformative image features, which significantly reduces the number of tokens at the input of the transformer. Experimental results show how our network is able to obtain competitive results on multiple public benchmarks while running at significantly higher speeds. | ||||
| Address | San Jose; CA; USA; August 2023 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ GKR2023a | Serial | 3907 | ||
| Permanent link to this record | |||||