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| Author | Marcos V Conde; Javier Vazquez; Michael S Brown; Radu TImofte | ||||
| Title | NILUT: Conditional Neural Implicit 3D Lookup Tables for Image Enhancement | Type | Conference Article | ||
| Year | 2024 | Publication | 38th AAAI Conference on Artificial Intelligence | Abbreviated Journal | |
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| Abstract | 3D lookup tables (3D LUTs) are a key component for image enhancement. Modern image signal processors (ISPs) have dedicated support for these as part of the camera rendering pipeline. Cameras typically provide multiple options for picture styles, where each style is usually obtained by applying a unique handcrafted 3D LUT. Current approaches for learning and applying 3D LUTs are notably fast, yet not so memory-efficient, as storing multiple 3D LUTs is required. For this reason and other implementation limitations, their use on mobile devices is less popular. In this work, we propose a Neural Implicit LUT (NILUT), an implicitly defined continuous 3D color transformation parameterized by a neural network. We show that NILUTs are capable of accurately emulating real 3D LUTs. Moreover, a NILUT can be extended to incorporate multiple styles into a single network with the ability to blend styles implicitly. Our novel approach is memory-efficient, controllable and can complement previous methods, including learned ISPs. | ||||
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| Area | Expedition | Conference | AAAI | ||
| Notes | CIC; MACO | Approved | no | ||
Call Number  |
Admin @ si @ CVB2024 | Serial | 3872 | ||
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| Author | Manuel Carbonell; Mauricio Villegas; Alicia Fornes; Josep Llados | ||||
| Title | Joint Recognition of Handwritten Text and Named Entities with a Neural End-to-end Model | Type | Conference Article | ||
| Year | 2018 | Publication | 13th IAPR International Workshop on Document Analysis Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 399-404 | ||
| Keywords | Named entity recognition; Handwritten Text Recognition; neural networks | ||||
| Abstract | When extracting information from handwritten documents, text transcription and named entity recognition are usually faced as separate subsequent tasks. This has the disadvantage that errors in the first module affect heavily the
performance of the second module. In this work we propose to do both tasks jointly, using a single neural network with a common architecture used for plain text recognition. Experimentally, the work has been tested on a collection of historical marriage records. Results of experiments are presented to show the effect on the performance for different configurations: different ways of encoding the information, doing or not transfer learning and processing at text line or multi-line region level. The results are comparable to state of the art reported in the ICDAR 2017 Information Extraction competition, even though the proposed technique does not use any dictionaries, language modeling or post processing. |
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| Address | Vienna; Austria; April 2018 | ||||
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| Series Editor | Series Title | Abbreviated Series Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 600.097; 603.057; 601.311; 600.121 | Approved | no | ||
| Call Number |
Admin @ si @ CVF2018 | Serial | 3170 | ||
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| Author | Trevor Canham; Javier Vazquez; D Long; Richard F. Murray; Michael S Brown | ||||
| Title | Noise Prism: A Novel Multispectral Visualization Technique | Type | Journal Article | ||
| Year | 2021 | Publication | 31st Color and Imaging Conference | Abbreviated Journal | |
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| Abstract | A novel technique for visualizing multispectral images is proposed. Inspired by how prisms work, our method spreads spectral information over a chromatic noise pattern. This is accomplished by populating the pattern with pixels representing each measurement band at a count proportional to its measured intensity. The method is advantageous because it allows for lightweight encoding and visualization of spectral information
while maintaining the color appearance of the stimulus. A four alternative forced choice (4AFC) experiment was conducted to validate the method’s information-carrying capacity in displaying metameric stimuli of varying colors and spectral basis functions. The scores ranged from 100% to 20% (less than chance given the 4AFC task), with many conditions falling somewhere in between at statistically significant intervals. Using this data, color and texture difference metrics can be evaluated and optimized to predict the legibility of the visualization technique. |
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| Area | Expedition | Conference | CIC | ||
| Notes | MACO; CIC | Approved | no | ||
| Call Number |
Admin @ si @ CVL2021 | Serial | 4000 | ||
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| Author | Trevor Canham; Javier Vazquez; Elise Mathieu; Marcelo Bertalmío | ||||
| Title | Matching visual induction effects on screens of different size | Type | Journal Article | ||
| Year | 2021 | Publication | Journal of Vision | Abbreviated Journal | JOV |
| Volume | 21 | Issue | 6(10) | Pages | 1-22 |
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| Abstract | In the film industry, the same movie is expected to be watched on displays of vastly different sizes, from cinema screens to mobile phones. But visual induction, the perceptual phenomenon by which the appearance of a scene region is affected by its surroundings, will be different for the same image shown on two displays of different dimensions. This phenomenon presents a practical challenge for the preservation of the artistic intentions of filmmakers, because it can lead to shifts in image appearance between viewing destinations. In this work, we show that a neural field model based on the efficient representation principle is able to predict induction effects and how, by regularizing its associated energy functional, the model is still able to represent induction but is now invertible. From this finding, we propose a method to preprocess an image in a screen–size dependent way so that its perception, in terms of visual induction, may remain constant across displays of different size. The potential of the method is demonstrated through psychophysical experiments on synthetic images and qualitative examples on natural images. | ||||
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| Area | Expedition | Conference | |||
| Notes | CIC | Approved | no | ||
| Call Number |
Admin @ si @ CVM2021 | Serial | 3595 | ||
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| Author | Jorge Charco; Boris X. Vintimilla; Angel Sappa | ||||
| Title | Deep learning based camera pose estimation in multi-view environment | Type | Conference Article | ||
| Year | 2018 | Publication | 14th IEEE International Conference on Signal Image Technology & Internet Based System | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Deep learning; Camera pose estimation; Multiview environment; Siamese architecture | ||||
| Abstract | This paper proposes to use a deep learning network architecture for relative camera pose estimation on a multi-view environment. The proposed network is a variant architecture of AlexNet to use as regressor for prediction the relative translation and rotation as output. The proposed approach is trained from
scratch on a large data set that takes as input a pair of imagesfrom the same scene. This new architecture is compared with a previous approach using standard metrics, obtaining better results on the relative camera pose. |
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| Address | Las Palmas de Gran Canaria; November 2018 | ||||
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| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | SITIS | ||
| Notes | MSIAU; 600.086; 600.130; 600.122 | Approved | no | ||
| Call Number |
Admin @ si @ CVS2018 | Serial | 3194 | ||
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| Author | Marcos V Conde; Florin Vasluianu; Javier Vazquez; Radu Timofte | ||||
| Title | Perceptual image enhancement for smartphone real-time applications | Type | Conference Article | ||
| Year | 2023 | Publication | Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 1848-1858 | ||
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| Abstract | Recent advances in camera designs and imaging pipelines allow us to capture high-quality images using smartphones. However, due to the small size and lens limitations of the smartphone cameras, we commonly find artifacts or degradation in the processed images. The most common unpleasant effects are noise artifacts, diffraction artifacts, blur, and HDR overexposure. Deep learning methods for image restoration can successfully remove these artifacts. However, most approaches are not suitable for real-time applications on mobile devices due to their heavy computation and memory requirements. In this paper, we propose LPIENet, a lightweight network for perceptual image enhancement, with the focus on deploying it on smartphones. Our experiments show that, with much fewer parameters and operations, our model can deal with the mentioned artifacts and achieve competitive performance compared with state-of-the-art methods on standard benchmarks. Moreover, to prove the efficiency and reliability of our approach, we deployed the model directly on commercial smartphones and evaluated its performance. Our model can process 2K resolution images under 1 second in mid-level commercial smartphones. | ||||
| Address | Waikoloa; Hawai; USA; January 2023 | ||||
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| Area | Expedition | Conference | WACV | ||
| Notes | MACO; CIC | Approved | no | ||
| Call Number |
Admin @ si @ CVV2023 | Serial | 3900 | ||
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| Author | Marco Cotogni; Fei Yang; Claudio Cusano; Andrew Bagdanov; Joost Van de Weijer | ||||
| Title | Gated Class-Attention with Cascaded Feature Drift Compensation for Exemplar-free Continual Learning of Vision Transformers | Type | Miscellaneous | ||
| Year | 2022 | Publication | Arxiv | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Marco Cotogni, Fei Yang, Claudio Cusano, Andrew D. Bagdanov, Joost van de Weijer | ||||
| Abstract | We propose a new method for exemplar-free class incremental training of ViTs. The main challenge of exemplar-free continual learning is maintaining plasticity of the learner without causing catastrophic forgetting of previously learned tasks. This is often achieved via exemplar replay which can help recalibrate previous task classifiers to the feature drift which occurs when learning new tasks. Exemplar replay, however, comes at the cost of retaining samples from previous tasks which for many applications may not be possible. To address the problem of continual ViT training, we first propose gated class-attention to minimize the drift in the final ViT transformer block. This mask-based gating is applied to class-attention mechanism of the last transformer block and strongly regulates the weights crucial for previous tasks. Importantly, gated class-attention does not require the task-ID during inference, which distinguishes it from other parameter isolation methods. Secondly, we propose a new method of feature drift compensation that accommodates feature drift in the backbone when learning new tasks. The combination of gated class-attention and cascaded feature drift compensation allows for plasticity towards new tasks while limiting forgetting of previous ones. Extensive experiments performed on CIFAR-100, Tiny-ImageNet and ImageNet100 demonstrate that our exemplar-free method obtains competitive results when compared to rehearsal based ViT methods. | ||||
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| Area | Expedition | Conference | |||
| Notes | LAMP; no proj | Approved | no | ||
| Call Number |
Admin @ si @ CYC2022 | Serial | 3827 | ||
| Permanent link to this record | |||||
| Author | Marco Cotogni; Fei Yang; Claudio Cusano; Andrew Bagdanov; Joost Van de Weijer | ||||
| Title | Exemplar-free Continual Learning of Vision Transformers via Gated Class-Attention and Cascaded Feature Drift Compensation | Type | Miscellaneous | ||
| Year | 2023 | Publication | ARXIV | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | We propose a new method for exemplar-free class incremental training of ViTs. The main challenge of exemplar-free continual learning is maintaining plasticity of the learner without causing catastrophic forgetting of previously learned tasks. This is often achieved via exemplar replay which can help recalibrate previous task classifiers to the feature drift which occurs when learning new tasks. Exemplar replay, however, comes at the cost of retaining samples from previous tasks which for many applications may not be possible. To address the problem of continual ViT training, we first propose gated class-attention to minimize the drift in the final ViT transformer block. This mask-based gating is applied to class-attention mechanism of the last transformer block and strongly regulates the weights crucial for previous tasks. Importantly, gated class-attention does not require the task-ID during inference, which distinguishes it from other parameter isolation methods. Secondly, we propose a new method of feature drift compensation that accommodates feature drift in the backbone when learning new tasks. The combination of gated class-attention and cascaded feature drift compensation allows for plasticity towards new tasks while limiting forgetting of previous ones. Extensive experiments performed on CIFAR-100, Tiny-ImageNet and ImageNet100 demonstrate that our exemplar-free method obtains competitive results when compared to rehearsal based ViT methods. | ||||
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| Area | Expedition | Conference | |||
| Notes | LAMP | Approved | no | ||
| Call Number |
Admin @ si @ CYC2023 | Serial | 3981 | ||
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| Author | Franck Davoine; Fadi Dornaika | ||||
| Title | Head and facial animation tracking using appearance-adaptive models and particle filters | Type | Book Chapter | ||
| Year | 2005 | Publication | V. Pavlovic and T.S. Huang (editors), Real–Time Vision for Human–Computer Interaction | Abbreviated Journal | |
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| Address | Springer-Verlag | ||||
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| Notes | Approved | no | |||
| Call Number |
Admin @ si @ DaD2005 | Serial | 599 | ||
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| Author | Clementine Decamps; Alexis Arnaud; Florent Petitprez; Mira Ayadi; Aurelia Baures; Lucile Armenoult; Sergio Escalera; Isabelle Guyon; Remy Nicolle; Richard Tomasini; Aurelien de Reynies; Jerome Cros; Yuna Blum; Magali Richard | ||||
| Title | DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification | Type | Journal Article | ||
| Year | 2021 | Publication | BMC Bioinformatics | Abbreviated Journal | |
| Volume | 22 | Issue | Pages | 473 | |
| Keywords | |||||
| Abstract | Quantification of tumor heterogeneity is essential to better understand cancer progression and to adapt therapeutic treatments to patient specificities. Bioinformatic tools to assess the different cell populations from single-omic datasets as bulk transcriptome or methylome samples have been recently developed, including reference-based and reference-free methods. Improved methods using multi-omic datasets are yet to be developed in the future and the community would need systematic tools to perform a comparative evaluation of these algorithms on controlled data. | ||||
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| Notes | HUPBA; no proj | Approved | no | ||
| Call Number |
Admin @ si @ DAP2021 | Serial | 3650 | ||
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| Author | Fadi Dornaika; A.Assoum; Bogdan Raducanu | ||||
| Title | Automatic Dimensionality Estimation for Manifold Learning through Optimal Feature Selection | Type | Conference Article | ||
| Year | 2012 | Publication | Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop | Abbreviated Journal | |
| Volume | 7626 | Issue | Pages | 575-583 | |
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| Abstract | A very important aspect in manifold learning is represented by automatic estimation of the intrinsic dimensionality. Unfortunately, this problem has received few attention in the literature of manifold learning. In this paper, we argue that feature selection paradigm can be used to the problem of automatic dimensionality estimation. Besides this, it also leads to improved recognition rates. Our approach for optimal feature selection is based on a Genetic Algorithm. As a case study for manifold learning, we have considered Laplacian Eigenmaps (LE) and Locally Linear Embedding (LLE). The effectiveness of the proposed framework was tested on the face recognition problem. Extensive experiments carried out on ORL, UMIST, Yale, and Extended Yale face data sets confirmed our hypothesis. | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-34165-6 | Medium | |
| Area | Expedition | Conference | SSPR&SPR | ||
| Notes | OR;MV | Approved | no | ||
| Call Number |
Admin @ si @ DAR2012 | Serial | 2174 | ||
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| Author | Fadi Dornaika; Jose Manuel Alvarez; Angel Sappa; Antonio Lopez | ||||
| Title | A New Framework for Stereo Sensor Pose through Road Segmentation and Registration | Type | Journal Article | ||
| Year | 2011 | Publication | IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
| Volume | 12 | Issue | 4 | Pages | 954-966 |
| Keywords | road detection | ||||
| Abstract | This paper proposes a new framework for real-time estimation of the onboard stereo head's position and orientation relative to the road surface, which is required for any advanced driver-assistance application. This framework can be used with all road types: highways, urban, etc. Unlike existing works that rely on feature extraction in either the image domain or 3-D space, we propose a framework that directly estimates the unknown parameters from the stream of stereo pairs' brightness. The proposed approach consists of two stages that are invoked for every stereo frame. The first stage segments the road region in one monocular view. The second stage estimates the camera pose using a featureless registration between the segmented monocular road region and the other view in the stereo pair. This paper has two main contributions. The first contribution combines a road segmentation algorithm with a registration technique to estimate the online stereo camera pose. The second contribution solves the registration using a featureless method, which is carried out using two different optimization techniques: 1) the differential evolution algorithm and 2) the Levenberg-Marquardt (LM) algorithm. We provide experiments and evaluations of performance. The results presented show the validity of our proposed framework. | ||||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1524-9050 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ADAS | Approved | no | ||
| Call Number |
Admin @ si @ DAS2011; ADAS @ adas @ das2011a | Serial | 1833 | ||
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| Author | Maria del Camp Davesa | ||||
| Title | Human action categorization in image sequences | Type | Report | ||
| Year | 2011 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume | 169 | Issue | Pages | ||
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| Address | Bellaterra (Spain) | ||||
| Corporate Author | Computer Vision Center | Thesis | Master's thesis | ||
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| Notes | CiC;CIC | Approved | no | ||
| Call Number |
Admin @ si @ Dav2011 | Serial | 1934 | ||
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| Author | Alloy Das; Sanket Biswas; Ayan Banerjee; Josep Llados; Umapada Pal; Saumik Bhattacharya | ||||
| Title | Harnessing the Power of Multi-Lingual Datasets for Pre-training: Towards Enhancing Text Spotting Performance | Type | Conference Article | ||
| Year | 2024 | Publication | Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 718-728 | ||
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| Abstract | The adaptation capability to a wide range of domains is crucial for scene text spotting models when deployed to real-world conditions. However, existing state-of-the-art (SOTA) approaches usually incorporate scene text detection and recognition simply by pretraining on natural scene text datasets, which do not directly exploit the intermediate feature representations between multiple domains. Here, we investigate the problem of domain-adaptive scene text spotting, i.e., training a model on multi-domain source data such that it can directly adapt to target domains rather than being specialized for a specific domain or scenario. Further, we investigate a transformer baseline called Swin-TESTR to focus on solving scene-text spotting for both regular and arbitrary-shaped scene text along with an exhaustive evaluation. The results clearly demonstrate the potential of intermediate representations to achieve significant performance on text spotting benchmarks across multiple domains (e.g. language, synth-to-real, and documents). both in terms of accuracy and efficiency. | ||||
| Address | Waikoloa; Hawai; USA; January 2024 | ||||
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| Area | Expedition | Conference | WACV | ||
| Notes | DAG | Approved | no | ||
| Call Number |
Admin @ si @ DBB2024 | Serial | 3986 | ||
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| Author | Alloy Das; Sanket Biswas; Umapada Pal; Josep Llados | ||||
| Title | Diving into the Depths of Spotting Text in Multi-Domain Noisy Scenes | Type | Conference Article | ||
| Year | 2024 | Publication | IEEE International Conference on Robotics and Automation in PACIFICO | Abbreviated Journal | |
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| Abstract | When used in a real-world noisy environment, the capacity to generalize to multiple domains is essential for any autonomous scene text spotting system. However, existing state-of-the-art methods employ pretraining and fine-tuning strategies on natural scene datasets, which do not exploit the feature interaction across other complex domains. In this work, we explore and investigate the problem of domain-agnostic scene text spotting, i.e., training a model on multi-domain source data such that it can directly generalize to target domains rather than being specialized for a specific domain or scenario. In this regard, we present the community a text spotting validation benchmark called Under-Water Text (UWT) for noisy underwater scenes to establish an important case study. Moreover, we also design an efficient super-resolution based end-to-end transformer baseline called DA-TextSpotter which achieves comparable or superior performance over existing text spotting architectures for both regular and arbitrary-shaped scene text spotting benchmarks in terms of both accuracy and model efficiency. The dataset, code and pre-trained models will be released upon acceptance. | ||||
| Address | Yokohama; Japan; May 2024 | ||||
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| Area | Expedition | Conference | ICRA | ||
| Notes | DAG | Approved | no | ||
| Call Number |
Admin @ si @ DBP2024 | Serial | 3979 | ||
| Permanent link to this record | |||||