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Author Alex Gomez-Villa; Bartlomiej Twardowski; Kai Wang; Joost van de Weijer edit   pdf
url  openurl
  Title Plasticity-Optimized Complementary Networks for Unsupervised Continual Learning Type Conference Article
  Year (down) 2024 Publication Winter Conference on Applications of Computer Vision Abbreviated Journal  
  Volume Issue Pages 1690-1700  
  Keywords  
  Abstract Continuous unsupervised representation learning (CURL) research has greatly benefited from improvements in self-supervised learning (SSL) techniques. As a result, existing CURL methods using SSL can learn high-quality representations without any labels, but with a notable performance drop when learning on a many-tasks data stream. We hypothesize that this is caused by the regularization losses that are imposed to prevent forgetting, leading to a suboptimal plasticity-stability trade-off: they either do not adapt fully to the incoming data (low plasticity), or incur significant forgetting when allowed to fully adapt to a new SSL pretext-task (low stability). In this work, we propose to train an expert network that is relieved of the duty of keeping the previous knowledge and can focus on performing optimally on the new tasks (optimizing plasticity). In the second phase, we combine this new knowledge with the previous network in an adaptation-retrospection phase to avoid forgetting and initialize a new expert with the knowledge of the old network. We perform several experiments showing that our proposed approach outperforms other CURL exemplar-free methods in few- and many-task split settings. Furthermore, we show how to adapt our approach to semi-supervised continual learning (Semi-SCL) and show that we surpass the accuracy of other exemplar-free Semi-SCL methods and reach the results of some others that use exemplars.  
  Address Waikoloa; Hawai; USA; January 2024  
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  ISSN ISBN Medium  
  Area Expedition Conference WACV  
  Notes LAMP Approved no  
  Call Number Admin @ si @ GTW2024 Serial 3989  
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Author Alloy Das; Sanket Biswas; Ayan Banerjee; Josep Llados; Umapada Pal; Saumik Bhattacharya edit   pdf
url  openurl
  Title Harnessing the Power of Multi-Lingual Datasets for Pre-training: Towards Enhancing Text Spotting Performance Type Conference Article
  Year (down) 2024 Publication Winter Conference on Applications of Computer Vision Abbreviated Journal  
  Volume Issue Pages 718-728  
  Keywords  
  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  
  Corporate Author Thesis  
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  Series Editor Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference WACV  
  Notes DAG Approved no  
  Call Number Admin @ si @ DBB2024 Serial 3986  
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Author Hector Laria Mantecon; Kai Wang; Joost Van de Weijer; Bogdan Raducanu; Kai Wang edit  url
openurl 
  Title NeRF-Diffusion for 3D-Consistent Face Generation and Editing Type Conference Article
  Year (down) 2024 Publication 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications Abbreviated Journal  
  Volume Issue Pages  
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  Abstract Generating high-fidelity 3D-aware images without 3D supervision is a valuable capability in various applications. Current methods based on NeRF features, SDF information, or triplane features have limited variation after training. To address this, we propose a novel approach that combines pretrained models for shape and content generation. Our method leverages a pretrained Neural Radiance Field as a shape prior and a diffusion model for content generation. By conditioning the diffusion model with 3D features, we enhance its ability to generate novel views with 3D awareness. We introduce a consistency token shared between the NeRF module and the diffusion model to maintain 3D consistency during sampling. Moreover, our framework allows for text editing of 3D-aware image generation, enabling users to modify the style over 3D views while preserving semantic content. Our contributions include incorporating 3D awareness into a text-to-image model, addressing identity consistency in 3D view synthesis, and enabling text editing of 3D-aware image generation. We provide detailed explanations, including the shape prior based on the NeRF model and the content generation process using the diffusion model. We also discuss challenges such as shape consistency and sampling saturation. Experimental results demonstrate the effectiveness and visual quality of our approach.  
  Address Roma; Italia; February 2024  
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  ISSN ISBN Medium  
  Area Expedition Conference VISAPP  
  Notes LAMP Approved no  
  Call Number Admin @ si @ LWW2024 Serial 4003  
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Author Henry Velesaca; Gisel Bastidas-Guacho; Mohammad Rouhani; Angel Sappa edit  url
openurl 
  Title Multimodal image registration techniques: a comprehensive survey Type Journal Article
  Year (down) 2024 Publication Multimedia Tools and Applications Abbreviated Journal MTAP  
  Volume Issue Pages  
  Keywords  
  Abstract This manuscript presents a review of state-of-the-art techniques proposed in the literature for multimodal image registration, addressing instances where images from different modalities need to be precisely aligned in the same reference system. This scenario arises when the images to be registered come from different modalities, among the visible and thermal spectral bands, 3D-RGB, or flash-no flash, or NIR-visible. The review spans different techniques from classical approaches to more modern ones based on deep learning, aiming to highlight the particularities required at each step in the registration pipeline when dealing with multimodal images. It is noteworthy that medical images are excluded from this review due to their specific characteristics, including the use of both active and passive sensors or the non-rigid nature of the body contained in the image.  
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  Notes MSIAU Approved no  
  Call Number Admin @ si @ VBR2024 Serial 3997  
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Author Hunor Laczko; Meysam Madadi; Sergio Escalera; Jordi Gonzalez edit   pdf
url  openurl
  Title A Generative Multi-Resolution Pyramid and Normal-Conditioning 3D Cloth Draping Type Conference Article
  Year (down) 2024 Publication Winter Conference on Applications of Computer Vision Abbreviated Journal  
  Volume Issue Pages 8709-8718  
  Keywords  
  Abstract RGB cloth generation has been deeply studied in the related literature, however, 3D garment generation remains an open problem. In this paper, we build a conditional variational autoencoder for 3D garment generation and draping. We propose a pyramid network to add garment details progressively in a canonical space, i.e. unposing and unshaping the garments w.r.t. the body. We study conditioning the network on surface normal UV maps, as an intermediate representation, which is an easier problem to optimize than 3D coordinates. Our results on two public datasets, CLOTH3D and CAPE, show that our model is robust, controllable in terms of detail generation by the use of multi-resolution pyramids, and achieves state-of-the-art results that can highly generalize to unseen garments, poses, and shapes even when training with small amounts of data.  
  Address Waikoloa; Hawai; USA; January 2024  
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  Publisher Place of Publication Editor  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference WACV  
  Notes ISE; HUPBA Approved no  
  Call Number Admin @ si @ LME2024 Serial 3996  
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Author M. Altillawi; S. Li; S.M. Prakhya; Z. Liu; Joan Serrat edit  doi
openurl 
  Title Implicit Learning of Scene Geometry From Poses for Global Localization Type Journal Article
  Year (down) 2024 Publication IEEE Robotics and Automation Letters Abbreviated Journal ROBOTAUTOMLET  
  Volume 9 Issue 2 Pages 955-962  
  Keywords Localization; Localization and mapping; Deep learning for visual perception; Visual learning  
  Abstract Global visual localization estimates the absolute pose of a camera using a single image, in a previously mapped area. Obtaining the pose from a single image enables many robotics and augmented/virtual reality applications. Inspired by latest advances in deep learning, many existing approaches directly learn and regress 6 DoF pose from an input image. However, these methods do not fully utilize the underlying scene geometry for pose regression. The challenge in monocular relocalization is the minimal availability of supervised training data, which is just the corresponding 6 DoF poses of the images. In this letter, we propose to utilize these minimal available labels (i.e., poses) to learn the underlying 3D geometry of the scene and use the geometry to estimate the 6 DoF camera pose. We present a learning method that uses these pose labels and rigid alignment to learn two 3D geometric representations ( X, Y, Z coordinates ) of the scene, one in camera coordinate frame and the other in global coordinate frame. Given a single image, it estimates these two 3D scene representations, which are then aligned to estimate a pose that matches the pose label. This formulation allows for the active inclusion of additional learning constraints to minimize 3D alignment errors between the two 3D scene representations, and 2D re-projection errors between the 3D global scene representation and 2D image pixels, resulting in improved localization accuracy. During inference, our model estimates the 3D scene geometry in camera and global frames and aligns them rigidly to obtain pose in real-time. We evaluate our work on three common visual localization datasets, conduct ablation studies, and show that our method exceeds state-of-the-art regression methods' pose accuracy on all datasets.  
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  ISSN 2377-3766 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number Admin @ si @ Serial 3857  
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Author Marcos V Conde; Javier Vazquez; Michael S Brown; Radu TImofte edit   pdf
url  openurl
  Title NILUT: Conditional Neural Implicit 3D Lookup Tables for Image Enhancement Type Conference Article
  Year (down) 2024 Publication 38th AAAI Conference on Artificial Intelligence Abbreviated Journal  
  Volume Issue Pages  
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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 Mohamed Ramzy Ibrahim; Robert Benavente; Daniel Ponsa; Felipe Lumbreras edit  url
openurl 
  Title SWViT-RRDB: Shifted Window Vision Transformer Integrating Residual in Residual Dense Block for Remote Sensing Super-Resolution Type Conference Article
  Year (down) 2024 Publication 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications Abbreviated Journal  
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  Abstract Remote sensing applications, impacted by acquisition season and sensor variety, require high-resolution images. Transformer-based models improve satellite image super-resolution but are less effective than convolutional neural networks (CNNs) at extracting local details, crucial for image clarity. This paper introduces SWViT-RRDB, a new deep learning model for satellite imagery super-resolution. The SWViT-RRDB, combining transformer with convolution and attention blocks, overcomes the limitations of existing models by better representing small objects in satellite images. In this model, a pipeline of residual fusion group (RFG) blocks is used to combine the multi-headed self-attention (MSA) with residual in residual dense block (RRDB). This combines global and local image data for better super-resolution. Additionally, an overlapping cross-attention block (OCAB) is used to enhance fusion and allow interaction between neighboring pixels to maintain long-range pixel dependencies across the image. The SWViT-RRDB model and its larger variants outperform state-of-the-art (SoTA) models on two different satellite datasets in terms of PSNR and SSIM.  
  Address Roma; Italia; February 2024  
  Corporate Author Thesis  
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  Series Editor Series Title Abbreviated Series Title  
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  Area Expedition Conference  
  Notes MSIAU Approved no  
  Call Number Admin @ si @ RBP2024 Serial 4004  
Permanent link to this record
 
 
Author Patricia Suarez; Angel Sappa edit  url
openurl 
  Title A Generative Model for Guided Thermal Image Super-Resolution Type Conference Article
  Year (down) 2024 Publication 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications Abbreviated Journal  
  Volume Issue Pages  
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  Abstract This paper presents a novel approach for thermal super-resolution based on a fusion prior, low-resolution thermal image and H brightness channel of the corresponding visible spectrum image. The method combines bicubic interpolation of the ×8 scale target image with the brightness component. To enhance the guidance process, the original RGB image is converted to HSV, and the brightness channel is extracted. Bicubic interpolation is then applied to the low-resolution thermal image, resulting in a Bicubic-Brightness channel blend. This luminance-bicubic fusion is used as an input image to help the training process. With this fused image, the cyclic adversarial generative network obtains high-resolution thermal image results. Experimental evaluations show that the proposed approach significantly improves spatial resolution and pixel intensity levels compared to other state-of-the-art techniques, making it a promising method to obtain high-resolution thermal.  
  Address Roma; Italia; February 2024  
  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 VISAPP  
  Notes MSIAU Approved no  
  Call Number Admin @ si @ SuS2024 Serial 4002  
Permanent link to this record
 
 
Author Patricia Suarez; Dario Carpio; Angel Sappa edit  url
openurl 
  Title Enhancement of guided thermal image super-resolution approaches Type Journal Article
  Year (down) 2024 Publication Neurocomputing Abbreviated Journal NEUCOM  
  Volume 573 Issue 127197 Pages 1-17  
  Keywords  
  Abstract Guided image processing techniques are widely used to extract meaningful information from a guiding image and facilitate the enhancement of the guided one. This paper specifically addresses the challenge of guided thermal image super-resolution, where a low-resolution thermal image is enhanced using a high-resolution visible spectrum image. We propose a new strategy that enhances outcomes from current guided super-resolution methods. This is achieved by transforming the initial guiding data into a representation resembling a thermal-like image, which is more closely in sync with the intended output. Experimental results with upscale factors of 8 and 16, demonstrate the outstanding performance of our approach in guided thermal image super-resolution obtained by mapping the original guiding information to a thermal-like image representation.  
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  Series Editor Series Title Abbreviated Series Title  
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  Area Expedition Conference  
  Notes MSIAU Approved no  
  Call Number Admin @ si @ SCS2024 Serial 3998  
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