| Records |
| 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 |
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Issue |
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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. |
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Virtual; June 2021 |
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CVPR |
| Notes |
LAMP; 600.120 |
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no |
| Call Number |
Admin @ si @ YHC2021 |
Serial |
3569 |
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| Author |
Mohammad Rouhani; Angel Sappa |
| Title |
The Richer Representation the Better Registration |
Type |
Journal Article |
| Year |
2013 |
Publication |
IEEE Transactions on Image Processing |
Abbreviated Journal |
TIP |
| Volume |
22 |
Issue |
12 |
Pages |
5036-5049 |
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| Abstract |
In this paper, the registration problem is formulated as a point to model distance minimization. Unlike most of the existing works, which are based on minimizing a point-wise correspondence term, this formulation avoids the correspondence search that is time-consuming. In the first stage, the target set is described through an implicit function by employing a linear least squares fitting. This function can be either an implicit polynomial or an implicit B-spline from a coarse to fine representation. In the second stage, we show how the obtained implicit representation is used as an interface to convert point-to-point registration into point-to-implicit problem. Furthermore, we show that this registration distance is smooth and can be minimized through the Levengberg-Marquardt algorithm. All the formulations presented for both stages are compact and easy to implement. In addition, we show that our registration method can be handled using any implicit representation though some are coarse and others provide finer representations; hence, a tradeoff between speed and accuracy can be set by employing the right implicit function. Experimental results and comparisons in 2D and 3D show the robustness and the speed of convergence of the proposed approach. |
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1057-7149 |
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ADAS |
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no |
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Admin @ si @ RoS2013 |
Serial |
2665 |
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| Author |
Anjan Dutta; Zeynep Akata |
| Title |
Semantically Tied Paired Cycle Consistency for Zero-Shot Sketch-based Image Retrieval |
Type |
Conference Article |
| Year |
2019 |
Publication |
32nd IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
5089-5098 |
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Zero-shot sketch-based image retrieval (SBIR) is an emerging task in computer vision, allowing to retrieve natural images relevant to sketch queries that might not been seen in the training phase. Existing works either require aligned sketch-image pairs or inefficient memory fusion layer for mapping the visual information to a semantic space. In this work, we propose a semantically aligned paired cycle-consistent generative (SEM-PCYC) model for zero-shot SBIR, where each branch maps the visual information to a common semantic space via an adversarial training. Each of these branches maintains a cycle consistency that only requires supervision at category levels, and avoids the need of highly-priced aligned sketch-image pairs. A classification criteria on the generators' outputs ensures the visual to semantic space mapping to be discriminating. Furthermore, we propose to combine textual and hierarchical side information via a feature selection auto-encoder that selects discriminating side information within a same end-to-end model. Our results demonstrate a significant boost in zero-shot SBIR performance over the state-of-the-art on the challenging Sketchy and TU-Berlin datasets. |
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Long beach; California; USA; June 2019 |
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CVPR |
| Notes |
DAG; 600.141; 600.121 |
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no |
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Admin @ si @ DuA2019 |
Serial |
3268 |
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| Author |
Yasuko Sugito; Javier Vazquez; Trevor Canham; Marcelo Bertalmio |
| Title |
Image quality evaluation in professional HDR/WCG production questions the need for HDR metrics |
Type |
Journal Article |
| Year |
2022 |
Publication |
IEEE Transactions on Image Processing |
Abbreviated Journal |
TIP |
| Volume |
31 |
Issue |
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Pages |
5163 - 5177 |
| Keywords |
Measurement; Image color analysis; Image coding; Production; Dynamic range; Brightness; Extraterrestrial measurements |
| Abstract |
In the quality evaluation of high dynamic range and wide color gamut (HDR/WCG) images, a number of works have concluded that native HDR metrics, such as HDR visual difference predictor (HDR-VDP), HDR video quality metric (HDR-VQM), or convolutional neural network (CNN)-based visibility metrics for HDR content, provide the best results. These metrics consider only the luminance component, but several color difference metrics have been specifically developed for, and validated with, HDR/WCG images. In this paper, we perform subjective evaluation experiments in a professional HDR/WCG production setting, under a real use case scenario. The results are quite relevant in that they show, firstly, that the performance of HDR metrics is worse than that of a classic, simple standard dynamic range (SDR) metric applied directly to the HDR content; and secondly, that the chrominance metrics specifically developed for HDR/WCG imaging have poor correlation with observer scores and are also outperformed by an SDR metric. Based on these findings, we show how a very simple framework for creating color HDR metrics, that uses only luminance SDR metrics, transfer functions, and classic color spaces, is able to consistently outperform, by a considerable margin, state-of-the-art HDR metrics on a varied set of HDR content, for both perceptual quantization (PQ) and Hybrid Log-Gamma (HLG) encoding, luminance and chroma distortions, and on different color spaces of common use. |
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| Notes |
600.161; 611.007 |
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no |
| Call Number |
Admin @ si @ SVG2022 |
Serial |
3683 |
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| Author |
Javier Vazquez; Graham D. Finlayson; Luis Herranz |
| Title |
Improving the perception of low-light enhanced images |
Type |
Journal Article |
| Year |
2024 |
Publication |
Optics Express |
Abbreviated Journal |
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| Volume |
32 |
Issue |
4 |
Pages |
5174-5190 |
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| Abstract |
Improving images captured under low-light conditions has become an important topic in computational color imaging, as it has a wide range of applications. Most current methods are either based on handcrafted features or on end-to-end training of deep neural networks that mostly focus on minimizing some distortion metric —such as PSNR or SSIM— on a set of training images. However, the minimization of distortion metrics does not mean that the results are optimal in terms of perception (i.e. perceptual quality). As an example, the perception-distortion trade-off states that, close to the optimal results, improving distortion results in worsening perception. This means that current low-light image enhancement methods —that focus on distortion minimization— cannot be optimal in the sense of obtaining a good image in terms of perception errors. In this paper, we propose a post-processing approach in which, given the original low-light image and the result of a specific method, we are able to obtain a result that resembles as much as possible that of the original method, but, at the same time, giving an improvement in the perception of the final image. More in detail, our method follows the hypothesis that in order to minimally modify the perception of an input image, any modification should be a combination of a local change in the shading across a scene and a global change in illumination color. We demonstrate the ability of our method quantitatively using perceptual blind image metrics such as BRISQUE, NIQE, or UNIQUE, and through user preference tests. |
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MACO |
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no |
| Call Number |
Admin @ si @ VFH2024 |
Serial |
4018 |
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| Author |
Mohamed Ali Souibgui; Alicia Fornes; Y.Kessentini; C.Tudor |
| Title |
A Few-shot Learning Approach for Historical Encoded Manuscript Recognition |
Type |
Conference Article |
| Year |
2021 |
Publication |
25th International Conference on Pattern Recognition |
Abbreviated Journal |
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Pages |
5413-5420 |
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Encoded (or ciphered) manuscripts are a special type of historical documents that contain encrypted text. The automatic recognition of this kind of documents is challenging because: 1) the cipher alphabet changes from one document to another, 2) there is a lack of annotated corpus for training and 3) touching symbols make the symbol segmentation difficult and complex. To overcome these difficulties, we propose a novel method for handwritten ciphers recognition based on few-shot object detection. Our method first detects all symbols of a given alphabet in a line image, and then a decoding step maps the symbol similarity scores to the final sequence of transcribed symbols. By training on synthetic data, we show that the proposed architecture is able to recognize handwritten ciphers with unseen alphabets. In addition, if few labeled pages with the same alphabet are used for fine tuning, our method surpasses existing unsupervised and supervised HTR methods for ciphers recognition. |
| Address |
Virtual; January 2021 |
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ICPR |
| Notes |
DAG; 600.121; 600.140 |
Approved |
no |
| Call Number |
Admin @ si @ SFK2021 |
Serial |
3449 |
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| Author |
Yaxing Wang; Joost Van de Weijer; Luis Herranz |
| Title |
Mix and match networks: encoder-decoder alignment for zero-pair image translation |
Type |
Conference Article |
| Year |
2018 |
Publication |
31st IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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Issue |
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Pages |
5467 - 5476 |
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We address the problem of image translation between domains or modalities for which no direct paired data is available (i.e. zero-pair translation). We propose mix and match networks, based on multiple encoders and decoders aligned in such a way that other encoder-decoder pairs can be composed at test time to perform unseen image translation tasks between domains or modalities for which explicit paired samples were not seen during training. We study the impact of autoencoders, side information and losses in improving the alignment and transferability of trained pairwise translation models to unseen translations. We show our approach is scalable and can perform colorization and style transfer between unseen combinations of domains. We evaluate our system in a challenging cross-modal setting where semantic segmentation is estimated from depth images, without explicit access to any depth-semantic segmentation training pairs. Our model outperforms baselines based on pix2pix and CycleGAN models. |
| Address |
Salt Lake City; USA; June 2018 |
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CVPR |
| Notes |
LAMP; 600.109; 600.106; 600.120 |
Approved |
no |
| Call Number |
Admin @ si @ WWH2018b |
Serial |
3131 |
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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 |
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Issue |
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Pages |
5471-5480 |
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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 |
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ICCV |
| Notes |
HUPBA; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ BMT2021 |
Serial |
3606 |
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| Author |
Jiaolong Xu; David Vazquez; Krystian Mikolajczyk; Antonio Lopez |
| Title |
Hierarchical online domain adaptation of deformable part-based models |
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Conference Article |
| Year |
2016 |
Publication |
IEEE International Conference on Robotics and Automation |
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5536-5541 |
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Domain Adaptation; Pedestrian Detection |
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We propose an online domain adaptation method for the deformable part-based model (DPM). The online domain adaptation is based on a two-level hierarchical adaptation tree, which consists of instance detectors in the leaf nodes and a category detector at the root node. Moreover, combined with a multiple object tracking procedure (MOT), our proposal neither requires target-domain annotated data nor revisiting the source-domain data for performing the source-to-target domain adaptation of the DPM. From a practical point of view this means that, given a source-domain DPM and new video for training on a new domain without object annotations, our procedure outputs a new DPM adapted to the domain represented by the video. As proof-of-concept we apply our proposal to the challenging task of pedestrian detection. In this case, each instance detector is an exemplar classifier trained online with only one pedestrian per frame. The pedestrian instances are collected by MOT and the hierarchical model is constructed dynamically according to the pedestrian trajectories. Our experimental results show that the adapted detector achieves the accuracy of recent supervised domain adaptation methods (i.e., requiring manually annotated targetdomain data), and improves the source detector more than 10 percentage points. |
| Address |
Stockholm; Sweden; May 2016 |
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ICRA |
| Notes |
ADAS; 600.085; 600.082; 600.076 |
Approved |
no |
| Call Number |
Admin @ si @ XVM2016 |
Serial |
2728 |
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| Author |
Lluis Garrido; M.Guerrieri; Laura Igual |
| Title |
Image Segmentation with Cage Active Contours |
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Journal Article |
| Year |
2015 |
Publication |
IEEE Transactions on Image Processing |
Abbreviated Journal |
TIP |
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24 |
Issue |
12 |
Pages |
5557 - 5566 |
| Keywords |
Level sets; Mean value coordinates; Parametrized active contours; level sets; mean value coordinates |
| Abstract |
In this paper, we present a framework for image segmentation based on parametrized active contours. The evolving contour is parametrized according to a reduced set of control points that form a closed polygon and have a clear visual interpretation. The parametrization, called mean value coordinates, stems from the techniques used in computer graphics to animate virtual models. Our framework allows to easily formulate region-based energies to segment an image. In particular, we present three different local region-based energy terms: 1) the mean model; 2) the Gaussian model; 3) and the histogram model. We show the behavior of our method on synthetic and real images and compare the performance with state-of-the-art level set methods. |
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1057-7149 |
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MILAB |
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no |
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Admin @ si @ GGI2015 |
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2673 |
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| Author |
M. Li; Xialei Liu; Joost Van de Weijer; Bogdan Raducanu |
| Title |
Learning to Rank for Active Learning: A Listwise Approach |
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Conference Article |
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2020 |
Publication |
25th International Conference on Pattern Recognition |
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5587-5594 |
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Active learning emerged as an alternative to alleviate the effort to label huge amount of data for data hungry applications (such as image/video indexing and retrieval, autonomous driving, etc.). The goal of active learning is to automatically select a number of unlabeled samples for annotation (according to a budget), based on an acquisition function, which indicates how valuable a sample is for training the model. The learning loss method is a task-agnostic approach which attaches a module to learn to predict the target loss of unlabeled data, and select data with the highest loss for labeling. In this work, we follow this strategy but we define the acquisition function as a learning to rank problem and rethink the structure of the loss prediction module, using a simple but effective listwise approach. Experimental results on four datasets demonstrate that our method outperforms recent state-of-the-art active learning approaches for both image classification and regression tasks. |
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Virtual; January 2021 |
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ICPR |
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LAMP; 600.120 |
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no |
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Admin @ si @ LLW2020a |
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3511 |
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| Author |
German Ros; J. Guerrero; Angel Sappa; Antonio Lopez |
| Title |
VSLAM pose initialization via Lie groups and Lie algebras optimization |
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Conference Article |
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2013 |
Publication |
Proceedings of IEEE International Conference on Robotics and Automation |
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5740 - 5747 |
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SLAM |
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We present a novel technique for estimating initial 3D poses in the context of localization and Visual SLAM problems. The presented approach can deal with noise, outliers and a large amount of input data and still performs in real time in a standard CPU. Our method produces solutions with an accuracy comparable to those produced by RANSAC but can be much faster when the percentage of outliers is high or for large amounts of input data. On the current work we propose to formulate the pose estimation as an optimization problem on Lie groups, considering their manifold structure as well as their associated Lie algebras. This allows us to perform a fast and simple optimization at the same time that conserve all the constraints imposed by the Lie group SE(3). Additionally, we present several key design concepts related with the cost function and its Jacobian; aspects that are critical for the good performance of the algorithm. |
| Address |
Karlsruhe; Germany; May 2013 |
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1050-4729 |
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978-1-4673-5641-1 |
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ICRA |
| Notes |
ADAS; 600.054; 600.055; 600.057 |
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no |
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Admin @ si @ RGS2013a; ADAS @ adas @ |
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2225 |
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| Author |
Axel Barroso-Laguna; Edgar Riba; Daniel Ponsa; Krystian Mikolajczyk |
| Title |
Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters |
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Conference Article |
| Year |
2019 |
Publication |
18th IEEE International Conference on Computer Vision |
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5835-5843 |
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We introduce a novel approach for keypoint detection task that combines handcrafted and learned CNN filters within a shallow multi-scale architecture. Handcrafted filters provide anchor structures for learned filters, which localize, score and rank repeatable features. Scale-space representation is used within the network to extract keypoints at different levels. We design a loss function to detect robust features that exist across a range of scales and to maximize the repeatability score. Our Key.Net model is trained on data synthetically created from ImageNet and evaluated on HPatches benchmark. Results show that our approach outperforms state-of-the-art detectors in terms of repeatability, matching performance and complexity. |
| Address |
Seul; Corea; October 2019 |
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ICCV |
| Notes |
MSIAU; 600.122 |
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no |
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Admin @ si @ BRP2019 |
Serial |
3290 |
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| Author |
Mikhail Mozerov; Joost Van de Weijer |
| Title |
Global Color Sparseness and a Local Statistics Prior for Fast Bilateral Filtering |
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Journal Article |
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2015 |
Publication |
IEEE Transactions on Image Processing |
Abbreviated Journal |
TIP |
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24 |
Issue |
12 |
Pages |
5842-5853 |
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The property of smoothing while preserving edges makes the bilateral filter a very popular image processing tool. However, its non-linear nature results in a computationally costly operation. Various works propose fast approximations to the bilateral filter. However, the majority does not generalize to vector input as is the case with color images. We propose a fast approximation to the bilateral filter for color images. The filter is based on two ideas. First, the number of colors, which occur in a single natural image, is limited. We exploit this color sparseness to rewrite the initial non-linear bilateral filter as a number of linear filter operations. Second, we impose a statistical prior to the image values that are locally present within the filter window. We show that this statistical prior leads to a closed-form solution of the bilateral filter. Finally, we combine both ideas into a single fast and accurate bilateral filter for color images. Experimental results show that our bilateral filter based on the local prior yields an extremely fast bilateral filter approximation, but with limited accuracy, which has potential application in real-time video filtering. Our bilateral filter, which combines color sparseness and local statistics, yields a fast and accurate bilateral filter approximation and obtains the state-of-the-art results. |
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1057-7149 |
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LAMP; 600.079;ISE |
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Admin @ si @ MoW2015b |
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2689 |
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| Author |
Chenshen Wu; Luis Herranz; Xialei Liu; Joost Van de Weijer; Bogdan Raducanu |
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Memory Replay GANs: Learning to Generate New Categories without Forgetting |
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Conference Article |
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2018 |
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32nd Annual Conference on Neural Information Processing Systems |
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5966-5976 |
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Previous works on sequential learning address the problem of forgetting in discriminative models. In this paper we consider the case of generative models. In particular, we investigate generative adversarial networks (GANs) in the task of learning new categories in a sequential fashion. We first show that sequential fine tuning renders the network unable to properly generate images from previous categories (ie forgetting). Addressing this problem, we propose Memory Replay GANs (MeRGANs), a conditional GAN framework that integrates a memory replay generator. We study two methods to prevent forgetting by leveraging these replays, namely joint training with replay and replay alignment. Qualitative and quantitative experimental results in MNIST, SVHN and LSUN datasets show that our memory replay approach can generate competitive images while significantly mitigating the forgetting of previous categories. |
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Montreal; Canada; December 2018 |
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NIPS |
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LAMP; 600.106; 600.109; 602.200; 600.120 |
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Admin @ si @ WHL2018 |
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3249 |
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