| Records |
| 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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| Volume |
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Issue |
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Pages  |
5467 - 5476 |
| Keywords |
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| Abstract |
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 |
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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| Volume |
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Issue |
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Pages |
5413-5420 |
| Keywords |
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| Abstract |
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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Conference |
ICPR |
| Notes |
DAG; 600.121; 600.140 |
Approved |
no |
| Call Number |
Admin @ si @ SFK2021 |
Serial |
3449 |
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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 |
Approved |
no |
| Call Number |
Admin @ si @ SVG2022 |
Serial |
3683 |
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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 |
| Keywords |
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| Abstract |
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. |
| Address |
Long beach; California; USA; June 2019 |
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Conference |
CVPR |
| Notes |
DAG; 600.141; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ DuA2019 |
Serial |
3268 |
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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 |
| Keywords |
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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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| ISSN |
1057-7149 |
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| Notes |
ADAS |
Approved |
no |
| Call Number |
Admin @ si @ RoS2013 |
Serial |
2665 |
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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 |
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| Volume |
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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. |
| Address |
Virtual; June 2021 |
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Conference |
CVPR |
| Notes |
LAMP; 600.120 |
Approved |
no |
| Call Number |
Admin @ si @ YHC2021 |
Serial |
3569 |
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| Author |
Jaykishan Patel; Alban Flachot; Javier Vazquez; David H. Brainard; Thomas S. A. Wallis; Marcus A. Brubaker; Richard F. Murray |
| Title |
A deep convolutional neural network trained to infer surface reflectance is deceived by mid-level lightness illusions |
Type |
Journal Article |
| Year |
2023 |
Publication |
Journal of Vision |
Abbreviated Journal |
JV |
| Volume |
23 |
Issue |
9 |
Pages |
4817-4817 |
| Keywords |
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| Abstract |
A long-standing view is that lightness illusions are by-products of strategies employed by the visual system to stabilize its perceptual representation of surface reflectance against changes in illumination. Computationally, one such strategy is to infer reflectance from the retinal image, and to base the lightness percept on this inference. CNNs trained to infer reflectance from images have proven successful at solving this problem under limited conditions. To evaluate whether these CNNs provide suitable starting points for computational models of human lightness perception, we tested a state-of-the-art CNN on several lightness illusions, and compared its behaviour to prior measurements of human performance. We trained a CNN (Yu & Smith, 2019) to infer reflectance from luminance images. The network had a 30-layer hourglass architecture with skip connections. We trained the network via supervised learning on 100K images, rendered in Blender, each showing randomly placed geometric objects (surfaces, cubes, tori, etc.), with random Lambertian reflectance patterns (solid, Voronoi, or low-pass noise), under randomized point+ambient lighting. The renderer also provided the ground-truth reflectance images required for training. After training, we applied the network to several visual illusions. These included the argyle, Koffka-Adelson, snake, White’s, checkerboard assimilation, and simultaneous contrast illusions, along with their controls where appropriate. The CNN correctly predicted larger illusions in the argyle, Koffka-Adelson, and snake images than in their controls. It also correctly predicted an assimilation effect in White's illusion. It did not, however, account for the checkerboard assimilation or simultaneous contrast effects. These results are consistent with the view that at least some lightness phenomena are by-products of a rational approach to inferring stable representations of physical properties from intrinsically ambiguous retinal images. Furthermore, they suggest that CNN models may be a promising starting point for new models of human lightness perception. |
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| Notes |
MACO; CIC |
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no |
| Call Number |
Admin @ si @ PFV2023 |
Serial |
3890 |
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| Author |
Jaume Garcia; Albert Andaluz; Debora Gil; Francesc Carreras |
| Title |
Decoupled External Forces in a Predictor-Corrector Segmentation Scheme for LV Contours in Tagged MR Images |
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Conference Article |
| Year |
2010 |
Publication |
32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
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4805-4808 |
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Computation of functional regional scores requires proper identification of LV contours. On one hand, manual segmentation is robust, but it is time consuming and requires high expertise. On the other hand, the tag pattern in TMR sequences is a problem for automatic segmentation of LV boundaries. We propose a segmentation method based on a predictorcorrector (Active Contours – Shape Models) scheme. Special stress is put in the definition of the AC external forces. First, we introduce a semantic description of the LV that discriminates myocardial tissue by using texture and motion descriptors. Second, in order to ensure convergence regardless of the initial contour, the external energy is decoupled according to the orientation of the edges in the image potential. We have validated the model in terms of error in segmented contours and accuracy of regional clinical scores. |
| Address |
Buenos Aires (Argentina) |
| Corporate Author |
IEEE EMB |
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1557-170X |
ISBN |
978-1-4244-4123-5 |
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EMBC |
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IAM |
Approved |
no |
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IAM @ iam @ GAG2010 |
Serial |
1514 |
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| Author |
Zhijie Fang; Antonio Lopez |
| Title |
Intention Recognition of Pedestrians and Cyclists by 2D Pose Estimation |
Type |
Journal Article |
| Year |
2019 |
Publication |
IEEE Transactions on Intelligent Transportation Systems |
Abbreviated Journal |
TITS |
| Volume |
21 |
Issue |
11 |
Pages |
4773 - 4783 |
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| Abstract |
Anticipating the intentions of vulnerable road users (VRUs) such as pedestrians and cyclists is critical for performing safe and comfortable driving maneuvers. This is the case for human driving and, thus, should be taken into account by systems providing any level of driving assistance, from advanced driver assistant systems (ADAS) to fully autonomous vehicles (AVs). In this paper, we show how the latest advances on monocular vision-based human pose estimation, i.e. those relying on deep Convolutional Neural Networks (CNNs), enable to recognize the intentions of such VRUs. In the case of cyclists, we assume that they follow traffic rules to indicate future maneuvers with arm signals. In the case of pedestrians, no indications can be assumed. Instead, we hypothesize that the walking pattern of a pedestrian allows to determine if he/she has the intention of crossing the road in the path of the ego-vehicle, so that the ego-vehicle must maneuver accordingly (e.g. slowing down or stopping). In this paper, we show how the same methodology can be used for recognizing pedestrians and cyclists' intentions. For pedestrians, we perform experiments on the JAAD dataset. For cyclists, we did not found an analogous dataset, thus, we created our own one by acquiring and annotating videos which we share with the research community. Overall, the proposed pipeline provides new state-of-the-art results on the intention recognition of VRUs. |
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| Notes |
ADAS; 600.118 |
Approved |
no |
| Call Number |
Admin @ si @ FaL2019 |
Serial |
3305 |
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| Author |
Ciprian Corneanu; Meysam Madadi; Sergio Escalera; Aleix M. Martinez |
| Title |
What does it mean to learn in deep networks? And, how does one detect adversarial attacks? |
Type |
Conference Article |
| Year |
2019 |
Publication |
32nd IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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Issue |
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Pages |
4752-4761 |
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| Abstract |
The flexibility and high-accuracy of Deep Neural Networks (DNNs) has transformed computer vision. But, the fact that we do not know when a specific DNN will work and when it will fail has resulted in a lack of trust. A clear example is self-driving cars; people are uncomfortable sitting in a car driven by algorithms that may fail under some unknown, unpredictable conditions. Interpretability and explainability approaches attempt to address this by uncovering what a DNN models, i.e., what each node (cell) in the network represents and what images are most likely to activate it. This can be used to generate, for example, adversarial attacks. But these approaches do not generally allow us to determine where a DNN will succeed or fail and why. i.e., does this learned representation generalize to unseen samples? Here, we derive a novel approach to define what it means to learn in deep networks, and how to use this knowledge to detect adversarial attacks. We show how this defines the ability of a network to generalize to unseen testing samples and, most importantly, why this is the case. |
| Address |
California; June 2019 |
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CVPR |
| Notes |
HuPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ CME2019 |
Serial |
3332 |
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| Author |
Felipe Codevilla; Matthias Muller; Antonio Lopez; Vladlen Koltun; Alexey Dosovitskiy |
| Title |
End-to-end Driving via Conditional Imitation Learning |
Type |
Conference Article |
| Year |
2018 |
Publication |
IEEE International Conference on Robotics and Automation |
Abbreviated Journal |
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Issue |
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Pages |
4693 - 4700 |
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Deep networks trained on demonstrations of human driving have learned to follow roads and avoid obstacles. However, driving policies trained via imitation learning cannot be controlled at test time. A vehicle trained end-to-end to imitate an expert cannot be guided to take a specific turn at an upcoming intersection. This limits the utility of such systems. We propose to condition imitation learning on high-level command input. At test time, the learned driving policy functions as a chauffeur that handles sensorimotor coordination but continues to respond to navigational commands. We evaluate different architectures for conditional imitation learning in vision-based driving. We conduct experiments in realistic three-dimensional simulations of urban driving and on a 1/5 scale robotic truck that is trained to drive in a residential area. Both systems drive based on visual input yet remain responsive to high-level navigational commands. The supplementary video can be viewed at this https URL |
| Address |
Brisbane; Australia; May 2018 |
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ICRA |
| Notes |
ADAS; 600.116; 600.124; 600.118 |
Approved |
no |
| Call Number |
Admin @ si @ CML2018 |
Serial |
3108 |
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| Author |
Angel Morera; Angel Sanchez; A. Belen Moreno; Angel Sappa; Jose F. Velez |
| Title |
SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities |
Type |
Journal Article |
| Year |
2020 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
| Volume |
20 |
Issue |
16 |
Pages |
4587 |
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| Abstract |
This work compares Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO) deep neural networks for the outdoor advertisement panel detection problem by handling multiple and combined variabilities in the scenes. Publicity panel detection in images offers important advantages both in the real world as well as in the virtual one. For example, applications like Google Street View can be used for Internet publicity and when detecting these ads panels in images, it could be possible to replace the publicity appearing inside the panels by another from a funding company. In our experiments, both SSD and YOLO detectors have produced acceptable results under variable sizes of panels, illumination conditions, viewing perspectives, partial occlusion of panels, complex background and multiple panels in scenes. Due to the difficulty of finding annotated images for the considered problem, we created our own dataset for conducting the experiments. The major strength of the SSD model was the almost elimination of False Positive (FP) cases, situation that is preferable when the publicity contained inside the panel is analyzed after detecting them. On the other side, YOLO produced better panel localization results detecting a higher number of True Positive (TP) panels with a higher accuracy. Finally, a comparison of the two analyzed object detection models with different types of semantic segmentation networks and using the same evaluation metrics is also included. |
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MSIAU; 600.130; 601.349; 600.122 |
Approved |
no |
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Admin @ si @ MSM2020 |
Serial |
3452 |
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| Author |
Xinhang Song; Shuqiang Jiang; Luis Herranz |
| Title |
Combining Models from Multiple Sources for RGB-D Scene Recognition |
Type |
Conference Article |
| Year |
2017 |
Publication |
26th International Joint Conference on Artificial Intelligence |
Abbreviated Journal |
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Issue |
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Pages |
4523-4529 |
| Keywords |
Robotics and Vision; Vision and Perception |
| Abstract |
Depth can complement RGB with useful cues about object volumes and scene layout. However, RGB-D image datasets are still too small for directly training deep convolutional neural networks (CNNs), in contrast to the massive monomodal RGB datasets. Previous works in RGB-D recognition typically combine two separate networks for RGB and depth data, pretrained with a large RGB dataset and then fine tuned to the respective target RGB and depth datasets. These approaches have several limitations: 1) only use low-level filters learned from RGB data, thus not being able to exploit properly depth-specific patterns, and 2) RGB and depth features are only combined at high-levels but rarely at lower-levels. In this paper, we propose a framework that leverages both knowledge acquired from large RGB datasets together with depth-specific cues learned from the limited depth data, obtaining more effective multi-source and multi-modal representations. We propose a multi-modal combination method that selects discriminative combinations of layers from the different source models and target modalities, capturing both high-level properties of the task and intrinsic low-level properties of both modalities. |
| Address |
Melbourne; Australia; August 2017 |
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Medium |
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| Area |
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Expedition |
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Conference |
IJCAI |
| Notes |
LAMP; 600.120 |
Approved |
no |
| Call Number |
Admin @ si @ SJH2017b |
Serial |
2966 |
| Permanent link to this record |
| |
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| |
| Author |
Klara Janousckova; Jiri Matas; Lluis Gomez; Dimosthenis Karatzas |
| Title |
Text Recognition – Real World Data and Where to Find Them |
Type |
Conference Article |
| Year |
2020 |
Publication |
25th International Conference on Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
4489-4496 |
| Keywords |
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| Abstract |
We present a method for exploiting weakly annotated images to improve text extraction pipelines. The approach uses an arbitrary end-to-end text recognition system to obtain text region proposals and their, possibly erroneous, transcriptions. The method includes matching of imprecise transcriptions to weak annotations and an edit distance guided neighbourhood search. It produces nearly error-free, localised instances of scene text, which we treat as “pseudo ground truth” (PGT). The method is applied to two weakly-annotated datasets. Training with the extracted PGT consistently improves the accuracy of a state of the art recognition model, by 3.7% on average, across different benchmark datasets (image domains) and 24.5% on one of the weakly annotated datasets 1 1 Acknowledgements. The authors were supported by Czech Technical University student grant SGS20/171/0HK3/3TJ13, the MEYS VVV project CZ.02.1.01/0.010.0J16 019/0000765 Research Center for Informatics, the Spanish Research project TIN2017-89779-P and the CERCA Programme / Generalitat de Catalunya. |
| Address |
Virtual; January 2021 |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
ICPR |
| Notes |
DAG; 600.121; 600.129 |
Approved |
no |
| Call Number |
Admin @ si @ JMG2020 |
Serial |
3557 |
| Permanent link to this record |
| |
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| |
| Author |
Joan Marti; Jose Miguel Benedi; Ana Maria Mendonça; Joan Serrat |
| Title |
Pattern Recognition and Image Analysis |
Type |
Book Whole |
| Year |
2007 |
Publication |
3rd Iberian Conference |
Abbreviated Journal |
|
| Volume |
6669 |
Issue |
|
Pages |
4477-4478 |
| Keywords |
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| Abstract |
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| Address |
Girona (Spain) |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
|
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
IbPRIA |
| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ MBM2007 |
Serial |
994 |
| Permanent link to this record |
