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| Author | Bojana Gajic; Ariel Amato; Ramon Baldrich; Joost Van de Weijer; Carlo Gatta |
 
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| Title | Area Under the ROC Curve Maximization for Metric Learning | Type | Conference Article | |||
| Year | 2022 | Publication | CVPR 2022 Workshop on Efficien Deep Learning for Computer Vision (ECV 2022, 5th Edition) | Abbreviated Journal | ||
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| Keywords | Training; Computer vision; Conferences; Area measurement; Benchmark testing; Pattern recognition | |||||
| Abstract | Most popular metric learning losses have no direct relation with the evaluation metrics that are subsequently applied to evaluate their performance. We hypothesize that training a metric learning model by maximizing the area under the ROC curve (which is a typical performance measure of recognition systems) can induce an implicit ranking suitable for retrieval problems. This hypothesis is supported by previous work that proved that a curve dominates in ROC space if and only if it dominates in Precision-Recall space. To test this hypothesis, we design and maximize an approximated, derivable relaxation of the area under the ROC curve. The proposed AUC loss achieves state-of-the-art results on two large scale retrieval benchmark datasets (Stanford Online Products and DeepFashion In-Shop). Moreover, the AUC loss achieves comparable performance to more complex, domain specific, state-of-the-art methods for vehicle re-identification. | |||||
| Address | New Orleans, USA; 20 June 2022 | |||||
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| Area | Expedition | Conference | CVPRW | |||
| Notes | CIC; LAMP; | Approved | no | |||
| Call Number | Admin @ si @ GAB2022 | Serial | 3700 | |||
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| Author | Bojana Gajic; Ramon Baldrich |
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| Title | Cross-domain fashion image retrieval | Type | Conference Article | |||
| Year | 2018 | Publication | CVPR 2018 Workshop on Women in Computer Vision (WiCV 2018, 4th Edition) | Abbreviated Journal | ||
| Volume | Issue | Pages | 19500-19502 | |||
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| Abstract | Cross domain image retrieval is a challenging task that implies matching images from one domain to their pairs from another domain. In this paper we focus on fashion image retrieval, which involves matching an image of a fashion item taken by users, to the images of the same item taken in controlled condition, usually by professional photographer. When facing this problem, we have different products
in train and test time, and we use triplet loss to train the network. We stress the importance of proper training of simple architecture, as well as adapting general models to the specific task. |
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| Address | Salt Lake City, USA; 22 June 2018 | |||||
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| Area | Expedition | Conference | CVPRW | |||
| Notes | CIC; 600.087 | Approved | no | |||
| Call Number | Admin @ si @ | Serial | 3709 | |||
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| Author | Bojana Gajic; Eduard Vazquez; Ramon Baldrich |
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| Title | Evaluation of Deep Image Descriptors for Texture Retrieval | Type | Conference Article | |||
| Year | 2017 | Publication | Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) | Abbreviated Journal | ||
| Volume | Issue | Pages | 251-257 | |||
| Keywords | Texture Representation; Texture Retrieval; Convolutional Neural Networks; Psychophysical Evaluation | |||||
| Abstract | The increasing complexity learnt in the layers of a Convolutional Neural Network has proven to be of great help for the task of classification. The topic has received great attention in recently published literature.
Nonetheless, just a handful of works study low-level representations, commonly associated with lower layers. In this paper, we explore recent findings which conclude, counterintuitively, the last layer of the VGG convolutional network is the best to describe a low-level property such as texture. To shed some light on this issue, we are proposing a psychophysical experiment to evaluate the adequacy of different layers of the VGG network for texture retrieval. Results obtained suggest that, whereas the last convolutional layer is a good choice for a specific task of classification, it might not be the best choice as a texture descriptor, showing a very poor performance on texture retrieval. Intermediate layers show the best performance, showing a good combination of basic filters, as in the primary visual cortex, and also a degree of higher level information to describe more complex textures. |
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| Address | Porto, Portugal; 27 February – 1 March 2017 | |||||
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| Area | Expedition | Conference | VISIGRAPP | |||
| Notes | CIC; 600.087 | Approved | no | |||
| Call Number | Admin @ si @ | Serial | 3710 | |||
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| Author | Marcos V Conde; Javier Vazquez; Michael S Brown; Radu TImofte |
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| 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 | Danna Xue; Javier Vazquez; Luis Herranz; Yang Zhang; Michael S Brown |
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| Title | Integrating High-Level Features for Consistent Palette-based Multi-image Recoloring | Type | Journal Article | |||
| Year | 2023 | Publication | Computer Graphics Forum | Abbreviated Journal | CGF | |
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| Abstract | Achieving visually consistent colors across multiple images is important when images are used in photo albums, websites, and brochures. Unfortunately, only a handful of methods address multi-image color consistency compared to one-to-one color transfer techniques. Furthermore, existing methods do not incorporate high-level features that can assist graphic designers in their work. To address these limitations, we introduce a framework that builds upon a previous palette-based color consistency method and incorporates three high-level features: white balance, saliency, and color naming. We show how these features overcome the limitations of the prior multi-consistency workflow and showcase the user-friendly nature of our framework. | |||||
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| Notes | CIC; MACO | Approved | no | |||
| Call Number | Admin @ si @ XVH2023 | Serial | 3883 | |||
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| Author | Jaykishan Patel; Alban Flachot; Javier Vazquez; David H. Brainard; Thomas S. A. Wallis; Marcus A. Brubaker; Richard F. Murray |
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| 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 | |
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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 | Approved | no | |||
| Call Number | Admin @ si @ PFV2023 | Serial | 3890 | |||
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| Author | Marcos V Conde; Florin Vasluianu; Javier Vazquez; Radu Timofte |
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| 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 | Danna Xue; Luis Herranz; Javier Vazquez; Yanning Zhang |
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| Title | Burst Perception-Distortion Tradeoff: Analysis and Evaluation | Type | Conference Article | |||
| Year | 2023 | Publication | IEEE International Conference on Acoustics, Speech and Signal Processing | Abbreviated Journal | ||
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| Abstract | Burst image restoration attempts to effectively utilize the complementary cues appearing in sequential images to produce a high-quality image. Most current methods use all the available images to obtain the reconstructed image. However, using more images for burst restoration is not always the best option regarding reconstruction quality and efficiency, as the images acquired by handheld imaging devices suffer from degradation and misalignment caused by the camera noise and shake. In this paper, we extend the perception-distortion tradeoff theory by introducing multiple-frame information. We propose the area of the unattainable region as a new metric for perception-distortion tradeoff evaluation and comparison. Based on this metric, we analyse the performance of burst restoration from the perspective of the perception-distortion tradeoff under both aligned bursts and misaligned bursts situations. Our analysis reveals the importance of inter-frame alignment for burst restoration and shows that the optimal burst length for the restoration model depends both on the degree of degradation and misalignment. | |||||
| Address | Rodhes Islands; Greece; June 2023 | |||||
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| Area | Expedition | Conference | ICASSP | |||
| Notes | CIC; MACO | Approved | no | |||
| Call Number | Admin @ si @ XHV2023 | Serial | 3909 | |||
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| Author | Yawei Li; Yulun Zhang; Radu Timofte; Luc Van Gool; Zhijun Tu; Kunpeng Du; Hailing Wang; Hanting Chen; Wei Li; Xiaofei Wang; Jie Hu; Yunhe Wang; Xiangyu Kong; Jinlong Wu; Dafeng Zhang; Jianxing Zhang; Shuai Liu; Furui Bai; Chaoyu Feng; Hao Wang; Yuqian Zhang; Guangqi Shao; Xiaotao Wang; Lei Lei; Rongjian Xu; Zhilu Zhang; Yunjin Chen; Dongwei Ren; Wangmeng Zuo; Qi Wu; Mingyan Han; Shen Cheng; Haipeng Li; Ting Jiang; Chengzhi Jiang; Xinpeng Li; Jinting Luo; Wenjie Lin; Lei Yu; Haoqiang Fan; Shuaicheng Liu; Aditya Arora; Syed Waqas Zamir; Javier Vazquez; Konstantinos G. Derpanis; Michael S. Brown; Hao Li; Zhihao Zhao; Jinshan Pan; Jiangxin Dong; Jinhui Tang; Bo Yang; Jingxiang Chen; Chenghua Li; Xi Zhang; Zhao Zhang; Jiahuan Ren; Zhicheng Ji; Kang Miao; Suiyi Zhao; Huan Zheng; YanYan Wei; Kangliang Liu; Xiangcheng Du; Sijie Liu; Yingbin Zheng; Xingjiao Wu; Cheng Jin; Rajeev Irny; Sriharsha Koundinya; Vighnesh Kamath; Gaurav Khandelwal; Sunder Ali Khowaja; Jiseok Yoon; Ik Hyun Lee; Shijie Chen; Chengqiang Zhao; Huabin Yang; Zhongjian Zhang; Junjia Huang; Yanru Zhang |
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| Title | NTIRE 2023 challenge on image denoising: Methods and results | Type | Conference Article | |||
| Year | 2023 | Publication | Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | ||
| Volume | Issue | Pages | 1904-1920 | |||
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| Abstract | This paper reviews the NTIRE 2023 challenge on image denoising (σ = 50) with a focus on the proposed solutions and results. The aim is to obtain a network design capable to produce high-quality results with the best performance measured by PSNR for image denoising. Independent additive white Gaussian noise (AWGN) is assumed and the noise level is 50. The challenge had 225 registered participants, and 16 teams made valid submissions. They gauge the state-of-the-art for image denoising. | |||||
| Address | Vancouver; Canada; June 2023 | |||||
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| Area | Expedition | Conference | CVPRW | |||
| Notes | MACO; CIC | Approved | no | |||
| Call Number | Admin @ si @ LZT2023 | Serial | 3910 | |||
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| Author | Justine Giroux; Mohammad Reza Karimi Dastjerdi; Yannick Hold-Geoffroy; Javier Vazquez; Jean François Lalonde |
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| Title | Towards a Perceptual Evaluation Framework for Lighting Estimation | Type | Conference Article | |||
| Year | 2024 | Publication | Arxiv | Abbreviated Journal | ||
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| Abstract | rogress in lighting estimation is tracked by computing existing image quality assessment (IQA) metrics on images from standard datasets. While this may appear to be a reasonable approach, we demonstrate that doing so does not correlate to human preference when the estimated lighting is used to relight a virtual scene into a real photograph. To study this, we design a controlled psychophysical experiment where human observers must choose their preference amongst rendered scenes lit using a set of lighting estimation algorithms selected from the recent literature, and use it to analyse how these algorithms perform according to human perception. Then, we demonstrate that none of the most popular IQA metrics from the literature, taken individually, correctly represent human perception. Finally, we show that by learning a combination of existing IQA metrics, we can more accurately represent human preference. This provides a new perceptual framework to help evaluate future lighting estimation algorithms. | |||||
| Address | Seattle; USA; June 2024 | |||||
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| Area | Expedition | Conference | CVPR | |||
| Notes | MACO; CIC | Approved | no | |||
| Call Number | Admin @ si @ GDH2024 | Serial | 3999 | |||
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| Author | Trevor Canham; Javier Vazquez; D Long; Richard F. Murray; Michael S Brown |
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| 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 | Partha Pratim Roy; Eduard Vazquez; Josep Llados; Ramon Baldrich; Umapada Pal |
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| Title | A System to Segment Text and Symbols from Color Maps | Type | Book Chapter | |||
| Year | 2008 | Publication | Graphics Recognition. Recent Advances and New Opportunities | Abbreviated Journal | ||
| Volume | 5046 | Issue | Pages | 245-256 | ||
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LNCS | |||
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| Notes | DAG;CIC | Approved | no | |||
| Call Number | CAT @ cat @ RVL2008 | Serial | 1005 | |||
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| Author | Eduard Vazquez; Joost Van de Weijer; Ramon Baldrich |
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| Title | Image Segmentation in the Presence of Shadows and Highligts | Type | Conference Article | |||
| Year | 2008 | Publication | 10th European Conference on Computer Vision | Abbreviated Journal | ||
| Volume | 5305 | Issue | Pages | 1–14 | ||
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| Address | Marseille (France) | |||||
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| Area | Expedition | Conference | ECCV | |||
| Notes | CAT;CIC | Approved | no | |||
| Call Number | CAT @ cat @ VVB2008b | Serial | 1013 | |||
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| Author | Antonio Lopez; J. Hilgenstock; A. Busse; Ramon Baldrich; Felipe Lumbreras; Joan Serrat |
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| Title | Nightime Vehicle Detecion for Intelligent Headlight Control | Type | Conference Article | |||
| Year | 2008 | Publication | Advanced Concepts for Intelligent Vision Systems, 10th International Conference, Proceedings, | Abbreviated Journal | ||
| Volume | 5259 | Issue | Pages | 113–124 | ||
| Keywords | Intelligent Headlights; vehicle detection | |||||
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| Address | Juan-les-Pins, France | |||||
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| Area | Expedition | Conference | ACIVS | |||
| Notes | ADAS;CIC | Approved | no | |||
| Call Number | ADAS @ adas @ LHB2008a | Serial | 1098 | |||
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| Author | Susana Alvarez; Anna Salvatella; Maria Vanrell; Xavier Otazu |
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| Title | 3D Texton Spaces for color-texture retrieval | Type | Conference Article | |||
| Year | 2010 | Publication | 7th International Conference on Image Analysis and Recognition | Abbreviated Journal | ||
| Volume | 6111 | Issue | Pages | 354–363 | ||
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| Abstract | Color and texture are visual cues of different nature, their integration in an useful visual descriptor is not an easy problem. One way to combine both features is to compute spatial texture descriptors independently on each color channel. Another way is to do the integration at the descriptor level. In this case the problem of normalizing both cues arises. In this paper we solve the latest problem by fusing color and texture through distances in texton spaces. Textons are the attributes of image blobs and they are responsible for texture discrimination as defined in Julesz’s Texton theory. We describe them in two low-dimensional and uniform spaces, namely, shape and color. The dissimilarity between color texture images is computed by combining the distances in these two spaces. Following this approach, we propose our TCD descriptor which outperforms current state of art methods in the two different approaches mentioned above, early combination with LBP and late combination with MPEG-7. This is done on an image retrieval experiment over a highly diverse texture dataset from Corel. | |||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | A.C. Campilho and M.S. Kamel | ||
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LNCS | |||
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| ISSN | 0302-9743 | ISBN | 978-3-642-13771-6 | Medium | ||
| Area | Expedition | Conference | ICIAR | |||
| Notes | CIC | Approved | no | |||
| Call Number | CAT @ cat @ ASV2010a | Serial | 1325 | |||
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| Author | Maria Vanrell; Naila Murray; Robert Benavente; C. Alejandro Parraga; Xavier Otazu; Ramon Baldrich |
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| Title | Perception Based Representations for Computational Colour | Type | Conference Article | |||
| Year | 2011 | Publication | 3rd International Workshop on Computational Color Imaging | Abbreviated Journal | ||
| Volume | 6626 | Issue | Pages | 16-30 | ||
| Keywords | colour perception, induction, naming, psychophysical data, saliency, segmentation | |||||
| Abstract | The perceived colour of a stimulus is dependent on multiple factors stemming out either from the context of the stimulus or idiosyncrasies of the observer. The complexity involved in combining these multiple effects is the main reason for the gap between classical calibrated colour spaces from colour science and colour representations used in computer vision, where colour is just one more visual cue immersed in a digital image where surfaces, shadows and illuminants interact seemingly out of control. With the aim to advance a few steps towards bridging this gap we present some results on computational representations of colour for computer vision. They have been developed by introducing perceptual considerations derived from the interaction of the colour of a point with its context. We show some techniques to represent the colour of a point influenced by assimilation and contrast effects due to the image surround and we show some results on how colour saliency can be derived in real images. We outline a model for automatic assignment of colour names to image points directly trained on psychophysical data. We show how colour segments can be perceptually grouped in the image by imposing shading coherence in the colour space. | |||||
| Address | Milan, Italy | |||||
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| Publisher | Springer-Verlag | Place of Publication | Editor | Raimondo Schettini, Shoji Tominaga, Alain Trémeau | ||
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| ISSN | ISBN | 978-3-642-20403-6 | Medium | |||
| Area | Expedition | Conference | CCIW | |||
| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ VMB2011 | Serial | 1733 | |||
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| Author | Aleksandr Setkov; Fabio Martinez Carillo; Michele Gouiffes; Christian Jacquemin; Maria Vanrell; Ramon Baldrich |
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| Title | DAcImPro: A Novel Database of Acquired Image Projections and Its Application to Object Recognition | Type | Conference Article | |||
| Year | 2015 | Publication | Advances in Visual Computing. Proceedings of 11th International Symposium, ISVC 2015 Part II | Abbreviated Journal | ||
| Volume | 9475 | Issue | Pages | 463-473 | ||
| Keywords | Projector-camera systems; Feature descriptors; Object recognition | |||||
| Abstract | Projector-camera systems are designed to improve the projection quality by comparing original images with their captured projections, which is usually complicated due to high photometric and geometric variations. Many research works address this problem using their own test data which makes it extremely difficult to compare different proposals. This paper has two main contributions. Firstly, we introduce a new database of acquired image projections (DAcImPro) that, covering photometric and geometric conditions and providing data for ground-truth computation, can serve to evaluate different algorithms in projector-camera systems. Secondly, a new object recognition scenario from acquired projections is presented, which could be of a great interest in such domains, as home video projections and public presentations. We show that the task is more challenging than the classical recognition problem and thus requires additional pre-processing, such as color compensation or projection area selection. | |||||
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| Publisher | Springer International Publishing | Place of Publication | Editor | |||
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LNCS | |||
| Series Volume | Series Issue | Edition | ||||
| ISSN | 0302-9743 | ISBN | 978-3-319-27862-9 | Medium | ||
| Area | Expedition | Conference | ISVC | |||
| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ SMG2015 | Serial | 2736 | |||
| Permanent link to this record | ||||||