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Author | Fernando Vilariño | ||||
Title | Unveiling the Social Impact of AI | Type | Conference Article | ||
Year | 2020 | Publication | Workshop at Digital Living Lab Days Conference | Abbreviated Journal | |
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Address | September 2020 | ||||
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Notes | MV; DAG; 600.121; 600.140;SIAI | Approved | no | ||
Call Number | Admin @ si @ Vil2020 | Serial | 3459 | ||
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Author | Hassan Ahmed Sial; Ramon Baldrich; Maria Vanrell; Dimitris Samaras | ||||
Title | Light Direction and Color Estimation from Single Image with Deep Regression | Type | Conference Article | ||
Year | 2020 | Publication | London Imaging Conference | Abbreviated Journal | |
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Abstract | We present a method to estimate the direction and color of the scene light source from a single image. Our method is based on two main ideas: (a) we use a new synthetic dataset with strong shadow effects with similar constraints to the SID dataset; (b) we define a deep architecture trained on the mentioned dataset to estimate the direction and color of the scene light source. Apart from showing good performance on synthetic images, we additionally propose a preliminary procedure to obtain light positions of the Multi-Illumination dataset, and, in this way, we also prove that our trained model achieves good performance when it is applied to real scenes. | ||||
Address | Virtual; September 2020 | ||||
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Area | Expedition | Conference | LIM | ||
Notes | CIC; 600.118; 600.140; | Approved | no | ||
Call Number | Admin @ si @ SBV2020 | Serial | 3460 | ||
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Author | Sagnik Das; Hassan Ahmed Sial; Ke Ma; Ramon Baldrich; Maria Vanrell; Dimitris Samaras | ||||
Title | Intrinsic Decomposition of Document Images In-the-Wild | Type | Conference Article | ||
Year | 2020 | Publication | 31st British Machine Vision Conference | Abbreviated Journal | |
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Abstract | Automatic document content processing is affected by artifacts caused by the shape
of the paper, non-uniform and diverse color of lighting conditions. Fully-supervised methods on real data are impossible due to the large amount of data needed. Hence, the current state of the art deep learning models are trained on fully or partially synthetic images. However, document shadow or shading removal results still suffer because: (a) prior methods rely on uniformity of local color statistics, which limit their application on real-scenarios with complex document shapes and textures and; (b) synthetic or hybrid datasets with non-realistic, simulated lighting conditions are used to train the models. In this paper we tackle these problems with our two main contributions. First, a physically constrained learning-based method that directly estimates document reflectance based on intrinsic image formation which generalizes to challenging illumination conditions. Second, a new dataset that clearly improves previous synthetic ones, by adding a large range of realistic shading and diverse multi-illuminant conditions, uniquely customized to deal with documents in-the-wild. The proposed architecture works in two steps. First, a white balancing module neutralizes the color of the illumination on the input image. Based on the proposed multi-illuminant dataset we achieve a good white-balancing in really difficult conditions. Second, the shading separation module accurately disentangles the shading and paper material in a self-supervised manner where only the synthetic texture is used as a weak training signal (obviating the need for very costly ground truth with disentangled versions of shading and reflectance). The proposed approach leads to significant generalization of document reflectance estimation in real scenes with challenging illumination. We extensively evaluate on the real benchmark datasets available for intrinsic image decomposition and document shadow removal tasks. Our reflectance estimation scheme, when used as a pre-processing step of an OCR pipeline, shows a 21% improvement of character error rate (CER), thus, proving the practical applicability. The data and code will be available at: https://github.com/cvlab-stonybrook/DocIIW. |
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Address | Virtual; September 2020 | ||||
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Area | Expedition | Conference | BMVC | ||
Notes | CIC; 600.087; 600.140; 600.118 | Approved | no | ||
Call Number | Admin @ si @ DSM2020 | Serial | 3461 | ||
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Author | Sounak Dey; Pau Riba; Anjan Dutta; Josep Llados; Yi-Zhe Song | ||||
Title | Doodle to Search: Practical Zero-Shot Sketch-Based Image Retrieval | Type | Conference Article | ||
Year | 2019 | Publication | IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 2179-2188 | ||
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Abstract | In this paper, we investigate the problem of zero-shot sketch-based image retrieval (ZS-SBIR), where human sketches are used as queries to conduct retrieval of photos from unseen categories. We importantly advance prior arts by proposing a novel ZS-SBIR scenario that represents a firm step forward in its practical application. The new setting uniquely recognizes two important yet often neglected challenges of practical ZS-SBIR, (i) the large domain gap between amateur sketch and photo, and (ii) the necessity for moving towards large-scale retrieval. We first contribute to the community a novel ZS-SBIR dataset, QuickDraw-Extended, that consists of 330,000 sketches and 204,000 photos spanning across 110 categories. Highly abstract amateur human sketches are purposefully sourced to maximize the domain gap, instead of ones included in existing datasets that can often be semi-photorealistic. We then formulate a ZS-SBIR framework to jointly model sketches and photos into a common embedding space. A novel strategy to mine the mutual information among domains is specifically engineered to alleviate the domain gap. External semantic knowledge is further embedded to aid semantic transfer. We show that, rather surprisingly, retrieval performance significantly outperforms that of state-of-the-art on existing datasets that can already be achieved using a reduced version of our model. We further demonstrate the superior performance of our full model by comparing with a number of alternatives on the newly proposed dataset. The new dataset, plus all training and testing code of our model, will be publicly released to facilitate future research. | ||||
Address | Long beach; CA; USA; June 2019 | ||||
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Area | Expedition | Conference | CVPR | ||
Notes | DAG; 600.140; 600.121; 600.097 | Approved | no | ||
Call Number | Admin @ si @ DRD2019 | Serial | 3462 | ||
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Author | Fernando Vilariño | ||||
Title | 3D Scanning of Capitals at Library Living Lab | Type | Book Whole | ||
Year | 2019 | Publication | “Living Lab Projects 2019”. ENoLL. | Abbreviated Journal | |
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Notes | MV; DAG; 600.140; 600.121;SIAI | Approved | no | ||
Call Number | Admin @ si @ Vil2019c | Serial | 3463 | ||
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Author | Yaxing Wang; Abel Gonzalez-Garcia; Luis Herranz; Joost Van de Weijer | ||||
Title | Controlling biases and diversity in diverse image-to-image translation | Type | Journal Article | ||
Year | 2021 | Publication | Computer Vision and Image Understanding | Abbreviated Journal | CVIU |
Volume | 202 | Issue | Pages | 103082 | |
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Abstract | JCR 2019 Q2, IF=3.121
The task of unpaired image-to-image translation is highly challenging due to the lack of explicit cross-domain pairs of instances. We consider here diverse image translation (DIT), an even more challenging setting in which an image can have multiple plausible translations. This is normally achieved by explicitly disentangling content and style in the latent representation and sampling different styles codes while maintaining the image content. Despite the success of current DIT models, they are prone to suffer from bias. In this paper, we study the problem of bias in image-to-image translation. Biased datasets may add undesired changes (e.g. change gender or race in face images) to the output translations as a consequence of the particular underlying visual distribution in the target domain. In order to alleviate the effects of this problem we propose the use of semantic constraints that enforce the preservation of desired image properties. Our proposed model is a step towards unbiased diverse image-to-image translation (UDIT), and results in less unwanted changes in the translated images while still performing the wanted transformation. Experiments on several heavily biased datasets show the effectiveness of the proposed techniques in different domains such as faces, objects, and scenes. |
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Notes | LAMP; 600.141; 600.109; 600.147 | Approved | no | ||
Call Number | Admin @ si @ WGH2021 | Serial | 3464 | ||
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Author | Xinhang Song; Haitao Zeng; Sixian Zhang; Luis Herranz; Shuqiang Jiang | ||||
Title | Generalized Zero-shot Learning with Multi-source Semantic Embeddings for Scene Recognition | Type | Conference Article | ||
Year | 2020 | Publication | 28th ACM International Conference on Multimedia | Abbreviated Journal | |
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Abstract | Recognizing visual categories from semantic descriptions is a promising way to extend the capability of a visual classifier beyond the concepts represented in the training data (i.e. seen categories). This problem is addressed by (generalized) zero-shot learning methods (GZSL), which leverage semantic descriptions that connect them to seen categories (e.g. label embedding, attributes). Conventional GZSL are designed mostly for object recognition. In this paper we focus on zero-shot scene recognition, a more challenging setting with hundreds of categories where their differences can be subtle and often localized in certain objects or regions. Conventional GZSL representations are not rich enough to capture these local discriminative differences. Addressing these limitations, we propose a feature generation framework with two novel components: 1) multiple sources of semantic information (i.e. attributes, word embeddings and descriptions), 2) region descriptions that can enhance scene discrimination. To generate synthetic visual features we propose a two-step generative approach, where local descriptions are sampled and used as conditions to generate visual features. The generated features are then aggregated and used together with real features to train a joint classifier. In order to evaluate the proposed method, we introduce a new dataset for zero-shot scene recognition with multi-semantic annotations. Experimental results on the proposed dataset and SUN Attribute dataset illustrate the effectiveness of the proposed method. | ||||
Address | Virtual; October 2020 | ||||
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Area | Expedition | Conference | ACM | ||
Notes | LAMP; 600.141; 600.120 | Approved | no | ||
Call Number | Admin @ si @ SZZ2020 | Serial | 3465 | ||
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Author | Kai Wang; Luis Herranz; Anjan Dutta; Joost Van de Weijer | ||||
Title | Bookworm continual learning: beyond zero-shot learning and continual learning | Type | Conference Article | ||
Year | 2020 | Publication | Workshop TASK-CV 2020 | Abbreviated Journal | |
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Abstract | We propose bookworm continual learning(BCL), a flexible setting where unseen classes can be inferred via a semantic model, and the visual model can be updated continually. Thus BCL generalizes both continual learning (CL) and zero-shot learning (ZSL). We also propose the bidirectional imagination (BImag) framework to address BCL where features of both past and future classes are generated. We observe that conditioning the feature generator on attributes can actually harm the continual learning ability, and propose two variants (joint class-attribute conditioning and asymmetric generation) to alleviate this problem. | ||||
Address | Virtual; August 2020 | ||||
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Area | Expedition | Conference | ECCVW | ||
Notes | LAMP; 600.141; 600.120 | Approved | no | ||
Call Number | Admin @ si @ WHD2020 | Serial | 3466 | ||
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Author | Debora Gil; Antonio Esteban Lansaque; Agnes Borras; Esmitt Ramirez; Carles Sanchez | ||||
Title | Intraoperative Extraction of Airways Anatomy in VideoBronchoscopy | Type | Journal Article | ||
Year | 2020 | Publication | IEEE Access | Abbreviated Journal | ACCESS |
Volume | 8 | Issue | Pages | 159696 - 159704 | |
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Abstract | A main bottleneck in bronchoscopic biopsy sampling is to efficiently reach the lesion navigating across bronchial levels. Any guidance system should be able to localize the scope position during the intervention with minimal costs and alteration of clinical protocols. With the final goal of an affordable image-based guidance, this work presents a novel strategy to extract and codify the anatomical structure of bronchi, as well as, the scope navigation path from videobronchoscopy. Experiments using interventional data show that our method accurately identifies the bronchial structure. Meanwhile, experiments using simulated data verify that the extracted navigation path matches the 3D route. | ||||
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Notes | IAM; 600.139; 600.145 | Approved | no | ||
Call Number | Admin @ si @ GEB2020 | Serial | 3467 | ||
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Author | Saad Minhas; Aura Hernandez-Sabate; Shoaib Ehsan; Klaus McDonald Maier | ||||
Title | Effects of Non-Driving Related Tasks during Self-Driving mode | Type | Journal Article | ||
Year | 2022 | Publication | IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
Volume | 23 | Issue | 2 | Pages | 1391-1399 |
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Abstract | Perception reaction time and mental workload have proven to be crucial in manual driving. Moreover, in highly automated cars, where most of the research is focusing on Level 4 Autonomous driving, take-over performance is also a key factor when taking road safety into account. This study aims to investigate how the immersion in non-driving related tasks affects the take-over performance of drivers in given scenarios. The paper also highlights the use of virtual simulators to gather efficient data that can be crucial in easing the transition between manual and autonomous driving scenarios. The use of Computer Aided Simulations is of absolute importance in this day and age since the automotive industry is rapidly moving towards Autonomous technology. An experiment comprising of 40 subjects was performed to examine the reaction times of driver and the influence of other variables in the success of take-over performance in highly automated driving under different circumstances within a highway virtual environment. The results reflect the relationship between reaction times under different scenarios that the drivers might face under the circumstances stated above as well as the importance of variables such as velocity in the success on regaining car control after automated driving. The implications of the results acquired are important for understanding the criteria needed for designing Human Machine Interfaces specifically aimed towards automated driving conditions. Understanding the need to keep drivers in the loop during automation, whilst allowing drivers to safely engage in other non-driving related tasks is an important research area which can be aided by the proposed study. | ||||
Address | Feb. 2022 | ||||
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Notes | IAM; 600.139; 600.145 | Approved | no | ||
Call Number | Admin @ si @ MHE2022 | Serial | 3468 | ||
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Author | Akhil Gurram; Ahmet Faruk Tuna; Fengyi Shen; Onay Urfalioglu; Antonio Lopez | ||||
Title | Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Supervision | Type | Journal Article | ||
Year | 2021 | Publication | IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
Volume | 23 | Issue | 8 | Pages | 12738-12751 |
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Abstract | Depth information is essential for on-board perception in autonomous driving and driver assistance. Monocular depth estimation (MDE) is very appealing since it allows for appearance and depth being on direct pixelwise correspondence without further calibration. Best MDE models are based on Convolutional Neural Networks (CNNs) trained in a supervised manner, i.e., assuming pixelwise ground truth (GT). Usually, this GT is acquired at training time through a calibrated multi-modal suite of sensors. However, also using only a monocular system at training time is cheaper and more scalable. This is possible by relying on structure-from-motion (SfM) principles to generate self-supervision. Nevertheless, problems of camouflaged objects, visibility changes, static-camera intervals, textureless areas, and scale ambiguity, diminish the usefulness of such self-supervision. In this paper, we perform monocular depth estimation by virtual-world supervision (MonoDEVS) and real-world SfM self-supervision. We compensate the SfM self-supervision limitations by leveraging virtual-world images with accurate semantic and depth supervision and addressing the virtual-to-real domain gap. Our MonoDEVSNet outperforms previous MDE CNNs trained on monocular and even stereo sequences. | ||||
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Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ GTS2021 | Serial | 3598 | ||
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Author | Debora Gil; Guillermo Torres | ||||
Title | A multi-shape loss function with adaptive class balancing for the segmentation of lung structures | Type | Conference Article | ||
Year | 2020 | Publication | 34th International Congress and Exhibition on Computer Assisted Radiology & Surgery | Abbreviated Journal | |
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Address | Virtual; June 2020 | ||||
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Area | Expedition | Conference | CARS | ||
Notes | IAM; 600.139; 600.145 | Approved | no | ||
Call Number | Admin @ si @ GiT2020 | Serial | 3472 | ||
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Author | Debora Gil; Oriol Ramos Terrades; Raquel Perez | ||||
Title | Topological Radiomics (TOPiomics): Early Detection of Genetic Abnormalities in Cancer Treatment Evolution | Type | Conference Article | ||
Year | 2020 | Publication | Women in Geometry and Topology | Abbreviated Journal | |
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Address | Barcelona; September 2019 | ||||
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Notes | IAM; DAG; 600.139; 600.145; 600.121 | Approved | no | ||
Call Number | Admin @ si @ GRP2020 | Serial | 3473 | ||
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Author | Debora Gil; Katerine Diaz; Carles Sanchez; Aura Hernandez-Sabate | ||||
Title | Early Screening of SARS-CoV-2 by Intelligent Analysis of X-Ray Images | Type | Miscellaneous | ||
Year | 2020 | Publication | Arxiv | Abbreviated Journal | |
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Abstract | Future SARS-CoV-2 virus outbreak COVID-XX might possibly occur during the next years. However the pathology in humans is so recent that many clinical aspects, like early detection of complications, side effects after recovery or early screening, are currently unknown. In spite of the number of cases of COVID-19, its rapid spread putting many sanitary systems in the edge of collapse has hindered proper collection and analysis of the data related to COVID-19 clinical aspects. We describe an interdisciplinary initiative that integrates clinical research, with image diagnostics and the use of new technologies such as artificial intelligence and radiomics with the aim of clarifying some of SARS-CoV-2 open questions. The whole initiative addresses 3 main points: 1) collection of standardize data including images, clinical data and analytics; 2) COVID-19 screening for its early diagnosis at primary care centers; 3) define radiomic signatures of COVID-19 evolution and associated pathologies for the early treatment of complications. In particular, in this paper we present a general overview of the project, the experimental design and first results of X-ray COVID-19 detection using a classic approach based on HoG and feature selection. Our experiments include a comparison to some recent methods for COVID-19 screening in X-Ray and an exploratory analysis of the feasibility of X-Ray COVID-19 screening. Results show that classic approaches can outperform deep-learning methods in this experimental setting, indicate the feasibility of early COVID-19 screening and that non-COVID infiltration is the group of patients most similar to COVID-19 in terms of radiological description of X-ray. Therefore, an efficient COVID-19 screening should be complemented with other clinical data to better discriminate these cases. | ||||
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Notes | IAM; 600.139; 600.145; 601.337 | Approved | no | ||
Call Number | Admin @ si @ GDS2020 | Serial | 3474 | ||
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Author | Oriol Ramos Terrades; Albert Berenguel; Debora Gil | ||||
Title | A flexible outlier detector based on a topology given by graph communities | Type | Miscellaneous | ||
Year | 2020 | Publication | Arxiv | Abbreviated Journal | |
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Abstract | Outlier, or anomaly, detection is essential for optimal performance of machine learning methods and statistical predictive models. It is not just a technical step in a data cleaning process but a key topic in many fields such as fraudulent document detection, in medical applications and assisted diagnosis systems or detecting security threats. In contrast to population-based methods, neighborhood based local approaches are simple flexible methods that have the potential to perform well in small sample size unbalanced problems. However, a main concern of local approaches is the impact that the computation of each sample neighborhood has on the method performance. Most approaches use a distance in the feature space to define a single neighborhood that requires careful selection of several parameters. This work presents a local approach based on a local measure of the heterogeneity of sample labels in the feature space considered as a topological manifold. Topology is computed using the communities of a weighted graph codifying mutual nearest neighbors in the feature space. This way, we provide with a set of multiple neighborhoods able to describe the structure of complex spaces without parameter fine tuning. The extensive experiments on real-world data sets show that our approach overall outperforms, both, local and global strategies in multi and single view settings. | ||||
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Notes | IAM; DAG; 600.139; 600.145; 600.140; 600.121 | Approved | no | ||
Call Number | Admin @ si @ RBG2020 | Serial | 3475 | ||
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