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| Author | Xavier Soria; Edgar Riba; Angel Sappa | ||||
| Title | Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection | Type | Conference Article | ||
| Year | 2020 | Publication | IEEE Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
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| Abstract | This paper proposes a Deep Learning based edge detector, which is inspired on both HED (Holistically-Nested Edge Detection) and Xception networks. The proposed approach generates thin edge-maps that are plausible for human eyes; it can be used in any edge detection task without previous training or fine tuning process. As a second contribution, a large dataset with carefully annotated edges has been generated. This dataset has been used for training the proposed approach as well the state-of-the-art algorithms for comparisons. Quantitative and qualitative evaluations have been performed on different benchmarks showing improvements with the proposed method when F-measure of ODS and OIS are considered. | ||||
| Address | Aspen; USA; March 2020 | ||||
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| Area | Expedition | Conference | WACV | ||
| Notes | MSIAU; 600.130; 601.349; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ SRS2020 | Serial | 3434 | ||
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| Author | Ciprian Corneanu; Sergio Escalera; Aleix M. Martinez | ||||
| Title | Computing the Testing Error Without a Testing Set | Type | Conference Article | ||
| Year | 2020 | Publication | 33rd IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
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| Abstract | Oral. Paper award nominee.
Deep Neural Networks (DNNs) have revolutionized computer vision. We now have DNNs that achieve top (performance) results in many problems, including object recognition, facial expression analysis, and semantic segmentation, to name but a few. The design of the DNNs that achieve top results is, however, non-trivial and mostly done by trailand-error. That is, typically, researchers will derive many DNN architectures (i.e., topologies) and then test them on multiple datasets. However, there are no guarantees that the selected DNN will perform well in the real world. One can use a testing set to estimate the performance gap between the training and testing sets, but avoiding overfitting-to-thetesting-data is almost impossible. Using a sequestered testing dataset may address this problem, but this requires a constant update of the dataset, a very expensive venture. Here, we derive an algorithm to estimate the performance gap between training and testing that does not require any testing dataset. Specifically, we derive a number of persistent topology measures that identify when a DNN is learning to generalize to unseen samples. This allows us to compute the DNN’s testing error on unseen samples, even when we do not have access to them. We provide extensive experimental validation on multiple networks and datasets to demonstrate the feasibility of the proposed approach. |
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| Address | Virtual CVPR | ||||
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| Area | Expedition | Conference | CVPR | ||
| Notes | HuPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ CEM2020 | Serial | 3437 | ||
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| Author | Swathikiran Sudhakaran; Sergio Escalera; Oswald Lanz | ||||
| Title | Gate-Shift Networks for Video Action Recognition | Type | Conference Article | ||
| Year | 2020 | Publication | 33rd IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
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| Abstract | Deep 3D CNNs for video action recognition are designed to learn powerful representations in the joint spatio-temporal feature space. In practice however, because of the large number of parameters and computations involved, they may under-perform in the lack of sufficiently large datasets for training them at scale. In this paper we introduce spatial gating in spatial-temporal decomposition of 3D kernels. We implement this concept with Gate-Shift Module (GSM). GSM is lightweight and turns a 2D-CNN into a highly efficient spatio-temporal feature extractor. With GSM plugged in, a 2D-CNN learns to adaptively route features through time and combine them, at almost no additional parameters and computational overhead. We perform an extensive evaluation of the proposed module to study its effectiveness in video action recognition, achieving state-of-the-art results on Something Something-V1 and Diving48 datasets, and obtaining competitive results on EPIC-Kitchens with far less model complexity. | ||||
| Address | Virtual CVPR | ||||
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| Area | Expedition | Conference | CVPR | ||
| Notes | HuPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ SEL2020 | Serial | 3438 | ||
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| Author | Eduardo Aguilar; Bhalaji Nagarajan; Rupali Khatun; Marc Bolaños; Petia Radeva | ||||
| Title | Uncertainty Modeling and Deep Learning Applied to Food Image Analysis | Type | Conference Article | ||
| Year | 2020 | Publication | 13th International Joint Conference on Biomedical Engineering Systems and Technologies | Abbreviated Journal | |
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| Abstract | Recently, computer vision approaches specially assisted by deep learning techniques have shown unexpected advancements that practically solve problems that never have been imagined to be automatized like face recognition or automated driving. However, food image recognition has received a little effort in the Computer Vision community. In this project, we review the field of food image analysis and focus on how to combine with two challenging research lines: deep learning and uncertainty modeling. After discussing our methodology to advance in this direction, we comment potential research, social and economic impact of the research on food image analysis. | ||||
| Address | Villetta; Malta; February 2020 | ||||
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| Area | Expedition | Conference | BIODEVICES | ||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ ANK2020 | Serial | 3526 | ||
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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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Issue | Pages | 5413-5420 | ||
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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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| Area | Expedition | Conference | ICPR | ||
| Notes | DAG; 600.121; 600.140 | Approved | no | ||
| Call Number | Admin @ si @ SFK2021 | Serial | 3449 | ||
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| Author | Mohamed Ali Souibgui; Y.Kessentini; Alicia Fornes | ||||
| Title | A conditional GAN based approach for distorted camera captured documents recovery | Type | Conference Article | ||
| Year | 2020 | Publication | 4th Mediterranean Conference on Pattern Recognition and Artificial Intelligence | Abbreviated Journal | |
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| Address | Virtual; December 2020 | ||||
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| Area | Expedition | Conference | MedPRAI | ||
| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ SKF2020 | Serial | 3450 | ||
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| Author | Albert Berenguel; Oriol Ramos Terrades; Josep Llados; Cristina Cañero | ||||
| Title | Recurrent Comparator with attention models to detect counterfeit documents | Type | Conference Article | ||
| Year | 2019 | Publication | 15th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
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| Abstract | This paper is focused on the detection of counterfeit documents via the recurrent comparison of the security textured background regions of two images. The main contributions are twofold: first we apply and adapt a recurrent comparator architecture with attention mechanism to the counterfeit detection task, which constructs a representation of the background regions by recurrently condition the next observation, learning the difference between genuine and counterfeit images through iterative glimpses. Second we propose a new counterfeit document dataset to ensure the generalization of the learned model towards the detection of the lack of resolution during the counterfeit manufacturing. The presented network, outperforms state-of-the-art classification approaches for counterfeit detection as demonstrated in the evaluation. | ||||
| Address | Sidney; Australia; September 2019 | ||||
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.140; 600.121; 601.269 | Approved | no | ||
| Call Number | Admin @ si @ BRL2019 | Serial | 3456 | ||
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| Author | Fernando Vilariño | ||||
| Title | Library Living Lab, Numérisation 3D des chapiteaux du cloître de Saint-Cugat : des citoyens co- créant le nouveau patrimoine culturel numérique | Type | Conference Article | ||
| Year | 2019 | Publication | Intersectorialité et approche Living Labs. Entretiens Jacques-Cartier | Abbreviated Journal | |
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| Address | Montreal; Canada; December 2019 | ||||
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| Notes | MV; DAG; 600.140; 600.121;SIAI | Approved | no | ||
| Call Number | Admin @ si @ Vil2019a | Serial | 3457 | ||
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| Author | Fernando Vilariño | ||||
| Title | Public Libraries Exploring how technology transforms the cultural experience of people | Type | Conference Article | ||
| Year | 2019 | Publication | Workshop on Social Impact of AI. Open Living Lab Days Conference. | Abbreviated Journal | |
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| Address | Thessaloniki; Grecia; September 2019 | ||||
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| Notes | MV; DAG; 600.140; 600.121;SIAI | Approved | no | ||
| Call Number | Admin @ si @ Vil2019b | Serial | 3458 | ||
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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 | |
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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 | 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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