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Armin Mehri; Parichehr Behjati Ardakani; Angel Sappa |
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MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution |
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Conference Article |
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2021 |
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IEEE Winter Conference on Applications of Computer Vision |
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2703-2712 |
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Lightweight super resolution networks have extremely importance for real-world applications. In recent years several SR deep learning approaches with outstanding achievement have been introduced by sacrificing memory and computational cost. To overcome this problem, a novel lightweight super resolution network is proposed, which improves the SOTA performance in lightweight SR and performs roughly similar to computationally expensive networks. Multi-Path Residual Network designs with a set of Residual concatenation Blocks stacked with Adaptive Residual Blocks: ($i$) to adaptively extract informative features and learn more expressive spatial context information; ($ii$) to better leverage multi-level representations before up-sampling stage; and ($iii$) to allow an efficient information and gradient flow within the network. The proposed architecture also contains a new attention mechanism, Two-Fold Attention Module, to maximize the representation ability of the model. Extensive experiments show the superiority of our model against other SOTA SR approaches. |
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Virtual; January 2021 |
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MSIAU; 600.130; 600.122 |
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Admin @ si @ MAS2021b |
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3582 |
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Armin Mehri; Parichehr Behjati Ardakani; Angel Sappa |
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Title |
LiNet: A Lightweight Network for Image Super Resolution |
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2021 |
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25th International Conference on Pattern Recognition |
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7196-7202 |
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This paper proposes a new lightweight network, LiNet, that enhancing technical efficiency in lightweight super resolution and operating approximately like very large and costly networks in terms of number of network parameters and operations. The proposed architecture allows the network to learn more abstract properties by avoiding low-level information via multiple links. LiNet introduces a Compact Dense Module, which contains set of inner and outer blocks, to efficiently extract meaningful information, to better leverage multi-level representations before upsampling stage, and to allow an efficient information and gradient flow within the network. Experiments on benchmark datasets show that the proposed LiNet achieves favorable performance against lightweight state-of-the-art methods. |
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Virtual; January 2021 |
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MSIAU; 600.130; 600.122 |
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Admin @ si @ MAS2021a |
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3583 |
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Hannes Mueller; Andre Groeger; Jonathan Hersh; Andrea Matranga; Joan Serrat |
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Monitoring war destruction from space using machine learning |
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2021 |
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Proceedings of the National Academy of Sciences of the United States of America |
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PNAS |
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118 |
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23 |
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e2025400118 |
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Existing data on building destruction in conflict zones rely on eyewitness reports or manual detection, which makes it generally scarce, incomplete, and potentially biased. This lack of reliable data imposes severe limitations for media reporting, humanitarian relief efforts, human-rights monitoring, reconstruction initiatives, and academic studies of violent conflict. This article introduces an automated method of measuring destruction in high-resolution satellite images using deep-learning techniques combined with label augmentation and spatial and temporal smoothing, which exploit the underlying spatial and temporal structure of destruction. As a proof of concept, we apply this method to the Syrian civil war and reconstruct the evolution of damage in major cities across the country. Our approach allows generating destruction data with unprecedented scope, resolution, and frequency—and makes use of the ever-higher frequency at which satellite imagery becomes available. |
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ADAS; 600.118 |
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Admin @ si @ MGH2021 |
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3584 |
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Zhengying Liu; Adrien Pavao; Zhen Xu; Sergio Escalera; Fabio Ferreira; Isabelle Guyon; Sirui Hong; Frank Hutter; Rongrong Ji; Julio C. S. Jacques Junior; Ge Li; Marius Lindauer; Zhipeng Luo; Meysam Madadi; Thomas Nierhoff; Kangning Niu; Chunguang Pan; Danny Stoll; Sebastien Treguer; Jin Wang; Peng Wang; Chenglin Wu; Youcheng Xiong; Arber Zela; Yang Zhang |
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Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019 |
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2021 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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43 |
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9 |
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3108 - 3125 |
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This paper reports the results and post-challenge analyses of ChaLearn's AutoDL challenge series, which helped sorting out a profusion of AutoML solutions for Deep Learning (DL) that had been introduced in a variety of settings, but lacked fair comparisons. All input data modalities (time series, images, videos, text, tabular) were formatted as tensors and all tasks were multi-label classification problems. Code submissions were executed on hidden tasks, with limited time and computational resources, pushing solutions that get results quickly. In this setting, DL methods dominated, though popular Neural Architecture Search (NAS) was impractical. Solutions relied on fine-tuned pre-trained networks, with architectures matching data modality. Post-challenge tests did not reveal improvements beyond the imposed time limit. While no component is particularly original or novel, a high level modular organization emerged featuring a “meta-learner”, “data ingestor”, “model selector”, “model/learner”, and “evaluator”. This modularity enabled ablation studies, which revealed the importance of (off-platform) meta-learning, ensembling, and efficient data management. Experiments on heterogeneous module combinations further confirm the (local) optimality of the winning solutions. Our challenge legacy includes an ever-lasting benchmark (http://autodl.chalearn.org), the open-sourced code of the winners, and a free “AutoDL self-service.” |
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HUPBA; no proj |
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Admin @ si @ LPX2021 |
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3587 |
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Albin Soutif; Marc Masana; Joost Van de Weijer; Bartlomiej Twardowski |
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On the importance of cross-task features for class-incremental learning |
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Conference Article |
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2021 |
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Theory and Foundation of continual learning workshop of ICML |
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In class-incremental learning, an agent with limited resources needs to learn a sequence of classification tasks, forming an ever growing classification problem, with the constraint of not being able to access data from previous tasks. The main difference with task-incremental learning, where a task-ID is available at inference time, is that the learner also needs to perform crosstask discrimination, i.e. distinguish between classes that have not been seen together. Approaches to tackle this problem are numerous and mostly make use of an external memory (buffer) of non-negligible size. In this paper, we ablate the learning of crosstask features and study its influence on the performance of basic replay strategies used for class-IL. We also define a new forgetting measure for class-incremental learning, and see that forgetting is not the principal cause of low performance. Our experimental results show that future algorithms for class-incremental learning should not only prevent forgetting, but also aim to improve the quality of the cross-task features. This is especially important when the number of classes per task is small. |
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Virtual; July 2021 |
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ICMLW |
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LAMP |
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Admin @ si @ SMW2021 |
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3588 |
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Sonia Baeza; R.Domingo; M.Salcedo; G.Moragas; J.Deportos; I.Garcia Olive; Carles Sanchez; Debora Gil; Antoni Rosell |
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Artificial Intelligence to Optimize Pulmonary Embolism Diagnosis During Covid-19 Pandemic by Perfusion SPECT/CT, a Pilot Study |
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2021 |
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American Journal of Respiratory and Critical Care Medicine |
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IAM; 600.145 |
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Admin @ si @ BDS2021 |
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3591 |
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Mireia Sole; Joan Blanco; Debora Gil; Oliver Valero; Alvaro Pascual; B. Cardenas; G. Fonseka; E. Anton; Richard Frodsham; Francesca Vidal; Zaida Sarrate |
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Chromosomal positioning in spermatogenic cells is influenced by chromosomal factors associated with gene activity, bouquet formation, and meiotic sex-chromosome inactivation |
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2021 |
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Chromosoma |
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130 |
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163-175 |
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Chromosome territoriality is not random along the cell cycle and it is mainly governed by intrinsic chromosome factors and gene expression patterns. Conversely, very few studies have explored the factors that determine chromosome territoriality and its influencing factors during meiosis. In this study, we analysed chromosome positioning in murine spermatogenic cells using three-dimensionally fluorescence in situ hybridization-based methodology, which allows the analysis of the entire karyotype. The main objective of the study was to decipher chromosome positioning in a radial axis (all analysed germ-cell nuclei) and longitudinal axis (only spermatozoa) and to identify the chromosomal factors that regulate such an arrangement. Results demonstrated that the radial positioning of chromosomes during spermatogenesis was cell-type specific and influenced by chromosomal factors associated to gene activity. Chromosomes with specific features that enhance transcription (high GC content, high gene density and high numbers of predicted expressed genes) were preferentially observed in the inner part of the nucleus in virtually all cell types. Moreover, the position of the sex chromosomes was influenced by their transcriptional status, from the periphery of the nucleus when its activity was repressed (pachytene) to a more internal position when it is partially activated (spermatid). At pachytene, chromosome positioning was also influenced by chromosome size due to the bouquet formation. Longitudinal chromosome positioning in the sperm nucleus was not random either, suggesting the importance of ordered longitudinal positioning for the release and activation of the paternal genome after fertilisation. |
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IAM; 600.145 |
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Admin @ si @ SBG2021 |
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3592 |
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Marta Ligero; Alonso Garcia Ruiz; Cristina Viaplana; Guillermo Villacampa; Maria V Raciti; Jaid Landa; Ignacio Matos; Juan Martin Liberal; Maria Ochoa de Olza; Cinta Hierro; Joaquin Mateo; Macarena Gonzalez; Rafael Morales Barrera; Cristina Suarez; Jordi Rodon; Elena Elez; Irene Braña; Eva Muñoz-Couselo; Ana Oaknin; Roberta Fasani; Paolo Nuciforo; Debora Gil; Carlota Rubio Perez; Joan Seoane; Enriqueta Felip; Manuel Escobar; Josep Tabernero; Joan Carles; Rodrigo Dienstmann; Elena Garralda; Raquel Perez Lopez |
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A CT-based radiomics signature is associated with response to immune checkpoint inhibitors in advanced solid tumors |
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2021 |
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Radiology |
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299 |
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1 |
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109-119 |
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Background Reliable predictive imaging markers of response to immune checkpoint inhibitors are needed. Purpose To develop and validate a pretreatment CT-based radiomics signature to predict response to immune checkpoint inhibitors in advanced solid tumors. Materials and Methods In this retrospective study, a radiomics signature was developed in patients with advanced solid tumors (including breast, cervix, gastrointestinal) treated with anti-programmed cell death-1 or programmed cell death ligand-1 monotherapy from August 2012 to May 2018 (cohort 1). This was tested in patients with bladder and lung cancer (cohorts 2 and 3). Radiomics variables were extracted from all metastases delineated at pretreatment CT and selected by using an elastic-net model. A regression model combined radiomics and clinical variables with response as the end point. Biologic validation of the radiomics score with RNA profiling of cytotoxic cells (cohort 4) was assessed with Mann-Whitney analysis. Results The radiomics signature was developed in 85 patients (cohort 1: mean age, 58 years ± 13 [standard deviation]; 43 men) and tested on 46 patients (cohort 2: mean age, 70 years ± 12; 37 men) and 47 patients (cohort 3: mean age, 64 years ± 11; 40 men). Biologic validation was performed in a further cohort of 20 patients (cohort 4: mean age, 60 years ± 13; 14 men). The radiomics signature was associated with clinical response to immune checkpoint inhibitors (area under the curve [AUC], 0.70; 95% CI: 0.64, 0.77; P < .001). In cohorts 2 and 3, the AUC was 0.67 (95% CI: 0.58, 0.76) and 0.67 (95% CI: 0.56, 0.77; P < .001), respectively. A radiomics-clinical signature (including baseline albumin level and lymphocyte count) improved on radiomics-only performance (AUC, 0.74 [95% CI: 0.63, 0.84; P < .001]; Akaike information criterion, 107.00 and 109.90, respectively). Conclusion A pretreatment CT-based radiomics signature is associated with response to immune checkpoint inhibitors, likely reflecting the tumor immunophenotype. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Summers in this issue. |
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IAM; 600.145 |
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Admin @ si @ LGV2021 |
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3593 |
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Debora Gil; Oriol Ramos Terrades; Raquel Perez |
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Topological Radiomics (TOPiomics): Early Detection of Genetic Abnormalities in Cancer Treatment Evolution |
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2021 |
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Extended Abstracts GEOMVAP 2019, Trends in Mathematics 15 |
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15 |
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89–93 |
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Abnormalities in radiomic measures correlate to genomic alterations prone to alter the outcome of personalized anti-cancer treatments. TOPiomics is a new method for the early detection of variations in tumor imaging phenotype from a topological structure in multi-view radiomic spaces. |
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Springer Nature |
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IAM; DAG; 600.120; 600.145; 600.139 |
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Admin @ si @ GRP2021 |
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3594 |
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Trevor Canham; Javier Vazquez; Elise Mathieu; Marcelo Bertalmío |
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Matching visual induction effects on screens of different size |
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2021 |
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Journal of Vision |
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JOV |
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21 |
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6(10) |
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1-22 |
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In the film industry, the same movie is expected to be watched on displays of vastly different sizes, from cinema screens to mobile phones. But visual induction, the perceptual phenomenon by which the appearance of a scene region is affected by its surroundings, will be different for the same image shown on two displays of different dimensions. This phenomenon presents a practical challenge for the preservation of the artistic intentions of filmmakers, because it can lead to shifts in image appearance between viewing destinations. In this work, we show that a neural field model based on the efficient representation principle is able to predict induction effects and how, by regularizing its associated energy functional, the model is still able to represent induction but is now invertible. From this finding, we propose a method to preprocess an image in a screen–size dependent way so that its perception, in terms of visual induction, may remain constant across displays of different size. The potential of the method is demonstrated through psychophysical experiments on synthetic images and qualitative examples on natural images. |
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Admin @ si @ CVM2021 |
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3595 |
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Graham D. Finlayson; Javier Vazquez; Fufu Fang |
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The Discrete Cosine Maximum Ignorance Assumption |
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2021 |
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29th Color and Imaging Conference |
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the performance of colour correction algorithms are dependent on the reflectance sets used. Sometimes, when the testing reflectance set is changed the ranking of colour correction algorithms also changes. To remove dependence on dataset we can
make assumptions about the set of all possible reflectances. In the Maximum Ignorance with Positivity (MIP) assumption we assume that all reflectances with per wavelength values between 0 and 1 are equally likely. A weakness in the MIP is that it fails to take into account the correlation of reflectance functions between
wavelengths (many of the assumed reflectances are, in reality, not possible).
In this paper, we take the view that the maximum ignorance assumption has merit but, hitherto it has been calculated with respect to the wrong coordinate basis. Here, we propose the Discrete Cosine Maximum Ignorance assumption (DCMI), where
all reflectances that have coordinates between max and min bounds in the Discrete Cosine Basis coordinate system are equally likely.
Here, the correlation between wavelengths is encoded and this results in the set of all plausible reflectances ’looking like’ typical reflectances that occur in nature. This said the DCMI model is also a superset of all measured reflectance sets.
Experiments show that, in colour correction, adopting the DCMI results in similar colour correction performance as using a particular reflectance set. |
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Virtual; November 2021 |
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FVF2021 |
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3596 |
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Josep Llados; Daniel Lopresti; Seiichi Uchida (eds) |
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16th International Conference, 2021, Proceedings, Part I |
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2021 |
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Document Analysis and Recognition – ICDAR 2021 |
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12821 |
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This four-volume set of LNCS 12821, LNCS 12822, LNCS 12823 and LNCS 12824, constitutes the refereed proceedings of the 16th International Conference on Document Analysis and Recognition, ICDAR 2021, held in Lausanne, Switzerland in September 2021. The 182 full papers were carefully reviewed and selected from 340 submissions, and are presented with 13 competition reports.
The papers are organized into the following topical sections: historical document analysis, document analysis systems, handwriting recognition, scene text detection and recognition, document image processing, natural language processing (NLP) for document understanding, and graphics, diagram and math recognition. |
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Lausanne, Switzerland, September 5-10, 2021 |
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Springer Cham |
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Editor |
Josep Llados; Daniel Lopresti; Seiichi Uchida |
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LNCS |
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ISBN |
978-3-030-86548-1 |
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Conference |
ICDAR |
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Notes |
DAG |
Approved |
no |
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Call Number |
Admin @ si @ |
Serial |
3725 |
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Author |
Josep Llados; Daniel Lopresti; Seiichi Uchida (eds) |
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Title |
16th International Conference, 2021, Proceedings, Part II |
Type |
Book Whole |
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Year |
2021 |
Publication |
Document Analysis and Recognition – ICDAR 2021 |
Abbreviated Journal |
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Volume |
12822 |
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Abstract |
This four-volume set of LNCS 12821, LNCS 12822, LNCS 12823 and LNCS 12824, constitutes the refereed proceedings of the 16th International Conference on Document Analysis and Recognition, ICDAR 2021, held in Lausanne, Switzerland in September 2021. The 182 full papers were carefully reviewed and selected from 340 submissions, and are presented with 13 competition reports.
The papers are organized into the following topical sections: document analysis for literature search, document summarization and translation, multimedia document analysis, mobile text recognition, document analysis for social good, indexing and retrieval of documents, physical and logical layout analysis, recognition of tables and formulas, and natural language processing (NLP) for document understanding. |
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Address |
Lausanne, Switzerland, September 5-10, 2021 |
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Corporate Author |
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Thesis |
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Publisher |
Springer Cham |
Place of Publication |
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Editor |
Josep Llados; Daniel Lopresti; Seiichi Uchida |
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LNCS |
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ISBN |
978-3-030-86330-2 |
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Conference |
ICDAR |
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Notes |
DAG |
Approved |
no |
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Call Number |
Admin @ si @ |
Serial |
3726 |
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Author |
Razieh Rastgoo; Kourosh Kiani; Sergio Escalera; Mohammad Sabokrou |
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Title |
Sign Language Production: A Review |
Type |
Conference Article |
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Year |
2021 |
Publication |
Conference on Computer Vision and Pattern Recognition Workshops |
Abbreviated Journal |
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Pages |
3472-3481 |
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Abstract |
Sign Language is the dominant yet non-primary form of communication language used in the deaf and hearing-impaired community. To make an easy and mutual communication between the hearing-impaired and the hearing communities, building a robust system capable of translating the spoken language into sign language and vice versa is fundamental. To this end, sign language recognition and production are two necessary parts for making such a two-way system. Sign language recognition and production need to cope with some critical challenges. In this survey, we review recent advances in Sign Language Production (SLP) and related areas using deep learning. This survey aims to briefly summarize recent achievements in SLP, discussing their advantages, limitations, and future directions of research. |
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Address |
Virtual; June 2021 |
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Conference |
CVPRW |
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Notes |
HUPBA; no proj |
Approved |
no |
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Call Number |
Admin @ si @ RKE2021b |
Serial |
3603 |
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Author |
Yaxing Wang; Hector Laria Mantecon; Joost Van de Weijer; Laura Lopez-Fuentes; Bogdan Raducanu |
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Title |
TransferI2I: Transfer Learning for Image-to-Image Translation from Small Datasets |
Type |
Conference Article |
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Year |
2021 |
Publication |
19th IEEE International Conference on Computer Vision |
Abbreviated Journal |
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Volume |
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Pages |
13990-13999 |
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Abstract |
Image-to-image (I2I) translation has matured in recent years and is able to generate high-quality realistic images. However, despite current success, it still faces important challenges when applied to small domains. Existing methods use transfer learning for I2I translation, but they still require the learning of millions of parameters from scratch. This drawback severely limits its application on small domains. In this paper, we propose a new transfer learning for I2I translation (TransferI2I). We decouple our learning process into the image generation step and the I2I translation step. In the first step we propose two novel techniques: source-target initialization and self-initialization of the adaptor layer. The former finetunes the pretrained generative model (e.g., StyleGAN) on source and target data. The latter allows to initialize all non-pretrained network parameters without the need of any data. These techniques provide a better initialization for the I2I translation step. In addition, we introduce an auxiliary GAN that further facilitates the training of deep I2I systems even from small datasets. In extensive experiments on three datasets, (Animal faces, Birds, and Foods), we show that we outperform existing methods and that mFID improves on several datasets with over 25 points. |
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Address |
Virtual; October 2021 |
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Conference |
ICCV |
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Notes |
LAMP; 600.147; 602.200; 600.120 |
Approved |
no |
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Call Number |
Admin @ si @ WLW2021 |
Serial |
3604 |
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