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Author | Ahmed M. A. Salih; Ilaria Boscolo Galazzo; Federica Cruciani; Lorenza Brusini; Petia Radeva | ||||
Title | Investigating Explainable Artificial Intelligence for MRI-based Classification of Dementia: a New Stability Criterion for Explainable Methods | Type | Conference Article | ||
Year | 2022 | Publication | 29th IEEE International Conference on Image Processing | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Image processing; Stability criteria; Machine learning; Robustness; Alzheimer's disease; Monitoring | ||||
Abstract | Individuals diagnosed with Mild Cognitive Impairment (MCI) have shown an increased risk of developing Alzheimer’s Disease (AD). As such, early identification of dementia represents a key prognostic element, though hampered by complex disease patterns. Increasing efforts have focused on Machine Learning (ML) to build accurate classification models relying on a multitude of clinical/imaging variables. However, ML itself does not provide sensible explanations related to the model mechanism and feature contribution. Explainable Artificial Intelligence (XAI) represents the enabling technology in this framework, allowing to understand ML outcomes and derive human-understandable explanations. In this study, we aimed at exploring ML combined with MRI-based features and XAI to solve this classification problem and interpret the outcome. In particular, we propose a new method to assess the robustness of feature rankings provided by XAI methods, especially when multicollinearity exists. Our findings indicate that our method was able to disentangle the list of the informative features underlying dementia, with important implications for aiding personalized monitoring plans. | ||||
Address | Bordeaux; France; October 2022 | ||||
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Language | Summary Language | Original Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ICIP | ||
Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ SBC2022 | Serial | 3789 | ||
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Author | Chengyi Zou; Shuai Wan; Marta Mrak; Marc Gorriz Blanch; Luis Herranz; Tiannan Ji | ||||
Title | Towards Lightweight Neural Network-based Chroma Intra Prediction for Video Coding | Type | Conference Article | ||
Year | 2022 | Publication | 29th IEEE International Conference on Image Processing | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Video coding; Quantization (signal); Computational modeling; Neural networks; Predictive models; Video compression; Syntactics | ||||
Abstract | In video compression the luma channel can be useful for predicting chroma channels (Cb, Cr), as has been demonstrated with the Cross-Component Linear Model (CCLM) used in Versatile Video Coding (VVC) standard. More recently, it has been shown that neural networks can even better capture the relationship among different channels. In this paper, a new attention-based neural network is proposed for cross-component intra prediction. With the goal to simplify neural network design, the new framework consists of four branches: boundary branch and luma branch for extracting features from reference samples, attention branch for fusing the first two branches, and prediction branch for computing the predicted chroma samples. The proposed scheme is integrated into VVC test model together with one additional binary block-level syntax flag which indicates whether a given block makes use of the proposed method. Experimental results demonstrate 0.31%/2.36%/2.00% BD-rate reductions on Y/Cb/Cr components, respectively, on top of the VVC Test Model (VTM) 7.0 which uses CCLM. | ||||
Address | Bordeaux; France; October 2022 | ||||
Corporate Author | Thesis | ||||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ICIP | ||
Notes | MACO | Approved | no | ||
Call Number | Admin @ si @ ZWM2022 | Serial | 3790 | ||
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Author | Yaxing Wang; Joost Van de Weijer; Lu Yu; Shangling Jui | ||||
Title | Distilling GANs with Style-Mixed Triplets for X2I Translation with Limited Data | Type | Conference Article | ||
Year | 2022 | Publication | 10th International Conference on Learning Representations | Abbreviated Journal | |
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Abstract | Conditional image synthesis is an integral part of many X2I translation systems, including image-to-image, text-to-image and audio-to-image translation systems. Training these large systems generally requires huge amounts of training data.
Therefore, we investigate knowledge distillation to transfer knowledge from a high-quality unconditioned generative model (e.g., StyleGAN) to a conditioned synthetic image generation modules in a variety of systems. To initialize the conditional and reference branch (from a unconditional GAN) we exploit the style mixing characteristics of high-quality GANs to generate an infinite supply of style-mixed triplets to perform the knowledge distillation. Extensive experimental results in a number of image generation tasks (i.e., image-to-image, semantic segmentation-to-image, text-to-image and audio-to-image) demonstrate qualitatively and quantitatively that our method successfully transfers knowledge to the synthetic image generation modules, resulting in more realistic images than previous methods as confirmed by a significant drop in the FID. |
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Address | Virtual | ||||
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Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ICLR | ||
Notes | LAMP; 600.147 | Approved | no | ||
Call Number | Admin @ si @ WWY2022 | Serial | 3791 | ||
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Author | Kai Wang; Fei Yang; Joost Van de Weijer | ||||
Title | Attention Distillation: self-supervised vision transformer students need more guidance | Type | Conference Article | ||
Year | 2022 | Publication | 33rd British Machine Vision Conference | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | Self-supervised learning has been widely applied to train high-quality vision transformers. Unleashing their excellent performance on memory and compute constraint devices is therefore an important research topic. However, how to distill knowledge from one self-supervised ViT to another has not yet been explored. Moreover, the existing self-supervised knowledge distillation (SSKD) methods focus on ConvNet based architectures are suboptimal for ViT knowledge distillation. In this paper, we study knowledge distillation of self-supervised vision transformers (ViT-SSKD). We show that directly distilling information from the crucial attention mechanism from teacher to student can significantly narrow the performance gap between both. In experiments on ImageNet-Subset and ImageNet-1K, we show that our method AttnDistill outperforms existing self-supervised knowledge distillation (SSKD) methods and achieves state-of-the-art k-NN accuracy compared with self-supervised learning (SSL) methods learning from scratch (with the ViT-S model). We are also the first to apply the tiny ViT-T model on self-supervised learning. Moreover, AttnDistill is independent of self-supervised learning algorithms, it can be adapted to ViT based SSL methods to improve the performance in future research. | ||||
Address | London; UK; November 2022 | ||||
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Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | BMVC | ||
Notes | LAMP; 600.147 | Approved | no | ||
Call Number | Admin @ si @ WYW2022 | Serial | 3793 | ||
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Author | Kai Wang; Chenshen Wu; Andrew Bagdanov; Xialei Liu; Shiqi Yang; Shangling Jui; Joost Van de Weijer | ||||
Title | Positive Pair Distillation Considered Harmful: Continual Meta Metric Learning for Lifelong Object Re-Identification | Type | Conference Article | ||
Year | 2022 | Publication | 33rd British Machine Vision Conference | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | Lifelong object re-identification incrementally learns from a stream of re-identification tasks. The objective is to learn a representation that can be applied to all tasks and that generalizes to previously unseen re-identification tasks. The main challenge is that at inference time the representation must generalize to previously unseen identities. To address this problem, we apply continual meta metric learning to lifelong object re-identification. To prevent forgetting of previous tasks, we use knowledge distillation and explore the roles of positive and negative pairs. Based on our observation that the distillation and metric losses are antagonistic, we propose to remove positive pairs from distillation to robustify model updates. Our method, called Distillation without Positive Pairs (DwoPP), is evaluated on extensive intra-domain experiments on person and vehicle re-identification datasets, as well as inter-domain experiments on the LReID benchmark. Our experiments demonstrate that DwoPP significantly outperforms the state-of-the-art. | ||||
Address | London; UK; November 2022 | ||||
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Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | BMVC | ||
Notes | LAMP; 600.147 | Approved | no | ||
Call Number | Admin @ si @ WWB2022 | Serial | 3794 | ||
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Author | Andrea Gemelli; Sanket Biswas; Enrico Civitelli; Josep Llados; Simone Marinai | ||||
Title | Doc2Graph: A Task Agnostic Document Understanding Framework Based on Graph Neural Networks | Type | Conference Article | ||
Year | 2022 | Publication | 17th European Conference on Computer Vision Workshops | Abbreviated Journal | |
Volume | 13804 | Issue | Pages | 329–344 | |
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Abstract | Geometric Deep Learning has recently attracted significant interest in a wide range of machine learning fields, including document analysis. The application of Graph Neural Networks (GNNs) has become crucial in various document-related tasks since they can unravel important structural patterns, fundamental in key information extraction processes. Previous works in the literature propose task-driven models and do not take into account the full power of graphs. We propose Doc2Graph, a task-agnostic document understanding framework based on a GNN model, to solve different tasks given different types of documents. We evaluated our approach on two challenging datasets for key information extraction in form understanding, invoice layout analysis and table detection. | ||||
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Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-3-031-25068-2 | Medium | ||
Area | Expedition | Conference | ECCV-TiE | ||
Notes | DAG; 600.162; 600.140; 110.312 | Approved | no | ||
Call Number | Admin @ si @ GBC2022 | Serial | 3795 | ||
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Author | Vishwesh Pillai; Pranav Mehar; Manisha Das; Deep Gupta; Petia Radeva | ||||
Title | Integrated Hierarchical and Flat Classifiers for Food Image Classification using Epistemic Uncertainty | Type | Conference Article | ||
Year | 2022 | Publication | IEEE International Conference on Signal Processing and Communications | Abbreviated Journal | |
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Abstract | The problem of food image recognition is an essential one in today’s context because health conditions such as diabetes, obesity, and heart disease require constant monitoring of a person’s diet. To automate this process, several models are available to recognize food images. Due to a considerable number of unique food dishes and various cuisines, a traditional flat classifier ceases to perform well. To address this issue, prediction schemes consisting of both flat and hierarchical classifiers, with the analysis of epistemic uncertainty are used to switch between the classifiers. However, the accuracy of the predictions made using epistemic uncertainty data remains considerably low. Therefore, this paper presents a prediction scheme using three different threshold criteria that helps to increase the accuracy of epistemic uncertainty predictions. The performance of the proposed method is demonstrated using several experiments performed on the MAFood-121 dataset. The experimental results validate the proposal performance and show that the proposed threshold criteria help to increase the overall accuracy of the predictions by correctly classifying the uncertainty distribution of the samples. | ||||
Address | Bangalore; India; July 2022 | ||||
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Language | Summary Language | Original Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | SPCOM | ||
Notes | MILAB; no menciona | Approved | no | ||
Call Number | Admin @ si @ PMD2022 | Serial | 3796 | ||
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Author | Javier Rodenas; Bhalaji Nagarajan; Marc Bolaños; Petia Radeva | ||||
Title | Learning Multi-Subset of Classes for Fine-Grained Food Recognition | Type | Conference Article | ||
Year | 2022 | Publication | 7th International Workshop on Multimedia Assisted Dietary Management | Abbreviated Journal | |
Volume | Issue | Pages | 17–26 | ||
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Abstract | Food image recognition is a complex computer vision task, because of the large number of fine-grained food classes. Fine-grained recognition tasks focus on learning subtle discriminative details to distinguish similar classes. In this paper, we introduce a new method to improve the classification of classes that are more difficult to discriminate based on Multi-Subsets learning. Using a pre-trained network, we organize classes in multiple subsets using a clustering technique. Later, we embed these subsets in a multi-head model structure. This structure has three distinguishable parts. First, we use several shared blocks to learn the generalized representation of the data. Second, we use multiple specialized blocks focusing on specific subsets that are difficult to distinguish. Lastly, we use a fully connected layer to weight the different subsets in an end-to-end manner by combining the neuron outputs. We validated our proposed method using two recent state-of-the-art vision transformers on three public food recognition datasets. Our method was successful in learning the confused classes better and we outperformed the state-of-the-art on the three datasets. | ||||
Address | Lisboa; Portugal; October 2022 | ||||
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Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | MADiMa | ||
Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ RNB2022 | Serial | 3797 | ||
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Author | Bhalaji Nagarajan; Ricardo Marques; Marcos Mejia; Petia Radeva | ||||
Title | Class-conditional Importance Weighting for Deep Learning with Noisy Labels | Type | Conference Article | ||
Year | 2022 | Publication | 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications | Abbreviated Journal | |
Volume | 5 | Issue | Pages | 679-686 | |
Keywords | Noisy Labeling; Loss Correction; Class-conditional Importance Weighting; Learning with Noisy Labels | ||||
Abstract | Large-scale accurate labels are very important to the Deep Neural Networks to train them and assure high performance. However, it is very expensive to create a clean dataset since usually it relies on human interaction. To this purpose, the labelling process is made cheap with a trade-off of having noisy labels. Learning with Noisy Labels is an active area of research being at the same time very challenging. The recent advances in Self-supervised learning and robust loss functions have helped in advancing noisy label research. In this paper, we propose a loss correction method that relies on dynamic weights computed based on the model training. We extend the existing Contrast to Divide algorithm coupled with DivideMix using a new class-conditional weighted scheme. We validate the method using the standard noise experiments and achieved encouraging results. | ||||
Address | Virtual; February 2022 | ||||
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Language | Summary Language | Original Title | |||
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Area | Expedition | Conference | VISAPP | ||
Notes | MILAB; no menciona | Approved | no | ||
Call Number | Admin @ si @ NMM2022 | Serial | 3798 | ||
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Author | Carles Onielfa; Carles Casacuberta; Sergio Escalera | ||||
Title | Influence in Social Networks Through Visual Analysis of Image Memes | Type | Conference Article | ||
Year | 2022 | Publication | Artificial Intelligence Research and Development | Abbreviated Journal | |
Volume | 356 | Issue | Pages | 71-80 | |
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Abstract | Memes evolve and mutate through their diffusion in social media. They have the potential to propagate ideas and, by extension, products. Many studies have focused on memes, but none so far, to our knowledge, on the users that post them, their relationships, and the reach of their influence. In this article, we define a meme influence graph together with suitable metrics to visualize and quantify influence between users who post memes, and we also describe a process to implement our definitions using a new approach to meme detection based on text-to-image area ratio and contrast. After applying our method to a set of users of the social media platform Instagram, we conclude that our metrics add information to already existing user characteristics. | ||||
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Notes | HuPBA; no menciona | Approved | no | ||
Call Number | Admin @ si @ OCE2022 | Serial | 3799 | ||
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Author | Smriti Joshi; Richard Osuala; Carlos Martin-Isla; Victor M.Campello; Carla Sendra-Balcells; Karim Lekadir; Sergio Escalera | ||||
Title | nn-UNet Training on CycleGAN-Translated Images for Cross-modal Domain Adaptation in Biomedical Imaging | Type | Conference Article | ||
Year | 2022 | Publication | International MICCAI Brainlesion Workshop | Abbreviated Journal | |
Volume | 12963 | Issue | Pages | 540–551 | |
Keywords | Domain adaptation; Vestibular schwannoma (VS); Deep learning; nn-UNet; CycleGAN | ||||
Abstract | In recent years, deep learning models have considerably advanced the performance of segmentation tasks on Brain Magnetic Resonance Imaging (MRI). However, these models show a considerable performance drop when they are evaluated on unseen data from a different distribution. Since annotation is often a hard and costly task requiring expert supervision, it is necessary to develop ways in which existing models can be adapted to the unseen domains without any additional labelled information. In this work, we explore one such technique which extends the CycleGAN [2] architecture to generate label-preserving data in the target domain. The synthetic target domain data is used to train the nn-UNet [3] framework for the task of multi-label segmentation. The experiments are conducted and evaluated on the dataset [1] provided in the ‘Cross-Modality Domain Adaptation for Medical Image Segmentation’ challenge [23] for segmentation of vestibular schwannoma (VS) tumour and cochlea on contrast enhanced (ceT1) and high resolution (hrT2) MRI scans. In the proposed approach, our model obtains dice scores (DSC) 0.73 and 0.49 for tumour and cochlea respectively on the validation set of the dataset. This indicates the applicability of the proposed technique to real-world problems where data may be obtained by different acquisition protocols as in [1] where hrT2 images are more reliable, safer, and lower-cost alternative to ceT1. | ||||
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Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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Area | Expedition | Conference | MICCAIW | ||
Notes | HUPBA; no menciona | Approved | no | ||
Call Number | Admin @ si @ JOM2022 | Serial | 3800 | ||
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Author | Silvio Giancola; Anthony Cioppa; Adrien Deliege; Floriane Magera; Vladimir Somers; Le Kang; Xin Zhou; Olivier Barnich; Christophe De Vleeschouwer; Alexandre Alahi; Bernard Ghanem; Marc Van Droogenbroeck; Abdulrahman Darwish; Adrien Maglo; Albert Clapes; Andreas Luyts; Andrei Boiarov; Artur Xarles; Astrid Orcesi; Avijit Shah; Baoyu Fan; Bharath Comandur; Chen Chen; Chen Zhang; Chen Zhao; Chengzhi Lin; Cheuk-Yiu Chan; Chun Chuen Hui; Dengjie Li; Fan Yang; Fan Liang; Fang Da; Feng Yan; Fufu Yu; Guanshuo Wang; H. Anthony Chan; He Zhu; Hongwei Kan; Jiaming Chu; Jianming Hu; Jianyang Gu; Jin Chen; Joao V. B. Soares; Jonas Theiner; Jorge De Corte; Jose Henrique Brito; Jun Zhang; Junjie Li; Junwei Liang; Leqi Shen; Lin Ma; Lingchi Chen; Miguel Santos Marques; Mike Azatov; Nikita Kasatkin; Ning Wang; Qiong Jia; Quoc Cuong Pham; Ralph Ewerth; Ran Song; Rengang Li; Rikke Gade; Ruben Debien; Runze Zhang; Sangrok Lee; Sergio Escalera; Shan Jiang; Shigeyuki Odashima; Shimin Chen; Shoichi Masui; Shouhong Ding; Sin-wai Chan; Siyu Chen; Tallal El-Shabrawy; Tao He; Thomas B. Moeslund; Wan-Chi Siu; Wei Zhang; Wei Li; Xiangwei Wang; Xiao Tan; Xiaochuan Li; Xiaolin Wei; Xiaoqing Ye; Xing Liu; Xinying Wang; Yandong Guo; Yaqian Zhao; Yi Yu; Yingying Li; Yue He; Yujie Zhong; Zhenhua Guo; Zhiheng Li | ||||
Title | SoccerNet 2022 Challenges Results | Type | Conference Article | ||
Year | 2022 | Publication | 5th International ACM Workshop on Multimedia Content Analysis in Sports | Abbreviated Journal | |
Volume | Issue | Pages | 75-86 | ||
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Abstract | The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team. In 2022, the challenges were composed of 6 vision-based tasks: (1) action spotting, focusing on retrieving action timestamps in long untrimmed videos, (2) replay grounding, focusing on retrieving the live moment of an action shown in a replay, (3) pitch localization, focusing on detecting line and goal part elements, (4) camera calibration, dedicated to retrieving the intrinsic and extrinsic camera parameters, (5) player re-identification, focusing on retrieving the same players across multiple views, and (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams. Compared to last year's challenges, tasks (1-2) had their evaluation metrics redefined to consider tighter temporal accuracies, and tasks (3-6) were novel, including their underlying data and annotations. More information on the tasks, challenges and leaderboards are available on this https URL. Baselines and development kits are available on this https URL. | ||||
Address | Lisboa; Portugal; October 2022 | ||||
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Area | Expedition | Conference | ACMW | ||
Notes | HUPBA; no menciona | Approved | no | ||
Call Number | Admin @ si @ GCD2022 | Serial | 3801 | ||
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Author | Dustin Carrion Ojeda; Hong Chen; Adrian El Baz; Sergio Escalera; Chaoyu Guan; Isabelle Guyon; Ihsan Ullah; Xin Wang; Wenwu Zhu | ||||
Title | NeurIPS’22 Cross-Domain MetaDL competition: Design and baseline results | Type | Conference Article | ||
Year | 2022 | Publication | Understanding Social Behavior in Dyadic and Small Group Interactions | Abbreviated Journal | |
Volume | 191 | Issue | Pages | 24-37 | |
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Abstract | We present the design and baseline results for a new challenge in the ChaLearn meta-learning series, accepted at NeurIPS'22, focusing on “cross-domain” meta-learning. Meta-learning aims to leverage experience gained from previous tasks to solve new tasks efficiently (i.e., with better performance, little training data, and/or modest computational resources). While previous challenges in the series focused on within-domain few-shot learning problems, with the aim of learning efficiently N-way k-shot tasks (i.e., N class classification problems with k training examples), this competition challenges the participants to solve “any-way” and “any-shot” problems drawn from various domains (healthcare, ecology, biology, manufacturing, and others), chosen for their humanitarian and societal impact. To that end, we created Meta-Album, a meta-dataset of 40 image classification datasets from 10 domains, from which we carve out tasks with any number of “ways” (within the range 2-20) and any number of “shots” (within the range 1-20). The competition is with code submission, fully blind-tested on the CodaLab challenge platform. The code of the winners will be open-sourced, enabling the deployment of automated machine learning solutions for few-shot image classification across several domains. | ||||
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Area | Expedition | Conference | PMLR | ||
Notes | HUPBA; no menciona | Approved | no | ||
Call Number | Admin @ si @ CCB2022 | Serial | 3802 | ||
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Author | Patricia Suarez; Dario Carpio; Angel Sappa; Henry Velesaca | ||||
Title | Transformer based Image Dehazing | Type | Conference Article | ||
Year | 2022 | Publication | 16th IEEE International Conference on Signal Image Technology & Internet Based System | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | atmospheric light; brightness component; computational cost; dehazing quality; haze-free image | ||||
Abstract | This paper presents a novel approach to remove non homogeneous haze from real images. The proposed method consists mainly of image feature extraction, haze removal, and image reconstruction. To accomplish this challenging task, we propose an architecture based on transformers, which have been recently introduced and have shown great potential in different computer vision tasks. Our model is based on the SwinIR an image restoration architecture based on a transformer, but by modifying the deep feature extraction module, the depth level of the model, and by applying a combined loss function that improves styling and adapts the model for the non-homogeneous haze removal present in images. The obtained results prove to be superior to those obtained by state-of-the-art models. | ||||
Address | Dijon; France; October 2022 | ||||
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Area | Expedition | Conference | SITIS | ||
Notes | MSIAU; no proj | Approved | no | ||
Call Number | Admin @ si @ SCS2022 | Serial | 3803 | ||
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Author | Angel Sappa; Patricia Suarez; Henry Velesaca; Dario Carpio | ||||
Title | Domain Adaptation in Image Dehazing: Exploring the Usage of Images from Virtual Scenarios | Type | Conference Article | ||
Year | 2022 | Publication | 16th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing | Abbreviated Journal | |
Volume | Issue | Pages | 85-92 | ||
Keywords | Domain adaptation; Synthetic hazed dataset; Dehazing | ||||
Abstract | This work presents a novel domain adaptation strategy for deep learning-based approaches to solve the image dehazing
problem. Firstly, a large set of synthetic images is generated by using a realistic 3D graphic simulator; these synthetic images contain different densities of haze, which are used for training the model that is later adapted to any real scenario. The adaptation process requires just a few images to fine-tune the model parameters. The proposed strategy allows overcoming the limitation of training a given model with few images. In other words, the proposed strategy implements the adaptation of a haze removal model trained with synthetic images to real scenarios. It should be noticed that it is quite difficult, if not impossible, to have large sets of pairs of real-world images (with and without haze) to train in a supervised way dehazing algorithms. Experimental results are provided showing the validity of the proposed domain adaptation strategy. |
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Address | Lisboa; Portugal; July 2022 | ||||
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Area | Expedition | Conference | CGVCVIP | ||
Notes | MSIAU; no proj | Approved | no | ||
Call Number | Admin @ si @ SSV2022 | Serial | 3804 | ||
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