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Author |
Md. Mostafa Kamal Sarker; Mohammed Jabreel; Hatem A. Rashwan; Syeda Furruka Banu; Antonio Moreno; Petia Radeva; Domenec Puig |
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Title |
CuisineNet: Food Attributes Classification using Multi-scale Convolution Network. |
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Miscellaneous |
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2018 |
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Arxiv |
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Diversity of food and its attributes represents the culinary habits of peoples from different countries. Thus, this paper addresses the problem of identifying food culture of people around the world and its flavor by classifying two main food attributes, cuisine and flavor. A deep learning model based on multi-scale convotuional networks is proposed for extracting more accurate features from input images. The aggregation of multi-scale convolution layers with different kernel size is also used for weighting the features results from different scales. In addition, a joint loss function based on Negative Log Likelihood (NLL) is used to fit the model probability to multi labeled classes for multi-modal classification task. Furthermore, this work provides a new dataset for food attributes, so-called Yummly48K, extracted from the popular food website, Yummly. Our model is assessed on the constructed Yummly48K dataset. The experimental results show that our proposed method yields 65% and 62% average F1 score on validation and test set which outperforming the state-of-the-art models. |
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MILAB; no proj |
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no |
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Admin @ si @ KJR2018 |
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3235 |
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Author |
Mert Kilickaya; Joost van de Weijer; Yuki M. Asano |
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Title |
Towards Label-Efficient Incremental Learning: A Survey |
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2023 |
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Arxiv |
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The current dominant paradigm when building a machine learning model is to iterate over a dataset over and over until convergence. Such an approach is non-incremental, as it assumes access to all images of all categories at once. However, for many applications, non-incremental learning is unrealistic. To that end, researchers study incremental learning, where a learner is required to adapt to an incoming stream of data with a varying distribution while preventing forgetting of past knowledge. Significant progress has been made, however, the vast majority of works focus on the fully supervised setting, making these algorithms label-hungry thus limiting their real-life deployment. To that end, in this paper, we make the first attempt to survey recently growing interest in label-efficient incremental learning. We identify three subdivisions, namely semi-, few-shot- and self-supervised learning to reduce labeling efforts. Finally, we identify novel directions that can further enhance label-efficiency and improve incremental learning scalability. Project website: this https URL. |
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LAMP |
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no |
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Admin @ si @ KWA2023 |
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3994 |
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Author |
Lubomir Latchev; Maya Dimitrova; David Rotger |
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A Classifier of Technical Diagnostic States of Electrocardiograph |
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2006 |
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International Conference on Computer Systems and Technologies (CompSysTech´06), 15.1–15.6 |
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University of Veliko Tarnovo (Bulgaria) |
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Admin @ si @ LDR2006 |
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774 |
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David Lloret; Derek L.G. Hill |
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System for live fusion of 2-D ultrasound scans to pre-interventional MR volumes of a patient. |
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1999 |
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Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes, 2:23–24. |
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Bilbao |
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Admin @ si @ LlH1999 |
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183 |
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Author |
Stefan Lonn; Petia Radeva; Mariella Dimiccoli |
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Title |
A picture is worth a thousand words but how to organize thousands of pictures? |
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2018 |
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Arxiv |
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We live in a society where the large majority of the population has a camera-equipped smartphone. In addition, hard drives and cloud storage are getting cheaper and cheaper, leading to a tremendous growth in stored personal photos. Unlike photo collections captured by a digital camera, which typically are pre-processed by the user who organizes them into event-related folders, smartphone pictures are automatically stored in the cloud. As a consequence, photo collections captured by a smartphone are highly unstructured and because smartphones are ubiquitous, they present a larger variability compared to pictures captured by a digital camera. To solve the need of organizing large smartphone photo collections automatically, we propose here a new methodology for hierarchical photo organization into topics and topic-related categories. Our approach successfully estimates latent topics in the pictures by applying probabilistic Latent Semantic Analysis, and automatically assigns a name to each topic by relying on a lexical database. Topic-related categories are then estimated by using a set of topic-specific Convolutional Neuronal Networks. To validate our approach, we ensemble and make public a large dataset of more than 8,000 smartphone pictures from 10 persons. Experimental results demonstrate better user satisfaction with respect to state of the art solutions in terms of organization. |
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MILAB; no proj |
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no |
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Admin @ si @ LRD2018 |
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3111 |
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Author |
Senmao Li; Joost van de Weijer; Taihang Hu; Fahad Shahbaz Khan; Qibin Hou; Yaxing Wang; Jian Yang |
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Title |
StyleDiffusion: Prompt-Embedding Inversion for Text-Based Editing |
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2023 |
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Arxiv |
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A significant research effort is focused on exploiting the amazing capacities of pretrained diffusion models for the editing of images. They either finetune the model, or invert the image in the latent space of the pretrained model. However, they suffer from two problems: (1) Unsatisfying results for selected regions, and unexpected changes in nonselected regions. (2) They require careful text prompt editing where the prompt should include all visual objects in the input image. To address this, we propose two improvements: (1) Only optimizing the input of the value linear network in the cross-attention layers, is sufficiently powerful to reconstruct a real image. (2) We propose attention regularization to preserve the object-like attention maps after editing, enabling us to obtain accurate style editing without invoking significant structural changes. We further improve the editing technique which is used for the unconditional branch of classifier-free guidance, as well as the conditional one as used by P2P. Extensive experimental prompt-editing results on a variety of images, demonstrate qualitatively and quantitatively that our method has superior editing capabilities than existing and concurrent works. |
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LAMP |
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no |
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Admin @ si @ LWH2023 |
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3870 |
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Author |
A. Martinez |
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Title |
Disseny d´agents autonoms. |
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1994 |
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Graduating Project |
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Master's thesis |
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Admin @ si @ Mar1994 |
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236 |
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Author |
Daniel Marczak; Sebastian Cygert; Tomasz Trzcinski; Bartlomiej Twardowski |
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Title |
Revisiting Supervision for Continual Representation Learning |
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2023 |
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Arxiv |
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In the field of continual learning, models are designed to learn tasks one after the other. While most research has centered on supervised continual learning, recent studies have highlighted the strengths of self-supervised continual representation learning. The improved transferability of representations built with self-supervised methods is often associated with the role played by the multi-layer perceptron projector. In this work, we depart from this observation and reexamine the role of supervision in continual representation learning. We reckon that additional information, such as human annotations, should not deteriorate the quality of representations. Our findings show that supervised models when enhanced with a multi-layer perceptron head, can outperform self-supervised models in continual representation learning. |
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xxx |
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Admin @ si @ MCT2023 |
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4013 |
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Author |
Enric Marti; Antoni Gurgui; Debora Gil; Aura Hernandez-Sabate; Jaume Rocarias; Ferran Poveda |
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Title |
ABP on line: Seguimiento, estregas y evaluación en aprendizaje basado en proyectos |
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2014 |
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8th International Congress on University Teaching and Innovation |
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Tarragona; juliol 2014 |
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CIDUI |
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IAM; ADAS; 600.076; 600.063; 600.075 |
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no |
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Admin @ si @ MGG2014 |
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2457 |
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Author |
Hannes Mueller; Andre Groger; Jonathan Hersh; Andrea Matranga; Joan Serrat |
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Title |
Monitoring War Destruction from Space: A Machine Learning Approach |
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2020 |
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Arxiv |
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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 data augmentation to expand training samples. We apply this method to the Syrian civil war and reconstruct the evolution of damage in major cities across the country. The approach allows generating destruction data with unprecedented scope, resolution, and frequency – only limited by the available satellite imagery – which can alleviate data limitations decisively. |
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ADAS; 600.118 |
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no |
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Admin @ si @ MGH2020 |
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3489 |
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Author |
C. Mariño; V.M. Gulias; M.G. Penas; M. Penedo; Victor Leboran; A. Mosquera; M.J. Carreira; David Lloret |
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Title |
Sistema de Interpretacion Automatica de Secuencias solo Basado en un Servidor vod. |
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2001 |
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Proceedings of the SIT2001. |
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Admin @ si @ MGP2001 |
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196 |
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Enric Marti; Ferran Poveda; Antoni Gurgui; Jaume Rocarias; Debora Gil |
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Una propuesta de seguimiento, tutorías on line y evaluación en la metodología de Aprendizaje Basado en Proyectos |
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2013 |
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IV Congreso Internacional UNIVEST |
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UNIVEST |
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IAM |
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Admin @ si @ MPG2013a |
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2304 |
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Enric Marti; Ferran Poveda; Antoni Gurgui; Jaume Rocarias; Debora Gil; Aura Hernandez-Sabate |
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Una experiencia de estructura, funcionamiento y evaluación de la asignatura de graficos por computador con metodologia de aprendizaje basado en proyectos |
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2013 |
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IV Congreso Internacional UNIVEST |
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IV Congreso Internacional UNIVEST |
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UNIVEST |
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IAM; ADAS |
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no |
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Admin @ si @ MPG2013b |
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2384 |
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C. Mariño; M.G. Penas; M. Penedo; David Lloret; M.J. Carreira |
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Integration of Mutual Information and Creaseness Based Methods for the Automatic Registration of SLO Sequences. |
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Miscellaneous |
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2001 |
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Proceedings of the SIARP´2001. |
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Brasil. |
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Admin @ si @ MPP2001 |
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197 |
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Md. Mostafa Kamal Sarker; Hatem A. Rashwan; Mohamed Abdel-Nasser; Vivek Kumar Singh; Syeda Furruka Banu; Farhan Akram; Forhad U. H. Chowdhury; Kabir Ahmed Choudhury; Sylvie Chambon; Petia Radeva; Domenec Puig |
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Title |
MobileGAN: Skin Lesion Segmentation Using a Lightweight Generative Adversarial Network |
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Miscellaneous |
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2019 |
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Arxiv |
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CoRR abs/1907.00856
Skin lesion segmentation in dermoscopic images is a challenge due to their blurry and irregular boundaries. Most of the segmentation approaches based on deep learning are time and memory consuming due to the hundreds of millions of parameters. Consequently, it is difficult to apply them to real dermatoscope devices with limited GPU and memory resources. In this paper, we propose a lightweight and efficient Generative Adversarial Networks (GAN) model, called MobileGAN for skin lesion segmentation. More precisely, the MobileGAN combines 1D non-bottleneck factorization networks with position and channel attention modules in a GAN model. The proposed model is evaluated on the test dataset of the ISBI 2017 challenges and the validation dataset of ISIC 2018 challenges. Although the proposed network has only 2.35 millions of parameters, it is still comparable with the state-of-the-art. The experimental results show that our MobileGAN obtains comparable performance with an accuracy of 97.61%. |
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Notes |
MILAB; no menciona |
Approved |
no |
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Call Number |
Admin @ si @ MRA2019 |
Serial |
3384 |
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Permanent link to this record |