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Author |
Laura Igual; Joan Carles Soliva; Antonio Hernandez; Sergio Escalera; Oscar Vilarroya; Petia Radeva |
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Title  |
Supervised Brain Segmentation and Classification in Diagnostic of Attention-Deficit/Hyperactivity Disorder |
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Conference Article |
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2012 |
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High Performance Computing and Simulation, International Conference on |
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182-187 |
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This paper presents an automatic method for external and internal segmentation of the caudate nucleus in Magnetic Resonance Images (MRI) based on statistical and structural machine learning approaches. This method is applied in Attention-Deficit/Hyperactivity Disorder (ADHD) diagnosis. The external segmentation method adapts the Graph Cut energy-minimization model to make it suitable for segmenting small, low-contrast structures, such as the caudate nucleus. In particular, new energy function data and boundary potentials are defined and a supervised energy term based on contextual brain structures is added. Furthermore, the internal segmentation method learns a classifier based on shape features of the Region of Interest (ROI) in MRI slices. The results show accurate external and internal caudate segmentation in a real data set and similar performance of ADHD diagnostic test to manual annotation. |
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Madrid |
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IEEE Xplore |
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978-1-4673-2359-8 |
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HPCS |
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MILAB;HuPBA |
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no |
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Admin @ si @ ISH2012a |
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2038 |
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Author |
H. Emrah Tasli; Cevahir Çigla; Theo Gevers; A. Aydin Alatan |
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Title |
Super pixel extraction via convexity induced boundary adaptation |
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Conference Article |
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2013 |
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14th IEEE International Conference on Multimedia and Expo |
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1-6 |
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This study presents an efficient super-pixel extraction algorithm with major contributions to the state-of-the-art in terms of accuracy and computational complexity. Segmentation accuracy is improved through convexity constrained geodesic distance utilization; while computational efficiency is achieved by replacing complete region processing with boundary adaptation idea. Starting from the uniformly distributed rectangular equal-sized super-pixels, region boundaries are adapted to intensity edges iteratively by assigning boundary pixels to the most similar neighboring super-pixels. At each iteration, super-pixel regions are updated and hence progressively converging to compact pixel groups. Experimental results with state-of-the-art comparisons, validate the performance of the proposed technique in terms of both accuracy and speed. |
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San Jose; USA; July 2013 |
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1945-7871 |
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ICME |
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ALTRES;ISE |
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Admin @ si @ TÇG2013 |
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2367 |
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Author |
Fadi Dornaika; Bogdan Raducanu |
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Title |
Subtle Facial Expression Recognition in Still Images and Videos |
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Book Chapter |
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2011 |
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Advances in Face Image Analysis: Techniques and Technologies |
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14 |
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259-277 |
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This chapter addresses the recognition of basic facial expressions. It has three main contributions. First, the authors introduce a view- and texture independent schemes that exploits facial action parameters estimated by an appearance-based 3D face tracker. they represent the learned facial actions associated with different facial expressions by time series. Two dynamic recognition schemes are proposed: (1) the first is based on conditional predictive models and on an analysis-synthesis scheme, and (2) the second is based on examples allowing straightforward use of machine learning approaches. Second, the authors propose an efficient recognition scheme based on the detection of keyframes in videos. Third, the authors compare the dynamic scheme with a static one based on analyzing individual snapshots and show that in general the former performs better than the latter. The authors then provide evaluations of performance using Linear Discriminant Analysis (LDA), Non parametric Discriminant Analysis (NDA), and Support Vector Machines (SVM). |
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IGI-Global |
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New York, USA |
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Yu-Jin Zhang |
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978-1-6152-0991-0 |
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OR;MV |
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Admin @ si @ DoR2011 |
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1751 |
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Author |
Lluis Gomez; Marçal Rusiñol; Ali Furkan Biten; Dimosthenis Karatzas |
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Title |
Subtitulació automàtica d'imatges. Estat de l'art i limitacions en el context arxivístic |
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Conference Article |
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2018 |
Publication |
Jornades Imatge i Recerca |
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JIR |
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DAG; 600.084; 600.135; 601.338; 600.121; 600.129 |
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Admin @ si @ GRB2018 |
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3173 |
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Author |
Anna Salvatella; Maria Vanrell; Ramon Baldrich |
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Title |
Subtexture Components for Texture Description |
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Miscellaneous |
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2003 |
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Lecture Notes in Computer Science, vol 2652, pp 884–892 |
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Springer-Verlag |
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CIC |
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CAT @ cat @ SVR2003 |
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421 |
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Author |
Xavier Perez Sala; Fernando De la Torre; Laura Igual; Sergio Escalera; Cecilio Angulo |
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Title |
Subspace Procrustes Analysis |
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Conference Article |
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Year |
2014 |
Publication |
ECCV Workshop on ChaLearn Looking at People |
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8925 |
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654-668 |
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Procrustes Analysis (PA) has been a popular technique to align and build 2-D statistical models of shapes. Given a set of 2-D shapes PA is applied to remove rigid transformations. Then, a non-rigid 2-D model is computed by modeling (e.g., PCA) the residual. Although PA has been widely used, it has several limitations for modeling 2-D shapes: occluded landmarks and missing data can result in local minima solutions, and there is no guarantee that the 2-D shapes provide a uniform sampling of the 3-D space of rotations for the object. To address previous issues, this paper proposes Subspace PA (SPA). Given several instances of a 3-D object, SPA computes the mean and a 2-D subspace that can simultaneously model all rigid and non-rigid deformations of the 3-D object. We propose a discrete (DSPA) and continuous (CSPA) formulation for SPA, assuming that 3-D samples of an object are provided. DSPA extends the traditional PA, and produces unbiased 2-D models by uniformly sampling di erent views of the 3-D object. CSPA provides a continuous approach to uniformly sample the space of 3-D rotations, being more ecient in space and time. Experiments using SPA to learn 2-D models of bodies from motion capture data illustrate the bene ts of our approach. |
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ECCVW |
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OR; HuPBA;MILAB |
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no |
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Call Number |
Admin @ si @ PTI2014 |
Serial |
2539 |
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Author |
Xavier Perez Sala; Fernando De la Torre; Laura Igual; Sergio Escalera; Cecilio Angulo |
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Title |
Subspace Procrustes Analysis |
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Journal Article |
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Year |
2017 |
Publication |
International Journal of Computer Vision |
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IJCV |
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Volume |
121 |
Issue |
3 |
Pages |
327–343 |
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Abstract |
Procrustes Analysis (PA) has been a popular technique to align and build 2-D statistical models of shapes. Given a set of 2-D shapes PA is applied to remove rigid transformations. Then, a non-rigid 2-D model is computed by modeling (e.g., PCA) the residual. Although PA has been widely used, it has several limitations for modeling 2-D shapes: occluded landmarks and missing data can result in local minima solutions, and there is no guarantee that the 2-D shapes provide a uniform sampling of the 3-D space of rotations for the object. To address previous issues, this paper proposes Subspace PA (SPA). Given several
instances of a 3-D object, SPA computes the mean and a 2-D subspace that can simultaneously model all rigid and non-rigid deformations of the 3-D object. We propose a discrete (DSPA) and continuous (CSPA) formulation for SPA, assuming that 3-D samples of an object are provided. DSPA extends the traditional PA, and produces unbiased 2-D models by uniformly sampling different views of the 3-D object. CSPA provides a continuous approach to uniformly sample the space of 3-D rotations, being more efficient in space and time. Experiments using SPA to learn 2-D models of bodies from motion capture data illustrate the benefits of our approach. |
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MILAB; HuPBA; no proj |
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no |
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Admin @ si @ PTI2017 |
Serial |
2841 |
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Author |
Agata Lapedriza; David Masip; Jordi Vitria |
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Title |
Subject Recognition Using a New Approach for Feature Extraction |
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Conference Article |
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Year |
2008 |
Publication |
3rd International Conference on Computer Vision Theory and Applications |
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2 |
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61–66 |
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Madeira (Portugal) |
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VISAPP |
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OR; MV |
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BCNPCL @ bcnpcl @ LMV2008a |
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980 |
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Author |
Muhammad Muzzamil Luqman; Jean-Yves Ramel; Josep Llados; Thierry Brouard |
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Title |
Subgraph Spotting Through Explicit Graph Embedding: An Application to Content Spotting in Graphic Document Images |
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Conference Article |
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Year |
2011 |
Publication |
11th International Conference on Document Analysis and Recognition |
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870-874 |
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We present a method for spotting a subgraph in a graph repository. Subgraph spotting is a very interesting research problem for various application domains where the use of a relational data structure is mandatory. Our proposed method accomplishes subgraph spotting through graph embedding. We achieve automatic indexation of a graph repository during off-line learning phase, where we (i) break the graphs into 2-node sub graphs (a.k.a. cliques of order 2), which are primitive building-blocks of a graph, (ii) embed the 2-node sub graphs into feature vectors by employing our recently proposed explicit graph embedding technique, (iii) cluster the feature vectors in classes by employing a classic agglomerative clustering technique, (iv) build an index for the graph repository and (v) learn a Bayesian network classifier. The subgraph spotting is achieved during the on-line querying phase, where we (i) break the query graph into 2-node sub graphs, (ii) embed them into feature vectors, (iii) employ the Bayesian network classifier for classifying the query 2-node sub graphs and (iv) retrieve the respective graphs by looking-up in the index of the graph repository. The graphs containing all query 2-node sub graphs form the set of result graphs for the query. Finally, we employ the adjacency matrix of each result graph along with a score function, for spotting the query graph in it. The proposed subgraph spotting method is equally applicable to a wide range of domains, offering ease of query by example (QBE) and granularity of focused retrieval. Experimental results are presented for graphs generated from two repositories of electronic and architectural document images. |
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Beijing, China |
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1520-5363 |
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978-1-4577-1350-7 |
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ICDAR |
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DAG |
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Admin @ si @ LRL2011 |
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1790 |
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Author |
Thanh Nam Le; Muhammad Muzzamil Luqman; Anjan Dutta; Pierre Heroux; Christophe Rigaud; Clement Guerin; Pasquale Foggia; Jean Christophe Burie; Jean Marc Ogier; Josep Llados; Sebastien Adam |
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Subgraph spotting in graph representations of comic book images |
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Journal Article |
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2018 |
Publication |
Pattern Recognition Letters |
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PRL |
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112 |
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118-124 |
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Attributed graph; Region adjacency graph; Graph matching; Graph isomorphism; Subgraph isomorphism; Subgraph spotting; Graph indexing; Graph retrieval; Query by example; Dataset and comic book images |
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Graph-based representations are the most powerful data structures for extracting, representing and preserving the structural information of underlying data. Subgraph spotting is an interesting research problem, especially for studying and investigating the structural information based content-based image retrieval (CBIR) and query by example (QBE) in image databases. In this paper we address the problem of lack of freely available ground-truthed datasets for subgraph spotting and present a new dataset for subgraph spotting in graph representations of comic book images (SSGCI) with its ground-truth and evaluation protocol. Experimental results of two state-of-the-art methods of subgraph spotting are presented on the new SSGCI dataset. |
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DAG; 600.097; 600.121 |
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Admin @ si @ LLD2018 |
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3150 |
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Sergio Escalera; David M.J. Tax; Oriol Pujol; Petia Radeva; Robert P.W. Duin |
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Subclass Problem-Dependent Design for Error-Correcting Output Codes |
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2008 |
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IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.30(6):1041–1054 |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ ETP2008 |
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951 |
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Sergio Escalera; Oriol Pujol; Petia Radeva |
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Sub-Class Error-Correcting Output Codes |
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2008 |
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Computer Vision Systems. 6th International Conference |
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5008 |
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494–504 |
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Santorini (Greece) |
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ICVS |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ EPR2008c |
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963 |
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Senmao Li; Joost van de Weijer; Taihang Hu; Fahad Shahbaz Khan; Qibin Hou; Yaxing Wang; Jian Yang |
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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 |
Approved |
no |
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Call Number |
Admin @ si @ LWH2023 |
Serial |
3870 |
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Author |
Francesc Tous |
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Title |
Study of Colour Normalisation for Skin Detection. |
Type |
Miscellaneous |
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Year |
2002 |
Publication |
Director: M. Vanrell, Master Thesis. |
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Approved |
no |
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Call Number |
Admin @ si @ Tou2002 |
Serial |
325 |
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Author |
R. Clariso; David Masip; A. Rius |
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Title |
Student projects empowering mobile learning in higher education |
Type |
Journal |
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Year |
2014 |
Publication |
Revista de Universidad y Sociedad del Conocimiento |
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RUSC |
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Volume |
11 |
Issue |
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Pages |
192-207 |
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1698-580X |
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OR;MV |
Approved |
no |
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Call Number |
Admin @ si @ CMR2014 |
Serial |
2619 |
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| Permanent link to this record |
