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Author |
Chenshen Wu; Joost Van de Weijer |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Density Map Distillation for Incremental Object Counting |
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Conference Article |
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Year |
2023 |
Publication |
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops |
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2505-2514 |
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We investigate the problem of incremental learning for object counting, where a method must learn to count a variety of object classes from a sequence of datasets. A naïve approach to incremental object counting would suffer from catastrophic forgetting, where it would suffer from a dramatic performance drop on previous tasks. In this paper, we propose a new exemplar-free functional regularization method, called Density Map Distillation (DMD). During training, we introduce a new counter head for each task and introduce a distillation loss to prevent forgetting of previous tasks. Additionally, we introduce a cross-task adaptor that projects the features of the current backbone to the previous backbone. This projector allows for the learning of new features while the backbone retains the relevant features for previous tasks. Finally, we set up experiments of incremental learning for counting new objects. Results confirm that our method greatly reduces catastrophic forgetting and outperforms existing methods. |
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Vancouver; Canada; June 2023 |
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CVPRW |
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LAMP |
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no |
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Admin @ si @ WuW2023 |
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3916 |
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Author |
Xavier Soria; Edgar Riba; Angel Sappa |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection |
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Conference Article |
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2020 |
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IEEE Winter Conference on Applications of Computer Vision |
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This paper proposes a Deep Learning based edge detector, which is inspired on both HED (Holistically-Nested Edge Detection) and Xception networks. The proposed approach generates thin edge-maps that are plausible for human eyes; it can be used in any edge detection task without previous training or fine tuning process. As a second contribution, a large dataset with carefully annotated edges has been generated. This dataset has been used for training the proposed approach as well the state-of-the-art algorithms for comparisons. Quantitative and qualitative evaluations have been performed on different benchmarks showing improvements with the proposed method when F-measure of ODS and OIS are considered. |
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Aspen; USA; March 2020 |
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WACV |
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MSIAU; 600.130; 601.349; 600.122 |
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no |
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Admin @ si @ SRS2020 |
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3434 |
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Author |
Xavier Soria; Angel Sappa; Patricio Humanante; Arash Akbarinia |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Dense extreme inception network for edge detection |
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Journal Article |
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Year |
2023 |
Publication |
Pattern Recognition |
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PR |
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Volume |
139 |
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Pages |
109461 |
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Edge detection is the basis of many computer vision applications. State of the art predominantly relies on deep learning with two decisive factors: dataset content and network architecture. Most of the publicly available datasets are not curated for edge detection tasks. Here, we address this limitation. First, we argue that edges, contours and boundaries, despite their overlaps, are three distinct visual features requiring separate benchmark datasets. To this end, we present a new dataset of edges. Second, we propose a novel architecture, termed Dense Extreme Inception Network for Edge Detection (DexiNed), that can be trained from scratch without any pre-trained weights. DexiNed outperforms other algorithms in the presented dataset. It also generalizes well to other datasets without any fine-tuning. The higher quality of DexiNed is also perceptually evident thanks to the sharper and finer edges it outputs. |
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MSIAU |
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no |
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Admin @ si @ SSH2023 |
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3982 |
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Author |
Q. Bao; Marçal Rusiñol; M.Coustaty; Muhammad Muzzamil Luqman; C.D. Tran; Jean-Marc Ogier |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Delaunay triangulation-based features for Camera-based document image retrieval system |
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Conference Article |
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Year |
2016 |
Publication |
12th IAPR Workshop on Document Analysis Systems |
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Pages |
1-6 |
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Camera-based Document Image Retrieval; Delaunay Triangulation; Feature descriptors; Indexing |
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In this paper, we propose a new feature vector, named DElaunay TRIangulation-based Features (DETRIF), for real-time camera-based document image retrieval. DETRIF is computed based on the geometrical constraints from each pair of adjacency triangles in delaunay triangulation which is constructed from centroids of connected components. Besides, we employ a hashing-based indexing system in order to evaluate the performance of DETRIF and to compare it with other systems such as LLAH and SRIF. The experimentation is carried out on two datasets comprising of 400 heterogeneous-content complex linguistic map images (huge size, 9800 X 11768 pixels resolution)and 700 textual document images. |
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Santorini; Greece; April 2016 |
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DAS |
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DAG; 600.061; 600.084; 600.077 |
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no |
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Call Number |
Admin @ si @ BRC2016 |
Serial |
2757 |
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Author |
Jon Almazan |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Deforming the Blurred Shape Model for Shape Description and Recognition |
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Report |
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2010 |
Publication |
CVC Technical Report |
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Volume |
163 |
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Master's thesis |
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no |
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Admin @ si @ Alm2010 |
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1354 |
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Author |
Jon Almazan; Ernest Valveny; Alicia Fornes |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Deforming the Blurred Shape Model for Shape Description and Recognition |
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Conference Article |
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2011 |
Publication |
5th Iberian Conference on Pattern Recognition and Image Analysis |
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Volume |
6669 |
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1-8 |
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This paper presents a new model for the description and recognition of distorted shapes, where the image is represented by a pixel density distribution based on the Blurred Shape Model combined with a non-linear image deformation model. This leads to an adaptive structure able to capture elastic deformations in shapes. This method has been evaluated using thee different datasets where deformations are present, showing the robustness and good performance of the new model. Moreover, we show that incorporating deformation and flexibility, the new model outperforms the BSM approach when classifying shapes with high variability of appearance. |
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Las Palmas de Gran Canaria. Spain |
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Springer-Verlag |
Place of Publication |
Berlin |
Editor |
Jordi Vitria; Joao Miguel Raposo; Mario Hernandez |
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LNCS |
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IbPRIA |
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DAG; |
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no |
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Admin @ si @ AVF2011 |
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1732 |
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Author |
Ernest Valveny; Enric Marti |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Deformable Template Matching within a Bayesian Framework for Hand-Written Graphic Symbol Recognition |
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Journal Article |
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2000 |
Publication |
Graphics Recognition Recent Advances |
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Volume |
1941 |
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193-208 |
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We describe a method for hand-drawn symbol recognition based on deformable template matching able to handle uncertainty and imprecision inherent to hand-drawing. Symbols are represented as a set of straight lines and their deformations as geometric transformations of these lines. Matching, however, is done over the original binary image to avoid loss of information during line detection. It is defined as an energy minimization problem, using a Bayesian framework which allows to combine fidelity to ideal shape of the symbol and flexibility to modify the symbol in order to get the best fit to the binary input image. Prior to matching, we find the best global transformation of the symbol to start the recognition process, based on the distance between symbol lines and image lines. We have applied this method to the recognition of dimensions and symbols in architectural floor plans and we show its flexibility to recognize distorted symbols. |
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Springer Verlag |
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Springer Verlag |
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DAG;IAM; |
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no |
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Call Number |
IAM @ iam @ MVA2000 |
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1655 |
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Permanent link to this record |
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Author |
Cristina Cañero |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Deformable models applied in Medical Imaging |
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Report |
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1999 |
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CVC Technical Report #33 |
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CVC (UAB) |
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Admin @ si @ Can1999 |
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193 |
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Author |
Petia Radeva; Amir Amini; Jintao Huang; Enric Marti |
![download PDF file pdf](img/file_PDF.gif)
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Deformable B-Solids: application for localization and tracking of MRI-SPAMM data |
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Report |
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1996 |
Publication |
CVC Technical Report |
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8 |
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To date, MRI-SPAMM data from different image slices have been analyzed independently. In this paper, we propose an approach for 3D tag localization and tracking of SPAMM data by a novel deformable B-solid. The solid is defined in terms of a 3D tensor product B-spline. The isoparametric curves of the B-spline solid have special importance. These are termed implicit snakes as they deform under image forces from tag lines in different image slices. The localization and tracking of tag lines is performed under constraints of continuity and smoothness of the B-solid. The framework unifies the problems of localization, and displacement fitting and interpolation into the same procedure utilizing B-spline bases for interpolation. To track motion from boundaries and restrict image forces to the myocardium, a volumetric model is employed as a pair of coupled endocardial and epicardial B-spline surfaces. To recover deformations in the LV an energy-minimization problem is posed where both tag and ... |
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CVC (UAB) |
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MILAB;IAM |
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no |
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IAM @ iam @ RHM1996 |
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1631 |
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Author |
Petia Radeva; A.Amini; J.Huang; Enric Marti |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Deformable B-Solids and Implicit Snakes for Localization and Tracking of SPAMM MRI-Data |
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Conference Article |
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1996 |
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Workshop on Mathematical Methods in Biomedical Image Analysis |
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192-201 |
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To date, MRI-SPAMM data from different image slices have been analyzed independently. In this paper, we propose an approach for 3D tag localization and tracking of SPAMM data by a novel deformable B-solid. The solid is defined in terms of a 3D tensor product B-spline. The isoparametric curves of the B-spline solid have special importance. These are termed implicit snakes as they deform under image forces from tag lines in different image slices. The localization and tracking of tag lines is performed under constraints of continuity and smoothness of the B-solid. The framework unifies the problems of localization, and displacement fitting and interpolation into the same procedure utilizing B-spline bases for interpolation. To track motion from boundaries and restrict image forces to the myocardium, a volumetric model is employed as a pair of coupled endocardial and epicardial B-spline surfaces. To recover deformations in the LV an energy-minimization problem is posed where both tag and ... |
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San Francisco CA |
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IEEE Computer Society |
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0-8186-7368-0 |
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MMBIA ’96 |
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MILAB;IAM; |
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no |
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IAM @ iam @ RAH1996 |
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1630 |
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Author |
C. Cortes |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Definicio d´un sensor de visio artificial per a l´ajust automatic de tintes en la impressio de pape |
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2001 |
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CVC Technical Report #53 |
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CVC (UAB) |
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Admin @ si @ Cor2001 |
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184 |
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Author |
Hugo Bertiche; Meysam Madadi; Emilio Tylson; Sergio Escalera |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
DeePSD: Automatic Deep Skinning And Pose Space Deformation For 3D Garment Animation |
Type |
Conference Article |
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Year |
2021 |
Publication |
19th IEEE International Conference on Computer Vision |
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5471-5480 |
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We present a novel solution to the garment animation problem through deep learning. Our contribution allows animating any template outfit with arbitrary topology and geometric complexity. Recent works develop models for garment edition, resizing and animation at the same time by leveraging the support body model (encoding garments as body homotopies). This leads to complex engineering solutions that suffer from scalability, applicability and compatibility. By limiting our scope to garment animation only, we are able to propose a simple model that can animate any outfit, independently of its topology, vertex order or connectivity. Our proposed architecture maps outfits to animated 3D models into the standard format for 3D animation (blend weights and blend shapes matrices), automatically providing of compatibility with any graphics engine. We also propose a methodology to complement supervised learning with an unsupervised physically based learning that implicitly solves collisions and enhances cloth quality. |
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Virtual; October 2021 |
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ICCV |
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HUPBA; no menciona |
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no |
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Admin @ si @ BMT2021 |
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3606 |
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Author |
Margarita Torre; Beatriz Remeseiro; Petia Radeva; Fernando Martinez |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
DeepNEM: Deep Network Energy-Minimization for Agricultural Field Segmentation |
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Journal Article |
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Year |
2020 |
Publication |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
Abbreviated Journal |
JSTAEOR |
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13 |
Issue |
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726-737 |
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Abstract |
One of the main characteristics of agricultural fields is that the appearance of different crops and their growth status, in an aerial image, is varied, and has a wide range of radiometric values and high level of variability. The extraction of these fields and their monitoring are activities that require a high level of human intervention. In this article, we propose a novel automatic algorithm, named deep network energy-minimization (DeepNEM), to extract agricultural fields in aerial images. The model-guided process selects the most relevant image clues extracted by a deep network, completes them and finally generates regions that represent the agricultural fields under a minimization scheme. DeepNEM has been tested over a broad range of fields in terms of size, shape, and content. Different measures were used to compare the DeepNEM with other methods, and to prove that it represents an improved approach to achieve a high-quality segmentation of agricultural fields. Furthermore, this article also presents a new public dataset composed of 1200 images with their parcels boundaries annotations. |
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MILAB |
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Admin @ si @ TRR2020 |
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3410 |
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Author |
Yaxing Wang; Lu Yu; Joost Van de Weijer |
![download PDF file pdf](img/file_PDF.gif)
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DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs |
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2020 |
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34th Conference on Neural Information Processing Systems |
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Image-to-image translation has recently achieved remarkable results. But despite current success, it suffers from inferior performance when translations between classes require large shape changes. We attribute this to the high-resolution bottlenecks which are used by current state-of-the-art image-to-image methods. Therefore, in this work, we propose a novel deep hierarchical Image-to-Image Translation method, called DeepI2I. We learn a model by leveraging hierarchical features: (a) structural information contained in the shallow layers and (b) semantic information extracted from the deep layers. To enable the training of deep I2I models on small datasets, we propose a novel transfer learning method, that transfers knowledge from pre-trained GANs. Specifically, we leverage the discriminator of a pre-trained GANs (i.e. BigGAN or StyleGAN) to initialize both the encoder and the discriminator and the pre-trained generator to initialize the generator of our model. Applying knowledge transfer leads to an alignment problem between the encoder and generator. We introduce an adaptor network to address this. On many-class image-to-image translation on three datasets (Animal faces, Birds, and Foods) we decrease mFID by at least 35% when compared to the state-of-the-art. Furthermore, we qualitatively and quantitatively demonstrate that transfer learning significantly improves the performance of I2I systems, especially for small datasets. Finally, we are the first to perform I2I translations for domains with over 100 classes. |
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virtual; December 2020 |
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LAMP; 600.120 |
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Admin @ si @ WYW2020 |
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3485 |
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Meysam Madadi; Hugo Bertiche; Sergio Escalera |
![download PDF file pdf](img/file_PDF.gif)
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Deep unsupervised 3D human body reconstruction from a sparse set of landmarks |
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2021 |
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International Journal of Computer Vision |
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IJCV |
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129 |
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2499–2512 |
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In this paper we propose the first deep unsupervised approach in human body reconstruction to estimate body surface from a sparse set of landmarks, so called DeepMurf. We apply a denoising autoencoder to estimate missing landmarks. Then we apply an attention model to estimate body joints from landmarks. Finally, a cascading network is applied to regress parameters of a statistical generative model that reconstructs body. Our set of proposed loss functions allows us to train the network in an unsupervised way. Results on four public datasets show that our approach accurately reconstructs the human body from real world mocap data. |
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HUPBA; no proj |
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Admin @ si @ MBE2021 |
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3654 |
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