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Author Xiangyang Li; Luis Herranz; Shuqiang Jiang edit   pdf
url  openurl
  Title Multifaceted Analysis of Fine-Tuning in Deep Model for Visual Recognition Type (up) Journal
  Year 2020 Publication ACM Transactions on Data Science Abbreviated Journal ACM  
  Volume Issue Pages  
  Keywords  
  Abstract In recent years, convolutional neural networks (CNNs) have achieved impressive performance for various visual recognition scenarios. CNNs trained on large labeled datasets can not only obtain significant performance on most challenging benchmarks but also provide powerful representations, which can be used to a wide range of other tasks. However, the requirement of massive amounts of data to train deep neural networks is a major drawback of these models, as the data available is usually limited or imbalanced. Fine-tuning (FT) is an effective way to transfer knowledge learned in a source dataset to a target task. In this paper, we introduce and systematically investigate several factors that influence the performance of fine-tuning for visual recognition. These factors include parameters for the retraining procedure (e.g., the initial learning rate of fine-tuning), the distribution of the source and target data (e.g., the number of categories in the source dataset, the distance between the source and target datasets) and so on. We quantitatively and qualitatively analyze these factors, evaluate their influence, and present many empirical observations. The results reveal insights into what fine-tuning changes CNN parameters and provide useful and evidence-backed intuitions about how to implement fine-tuning for computer vision tasks.  
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  Area Expedition Conference  
  Notes LAMP; 600.141; 600.120 Approved no  
  Call Number Admin @ si @ LHJ2020 Serial 3423  
Permanent link to this record
 

 
Author Aymen Azaza; Joost Van de Weijer; Ali Douik; Javad Zolfaghari Bengar; Marc Masana edit  url
openurl 
  Title Saliency from High-Level Semantic Image Features Type (up) Journal
  Year 2020 Publication SN Computer Science Abbreviated Journal SN  
  Volume 1 Issue 4 Pages 1-12  
  Keywords  
  Abstract Top-down semantic information is known to play an important role in assigning saliency. Recently, large strides have been made in improving state-of-the-art semantic image understanding in the fields of object detection and semantic segmentation. Therefore, since these methods have now reached a high-level of maturity, evaluation of the impact of high-level image understanding on saliency estimation is now feasible. We propose several saliency features which are computed from object detection and semantic segmentation results. We combine these features with a standard baseline method for saliency detection to evaluate their importance. Experiments demonstrate that the proposed features derived from object detection and semantic segmentation improve saliency estimation significantly. Moreover, they show that our method obtains state-of-the-art results on (FT, ImgSal, and SOD datasets) and obtains competitive results on four other datasets (ECSSD, PASCAL-S, MSRA-B, and HKU-IS).  
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  Area Expedition Conference  
  Notes LAMP; 600.120; 600.109; 600.106 Approved no  
  Call Number Admin @ si @ AWD2020 Serial 3503  
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Author Juan Ramon Terven Salinas; Joaquin Salas; Bogdan Raducanu edit   pdf
url  doi
openurl 
  Title New Opportunities for Computer Vision-Based Assistive Technology Systems for the Visually Impaired Type (up) Journal Article
  Year 2014 Publication Computer Abbreviated Journal COMP  
  Volume 47 Issue 4 Pages 52-58  
  Keywords  
  Abstract Computing advances and increased smartphone use gives technology system designers greater flexibility in exploiting computer vision to support visually impaired users. Understanding these users' needs will certainly provide insight for the development of improved usability of computing devices.  
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  Series Volume Series Issue Edition  
  ISSN 0018-9162 ISBN Medium  
  Area Expedition Conference  
  Notes LAMP; Approved no  
  Call Number Admin @ si @ TSR2014a Serial 2317  
Permanent link to this record
 

 
Author Bogdan Raducanu; Fadi Dornaika edit   pdf
doi  openurl
  Title Embedding new observations via sparse-coding for non-linear manifold learning Type (up) Journal Article
  Year 2014 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 47 Issue 1 Pages 480-492  
  Keywords  
  Abstract Non-linear dimensionality reduction techniques are affected by two critical aspects: (i) the design of the adjacency graphs, and (ii) the embedding of new test data-the out-of-sample problem. For the first aspect, the proposed solutions, in general, were heuristically driven. For the second aspect, the difficulty resides in finding an accurate mapping that transfers unseen data samples into an existing manifold. Past works addressing these two aspects were heavily parametric in the sense that the optimal performance is only achieved for a suitable parameter choice that should be known in advance. In this paper, we demonstrate that the sparse representation theory not only serves for automatic graph construction as shown in recent works, but also represents an accurate alternative for out-of-sample embedding. Considering for a case study the Laplacian Eigenmaps, we applied our method to the face recognition problem. To evaluate the effectiveness of the proposed out-of-sample embedding, experiments are conducted using the K-nearest neighbor (KNN) and Kernel Support Vector Machines (KSVM) classifiers on six public face datasets. The experimental results show that the proposed model is able to achieve high categorization effectiveness as well as high consistency with non-linear embeddings/manifolds obtained in batch modes.  
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  Series Editor Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes LAMP; Approved no  
  Call Number Admin @ si @ RaD2013b Serial 2316  
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Author Simone Balocco; Carlo Gatta; Francesco Ciompi; A. Wahle; Petia Radeva; S. Carlier; G. Unal; E. Sanidas; J. Mauri; X. Carillo; T. Kovarnik; C. Wang; H. Chen; T. P. Exarchos; D. I. Fotiadis; F. Destrempes; G. Cloutier; Oriol Pujol; Marina Alberti; E. G. Mendizabal-Ruiz; M. Rivera; T. Aksoy; R. W. Downe; I. A. Kakadiaris edit   pdf
doi  openurl
  Title Standardized evaluation methodology and reference database for evaluating IVUS image segmentation Type (up) Journal Article
  Year 2014 Publication Computerized Medical Imaging and Graphics Abbreviated Journal CMIG  
  Volume 38 Issue 2 Pages 70-90  
  Keywords IVUS (intravascular ultrasound); Evaluation framework; Algorithm comparison; Image segmentation  
  Abstract This paper describes an evaluation framework that allows a standardized and quantitative comparison of IVUS lumen and media segmentation algorithms. This framework has been introduced at the MICCAI 2011 Computing and Visualization for (Intra)Vascular Imaging (CVII) workshop, comparing the results of eight teams that participated.
We describe the available data-base comprising of multi-center, multi-vendor and multi-frequency IVUS datasets, their acquisition, the creation of the reference standard and the evaluation measures. The approaches address segmentation of the lumen, the media, or both borders; semi- or fully-automatic operation; and 2-D vs. 3-D methodology. Three performance measures for quantitative analysis have
been proposed. The results of the evaluation indicate that segmentation of the vessel lumen and media is possible with an accuracy that is comparable to manual annotation when semi-automatic methods are used, as well as encouraging results can be obtained also in case of fully-automatic segmentation. The analysis performed in this paper also highlights the challenges in IVUS segmentation that remains to be
solved.
 
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  Area Expedition Conference  
  Notes MILAB; LAMP; HuPBA; 600.046; 600.063; 600.079 Approved no  
  Call Number Admin @ si @ BGC2013 Serial 2314  
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