| |
Records |
Links |
|
Author |
Xiangyang Li; Luis Herranz; Shuqiang Jiang |
|
| |
Title |
Multifaceted Analysis of Fine-Tuning in Deep Model for Visual Recognition |
Type |
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. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
LAMP; 600.141; 600.120 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ LHJ2020 |
Serial |
3423 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Simone Balocco; Carlo Gatta; Francesco Ciompi; A. Wahle; Petia Radeva; S. Carlier; G. Unal; E. Sanidas; F. 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 |
|
| |
Title |
Standardized evaluation methodology and reference database for evaluating IVUS image segmentation |
Type |
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. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
MILAB; LAMP; HuPBA; 600.046; 600.063; 600.079 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ BGC2013 |
Serial |
2314 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Simeon Petkov; Xavier Carrillo; Petia Radeva; Carlo Gatta |
|
| |
Title |
Diaphragm border detection in coronary X-ray angiographies: New method and applications |
Type |
Journal Article |
| |
Year |
2014 |
Publication |
Computerized Medical Imaging and Graphics |
Abbreviated Journal |
CMIG |
|
| |
Volume |
38 |
Issue |
4 |
Pages |
296-305 |
|
| |
Keywords |
|
|
| |
Abstract |
X-ray angiography is widely used in cardiac disease diagnosis during or prior to intravascular interventions. The diaphragm motion and the heart beating induce gray-level changes, which are one of the main obstacles in quantitative analysis of myocardial perfusion. In this paper we focus on detecting the diaphragm border in both single images or whole X-ray angiography sequences. We show that the proposed method outperforms state of the art approaches. We extend a previous publicly available data set, adding new ground truth data. We also compose another set of more challenging images, thus having two separate data sets of increasing difficulty. Finally, we show three applications of our method: (1) a strategy to reduce false positives in vessel enhanced images; (2) a digital diaphragm removal algorithm; (3) an improvement in Myocardial Blush Grade semi-automatic estimation. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
MILAB; LAMP; 600.079 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ PCR2014 |
Serial |
2468 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Aymen Azaza; Joost Van de Weijer; Ali Douik; Marc Masana |
|
| |
Title |
Context Proposals for Saliency Detection |
Type |
Journal Article |
| |
Year |
2018 |
Publication |
Computer Vision and Image Understanding |
Abbreviated Journal |
CVIU |
|
| |
Volume |
174 |
Issue |
|
Pages |
1-11 |
|
| |
Keywords |
|
|
| |
Abstract |
One of the fundamental properties of a salient object region is its contrast
with the immediate context. The problem is that numerous object regions
exist which potentially can all be salient. One way to prevent an exhaustive
search over all object regions is by using object proposal algorithms. These
return a limited set of regions which are most likely to contain an object. Several saliency estimation methods have used object proposals. However, they focus on the saliency of the proposal only, and the importance of its immediate context has not been evaluated.
In this paper, we aim to improve salient object detection. Therefore, we extend object proposal methods with context proposals, which allow to incorporate the immediate context in the saliency computation. We propose several saliency features which are computed from the context proposals. In the experiments, we evaluate five object proposal methods for the task of saliency segmentation, and find that Multiscale Combinatorial Grouping outperforms the others. Furthermore, experiments show that the proposed context features improve performance, and that our method matches results on the FT datasets and obtains competitive results on three other datasets (PASCAL-S, MSRA-B and ECSSD). |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
LAMP; 600.109; 600.109; 600.120 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ AWD2018 |
Serial |
3241 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Yaxing Wang; Abel Gonzalez-Garcia; Luis Herranz; Joost Van de Weijer |
|
| |
Title |
Controlling biases and diversity in diverse image-to-image translation |
Type |
Journal Article |
| |
Year |
2021 |
Publication |
Computer Vision and Image Understanding |
Abbreviated Journal |
CVIU |
|
| |
Volume |
202 |
Issue |
|
Pages |
103082 |
|
| |
Keywords |
|
|
| |
Abstract |
JCR 2019 Q2, IF=3.121
The task of unpaired image-to-image translation is highly challenging due to the lack of explicit cross-domain pairs of instances. We consider here diverse image translation (DIT), an even more challenging setting in which an image can have multiple plausible translations. This is normally achieved by explicitly disentangling content and style in the latent representation and sampling different styles codes while maintaining the image content. Despite the success of current DIT models, they are prone to suffer from bias. In this paper, we study the problem of bias in image-to-image translation. Biased datasets may add undesired changes (e.g. change gender or race in face images) to the output translations as a consequence of the particular underlying visual distribution in the target domain. In order to alleviate the effects of this problem we propose the use of semantic constraints that enforce the preservation of desired image properties. Our proposed model is a step towards unbiased diverse image-to-image translation (UDIT), and results in less unwanted changes in the translated images while still performing the wanted transformation. Experiments on several heavily biased datasets show the effectiveness of the proposed techniques in different domains such as faces, objects, and scenes. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
LAMP; 600.141; 600.109; 600.147 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ WGH2021 |
Serial |
3464 |
|
| Permanent link to this record |
