|
Records |
Links |
|
Author |
Fei Yang; Yongmei Cheng; Joost Van de Weijer; Mikhail Mozerov |
![goto web page url](http://refbase.cvc.uab.es/img/www.gif)
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
|
|
Title |
Improved Discrete Optical Flow Estimation With Triple Image Matching Cost |
Type |
Journal Article |
|
Year |
2020 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
|
|
Volume ![sorted by Volume (numeric) field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
8 |
Issue |
|
Pages |
17093 - 17102 |
|
|
Keywords |
|
|
|
Abstract |
Approaches that use more than two consecutive video frames in the optical flow estimation have a long research history. However, almost all such methods utilize extra information for a pre-processing flow prediction or for a post-processing flow correction and filtering. In contrast, this paper differs from previously developed techniques. We propose a new algorithm for the likelihood function calculation (alternatively the matching cost volume) that is used in the maximum a posteriori estimation. We exploit the fact that in general, optical flow is locally constant in the sense of time and the likelihood function depends on both the previous and the future frame. Implementation of our idea increases the robustness of optical flow estimation. As a result, our method outperforms 9% over the DCFlow technique, which we use as prototype for our CNN based computation architecture, on the most challenging MPI-Sintel dataset for the non-occluded mask metric. Furthermore, our approach considerably increases the accuracy of the flow estimation for the matching cost processing, consequently outperforming the original DCFlow algorithm results up to 50% in occluded regions and up to 9% in non-occluded regions on the MPI-Sintel dataset. The experimental section shows that the proposed method achieves state-of-the-arts results especially on the MPI-Sintel dataset. |
|
|
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; 600.120 |
Approved |
no |
|
|
Call Number |
Admin @ si @ YCW2020 |
Serial |
3345 |
|
Permanent link to this record |
|
|
|
|
Author |
Aymen Azaza; Joost Van de Weijer; Ali Douik; Javad Zolfaghari Bengar; Marc Masana |
![goto web page url](http://refbase.cvc.uab.es/img/www.gif)
|
|
Title |
Saliency from High-Level Semantic Image Features |
Type |
Journal |
|
Year |
2020 |
Publication |
SN Computer Science |
Abbreviated Journal |
SN |
|
|
Volume ![sorted by Volume (numeric) field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
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). |
|
|
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; 600.120; 600.109; 600.106 |
Approved |
no |
|
|
Call Number |
Admin @ si @ AWD2020 |
Serial |
3503 |
|
Permanent link to this record |
|
|
|
|
Author |
Xiangyang Li; Luis Herranz; Shuqiang Jiang |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
|
|
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 ![sorted by Volume (numeric) field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
|
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 |
Marc Masana; Xialei Liu; Bartlomiej Twardowski; Mikel Menta; Andrew Bagdanov; Joost Van de Weijer |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
|
|
Title |
Class-incremental learning: survey and performance evaluation |
Type |
Journal Article |
|
Year |
2022 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
|
|
Volume ![sorted by Volume (numeric) field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
For future learning systems incremental learning is desirable, because it allows for: efficient resource usage by eliminating the need to retrain from scratch at the arrival of new data; reduced memory usage by preventing or limiting the amount of data required to be stored -- also important when privacy limitations are imposed; and learning that more closely resembles human learning. The main challenge for incremental learning is catastrophic forgetting, which refers to the precipitous drop in performance on previously learned tasks after learning a new one. Incremental learning of deep neural networks has seen explosive growth in recent years. Initial work focused on task incremental learning, where a task-ID is provided at inference time. Recently we have seen a shift towards class-incremental learning where the learner must classify at inference time between all classes seen in previous tasks without recourse to a task-ID. In this paper, we provide a complete survey of existing methods for incremental learning, and in particular we perform an extensive experimental evaluation on twelve class-incremental methods. We consider several new experimental scenarios, including a comparison of class-incremental methods on multiple large-scale datasets, investigation into small and large domain shifts, and comparison on various network architectures. |
|
|
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; 600.120 |
Approved |
no |
|
|
Call Number |
Admin @ si @ MLT2022 |
Serial |
3538 |
|
Permanent link to this record |
|
|
|
|
Author |
Lu Yu; Xialei Liu; Joost Van de Weijer |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
|
|
Title |
Self-Training for Class-Incremental Semantic Segmentation |
Type |
Journal Article |
|
Year |
2022 |
Publication |
IEEE Transactions on Neural Networks and Learning Systems |
Abbreviated Journal |
TNNLS |
|
|
Volume ![sorted by Volume (numeric) field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
|
Issue |
|
Pages |
|
|
|
Keywords |
Class-incremental learning; Self-training; Semantic segmentation. |
|
|
Abstract |
In class-incremental semantic segmentation, we have no access to the labeled data of previous tasks. Therefore, when incrementally learning new classes, deep neural networks suffer from catastrophic forgetting of previously learned knowledge. To address this problem, we propose to apply a self-training approach that leverages unlabeled data, which is used for rehearsal of previous knowledge. Specifically, we first learn a temporary model for the current task, and then, pseudo labels for the unlabeled data are computed by fusing information from the old model of the previous task and the current temporary model. In addition, conflict reduction is proposed to resolve the conflicts of pseudo labels generated from both the old and temporary models. We show that maximizing self-entropy can further improve results by smoothing the overconfident predictions. Interestingly, in the experiments, we show that the auxiliary data can be different from the training data and that even general-purpose, but diverse auxiliary data can lead to large performance gains. The experiments demonstrate the state-of-the-art results: obtaining a relative gain of up to 114% on Pascal-VOC 2012 and 8.5% on the more challenging ADE20K compared to previous state-of-the-art methods. |
|
|
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; 600.147; 611.008; |
Approved |
no |
|
|
Call Number |
Admin @ si @ YLW2022 |
Serial |
3745 |
|
Permanent link to this record |