A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Filters
Learning Soft Mask Based Feature Fusion with Channel and Spatial Attention for Robust Visual Object Tracking
2020
Sensors
Preserved Fulltext
The channel attention is used to identify more discriminative channels for better target representation. ...
The proposed technique is further strengthened by integrating channel and spatial attention mechanisms. ...
Proposed channel attention learns which deep channels should be highlighted for better target feature discrimination. ...
doi:10.3390/s20144021
pmid:32698339
pmcid:PMC7412361
fatcat:ve4vb6tfozb2pb5u4jl7lbdlgy
Efficient Visual Tracking with Stacked Channel-Spatial Attention Learning
2020
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
INDEX TERMS Deep learning, Siamese architecture, stacked channel-spatial attention, visual object tracking. ...
We integrate the proposed channel and spatial attention modules to enhance tracking performance with end-to-end learning. ...
For the spatial-first attention (SFAtt) case, we first computed spatial attention and then stacked channel attention on it. ...
doi:10.1109/access.2020.2997917
fatcat:hui5geq7cncizcg76hki7mbvh4
Attention-based Siamese Region Proposals Network for Visual Tracking
2020
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
The attention mechanism is essentially realized by convolutional neural network. The feature optimization mainly includes spatial attention selection and channel attention selection. ...
Specifically, the spatial attention convolutional neural network is used to learn the planar weights to enhance the foreground and suppress the interference background. ...
It is a successful application for CNN in visual tracking. ...
doi:10.1109/access.2020.2991238
fatcat:qta4v5vfafdx5hmht2m25giroe
Siamese Cascaded Region Proposal Networks with Channel-Interconnection-Spatial Attention for Visual Tracking
2020
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
.: Siamese Cascaded Region Proposal Networks with Channel-Interconnection-Spatial Attention for Visual Tracking 2 VOLUME XX, 2017(a) (b) (c) (d) (e) (f) (g) (h) (i) (j) (k)FIGURE 7. ...
Cui et al.: Siamese Cascaded Region Proposal Networks with Channel-Interconnection-Spatial Attention for Visual Tracking 2
VOLUME XX, 2017
(d)
(e)
(f)
(g)
(h)
(i)
(j)
(k)
(l)
TABLE II ...
doi:10.1109/access.2020.3017179
fatcat:63t4ncbrgjczpemn6hhigipamu
HROM: Learning High-Resolution Representation and Object-Aware Masks for Visual Object Tracking
2020
Sensors
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Moreover, we exploit attention mechanisms to learn object-aware masks for adaptive feature refinement, and use deformable convolution to handle complex geometric transformations. ...
The resulting representation is semantically richer and spatially more precise by a simple yet effective multi-scale feature fusion strategy. ...
Channel and spatial attention mechanisms can learn 'what' and 'where' to emphasize or restrain in the channel and spatial axes, respectively. ...
doi:10.3390/s20174807
pmid:32858872
fatcat:m2gcbfyklzfylbpehtqnm3m5bu
Learning Attentions: Residual Attentional Siamese Network for High Performance Online Visual Tracking
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
The proposed deep architecture is trained from end to end and takes full advantage of the rich spatial temporal information to achieve robust visual tracking. ...
However, it is still difficult to adapt an offline trained model to a target tracked online. This work presents a Residual Attentional Siamese Network (RASNet) for high performance object tracking. ...
for visual tracking. ...
doi:10.1109/cvpr.2018.00510
dblp:conf/cvpr/WangTXGHM18
fatcat:5f2p5jhp55dzxigay5tqhidvza
Deformable Siamese Attention Networks for Visual Object Tracking
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Other Versions
The self attention learns strong context information via spatial attention, and selectively emphasizes interdependent channel-wise features with channel attention. ...
Siamese-based trackers have achieved excellent performance on visual object tracking. ...
Conclusion We have presented new Deformable Siamese Attention Networks for visual object tracking. ...
arXiv:2004.06711v2
fatcat:bhvjgmnsvvdt5fobmkv7rqjgxu
Learning Reinforced Attentional Representation for End-to-End Visual Tracking
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
In this paper, we propose an end-to-end network model to learn reinforced attentional representation for accurate target object discrimination and localization. ...
Despite the fact that tremendous advances have been made by numerous recent tracking approaches in the last decade, how to achieve high-performance visual tracking is still an open problem. ...
Some works explore attention mechanisms to highlight useful information in visual tracking. CSRDCF [34] constructs a unique spatial reliability map to constraint filters learning. ...
arXiv:1908.10009v2
fatcat:yr5ikhrzdjdz7bt2toxqzfrkeq
Siamese Attentional Keypoint Network for High Performance Visual Tracking
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Firstly, a new Siamese lightweight hourglass network is specifically designed for visual tracking. ...
Secondly, a novel cross-attentional module is utilized to leverage both channel-wise and spatial intermediate attentional information, which enhance both discriminative and localization capabilities of ...
Maybank, Learning attentions: residual attentional siamese network
for high performance online visual tracking, in: IEEE Conference on Computer Vision and Pattern Recognition
(CVPR), 2018, pp ...
arXiv:1904.10128v1
fatcat:ovn4s5cbdfbkllioy7fxnqxp5q
SiamCAM: A Real-Time Siamese Network for Object Tracking with Compensating Attention Mechanism
2022
Applied Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
We propose a real-time Siamese network object tracking algorithm combined with a compensating attention mechanism to solve this problem. ...
The Siamese-based object tracking algorithm regards tracking as a similarity matching problem. ...
SCSAtt [39] employed channel attention and spatial attention mechanisms together to improve tracking performance with end-to-end learning. ...
doi:10.3390/app12083931
fatcat:6cl4lyek55gdfprygsoll7ymuy
AdaptiveWeighted Attention Network with Camera Spectral Sensitivity Prior for Spectral Reconstruction from RGB Images
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Concretely, we investigate an adaptive weighted channel attention (AWCA) module to reallocate channel-wise feature responses via integrating correlations between channels. ...
To conquer these issues, we propose a novel adaptive weighted attention network (AWAN) for SR, whose backbone is stacked with multiple dual residual attention blocks (DRAB) decorating with long and short ...
Adaptive Weighted Channel Attention (AWCA) Extracting interdependencies among intermediate features is indispensable for strengthening discriminant learning power of CNNs. ...
arXiv:2005.09305v1
fatcat:i75u3xzivbfqdgxlaa7fics7f4
MFGNet: Dynamic Modality-Aware Filter Generation for RGB-T Tracking
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Other Versions
The spatial and temporal recurrent neural network is used to capture the direction-aware context for accurate global attention prediction. ...
various input images in practical tracking. ...
It is worth mentioning that our model can also be utilized together with attention schemes, like spatial and channel attention, for robust feature learning. ...
arXiv:2107.10433v2
fatcat:3mxe5iidvrgbvbxdna4pwwlv74
SIAMESE NETWORK COMBINED WITH ATTENTION MECHANISM FOR OBJECT TRACKING
2020
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
channel and spatial weighting on the feature maps obtained by convolution of the input image. ...
At the same time, the backbone network model of CNN in the algorithm is adjusted, then the siamese network combined with attention mechanism for object tracking is proposed. ...
The attention mechanism module includes channel attention module and spatial attention module. ...
doi:10.5194/isprs-archives-xliii-b2-2020-1315-2020
fatcat:sm2i34u5fbgqjkgn7rnwejd6w4
An Enhanced Visual Attention Siamese Network That Updates Template Features Online
2021
Security and Communication Networks
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
The method is based on a deep convolutional neural network, which integrates channel attention and spatial self-attention to improve the discriminative ability of the tracker for positive and negative ...
In the current study, we introduce an enhanced visual attention Siamese network (ESA-Siam). ...
tracking algorithm based on the enhanced visual attention mechanism (including channel attention, spatial self-attention, and template search collaborative attention). ...
doi:10.1155/2021/9719745
fatcat:5tzzjuqapbbbdd2sgpnfaanwqq
Faster MDNet for Visual Object Tracking
2022
Applied Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Simultaneously, we implement an adaptive, spatial pyramid pooling layer for reducing model complexity and accelerating the tracking speed. ...
To improve the tracking accuracy, a channel attention module is introduced to our method. We also design domain adaptation components to obtain more generic features. ...
CBAM [33] combined channel attention and spatial attention, which improved computational efficiency by decoupling attention maps between channel dimensions and spatial dimensions. ...
doi:10.3390/app12052336
fatcat:vj767tq2hzeydbbxvkzzknbowq
« Previous
Showing results 1 — 15 out of 48,532 results