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.
Filters
Masked Siamese ConvNets
[article]
2022
arXiv
pre-print
Preserved Fulltext
However, masked siamese networks require particular inductive bias and practically only work well with Vision Transformers. ...
The siamese network, which encourages embeddings to be invariant to distortions, is one of the most successful self-supervised visual representation learning approaches. ...
Acknowledgement We thank Mahmoud Assran and Nicolas Ballas for kindly sharing insights from the Masked Siamese Networks paper. We thank Nicolas Carion for useful discussions on object detection. ...
arXiv:2206.07700v1
fatcat:evjcp7xoovhktnnnnsgab5ztbu
Mask-guided Image Classification with Siamese Networks
2020
Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
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.
This mask as well as the color image are provided as inputs to a siamese network. ...
We show that the use of a siamese network allows to learn a single model that performs very well on 20 different skilifts. ...
A second advantage of using a specific binary mask for each chairlift is that the siamese network is not trying to learn general features that should work on all the chairlifts, but instead it learns specific ...
doi:10.5220/0009180005360543
dblp:conf/visapp/AlqasirMD20
fatcat:hgtwvfsfindnhixcumduwdj5vq
One-Shot Learning for Semantic Segmentation
[article]
2017
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.
Specifically, we train a network that, given a small set of annotated images, produces parameters for a Fully Convolutional Network (FCN). ...
Low-shot learning methods for image classification support learning from sparse data. We extend these techniques to support dense semantic image segmentation. ...
Figure S2 : S2 Siamese network architeture for dense matching. ...
arXiv:1709.03410v1
fatcat:3iwqxhoiaja37eydqifz5ppuse
CRNet: Cross-Reference Networks for Few-Shot Segmentation
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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.
For the k-shot learning, we propose to finetune parts of networks to take advantage of multiple labeled support images. ...
In this paper, we propose a cross-reference network (CRNet) for few-shot segmentation. ...
Finetuning for K-Shot Learning In the case of k-shot learning, we propose to finetune our network to take advantage of multiple labeled support images. ...
doi:10.1109/cvpr42600.2020.00422
dblp:conf/cvpr/LiuZLL20
fatcat:6w2q5srejvf5nkqrz2k3z36olm
CRNet: Cross-Reference Networks for Few-Shot Segmentation
[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.
For the k-shot learning, we propose to finetune parts of networks to take advantage of multiple labeled support images. ...
In this paper, we propose a cross-reference network (CRNet) for few-shot segmentation. ...
Finetuning for K-Shot Learning In the case of k-shot learning, we propose to finetune our network to take advantage of multiple labeled support images. ...
arXiv:2003.10658v1
fatcat:vuyhq3h57rakteroednjwpd2jy
Discriminative and Robust Online Learning for Siamese 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
Therefore, we propose an online module with an attention mechanism for offline siamese networks to extract target-specific features under L2 error. ...
We further propose a filter update strategy adaptive to treacherous background noises for discriminative learning, and a template update strategy to handle large target deformations for robust learning ...
Comparatively speaking, online learning for siamese-network-based trackers has had less attention. ...
arXiv:1909.02959v2
fatcat:pmc5vg3odnghnjotg4wzvfjcmy
MPASNET: Motion Prior-Aware Siamese Network for Unsupervised Deep Crowd Segmentation in Video Scenes
[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
In this paper, we propose the Motion Prior-Aware Siamese Network (MPASNET) for unsupervised crowd semantic segmentation. ...
Moreover, we equip MPASNET with siamese branches for augmentation-invariant regularization and siamese feature aggregation. ...
masks centered at collective motion particles. (2) We devise an end-to-end siamese network and the associated loss functions to learn from the self-produced pseudo-labels. (3) We evaluate our unsupervised ...
arXiv:2101.08609v2
fatcat:oa6dlouf55dwjjbkstnaa7ure4
Self-Supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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.
Therefore, we propose consistency regularization on predicted CAMs from various transformed images to provide self-supervision for network learning. ...
However, CAMs can hardly serve as the object mask due to the gap between full and weak supervisions. ...
Our SEAM is implemented by a siamese network structure with efficient regularization losses. ...
doi:10.1109/cvpr42600.2020.01229
dblp:conf/cvpr/WangZKSC20
fatcat:tgv5tkcxlng4ffxdftrsxeiwvy
Discriminative and Robust Online Learning for Siamese Visual Tracking
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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.
Therefore, we propose an online module with an attention mechanism for offline siamese networks to extract target-specific features under L2 error. ...
We further propose a filter update strategy adaptive to treacherous background noises for discriminative learning, and a template update strategy to handle large target deformations for robust learning ...
Comparatively speaking, online learning for siamese-network-based trackers has had less attention. ...
doi:10.1609/aaai.v34i07.7002
fatcat:vrx3qxk5qrcbtdpfvlbp5mmelm
Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation
[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.
Therefore, we propose consistency regularization on predicted CAMs from various transformed images to provide self-supervision for network learning. ...
However, CAMs can hardly serve as the object mask due to the gap between full and weak supervisions. ...
Our SEAM is implemented by a siamese network structure with efficient regularization losses. ...
arXiv:2004.04581v1
fatcat:ndlyv3fvgrfw7frtiio2sxcnla
IFSS-Net: Interactive Few-Shot Siamese Network for Faster Muscle Segmentation and Propagation in Volumetric Ultrasound
2021
IEEE Transactions on Medical Imaging
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.
A deep Siamese 3D Encoder-Decoder network that captures the evolution of the muscle appearance and shape for contiguous slices is deployed. ...
We present an accurate, fast and efficient method for segmentation and muscle mask propagation in 3D freehand ultrasound data, towards accurate volume quantification. ...
To this end, we propose a deep Siamese 3D Encoder-Decoder network that captures the evolution of the muscle appearance and shape for contiguous slices and uses it to propagate a reference mask annotated ...
doi:10.1109/tmi.2021.3058303
pmid:33560982
fatcat:b4ctszsxenhj7o2xrko4oiaby4
Deep Segmentation and Registration in X-Ray Angiography Video
[article]
2018
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 2019; you can also visit the original URL.
The file type is application/pdf.
Other Versions
We propose a real-time segmentation method for these tasks, based on U-Net network trained in a Siamese architecture from automatically generated annotations. ...
Acknowledgements The authors would like to thank Dr Tsagarakis, the chair of the scientific and ethical boards of the Evangelismos General Hospital, Athens, Greece for providing anonymized angiography ...
the network in addition to the large volume of data that is automatically labelled. ...
arXiv:1805.06406v2
fatcat:ritgddt2kjhvha2dzg53ugbd7e
A Survey on Label-efficient Deep Segmentation: Bridging the Gap between Weak Supervision and Dense Prediction
[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.
Finally, we share our opinions about the future research directions for label-efficient deep segmentation. ...
To alleviate this burden, the past years have witnessed an increasing attention in building label-efficient, deep-learning-based segmentation algorithms. ...
[26] designed a Siamese network for seed area refinement. ...
arXiv:2207.01223v1
fatcat:i7rgpxrfkrdbfm4effjdcjjr24
Extreme Masking for Learning Instance and Distributed Visual Representations
[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.
The paper makes three contributions: 1) Random masking is a strong and computationally efficient data augmentation for learning generalizable attention representations. 2) With multiple sampling per instance ...
, extreme masking greatly speeds up learning and hungers for more data. 3) Distributed representations can be learned from the instance supervision alone, unlike per-token supervisions in masked modeling ...
On the other hand, Siamese networks trained with contrastive objectives [8, 5] are strong for learning off-the-shelf representations [9] . ...
arXiv:2206.04667v1
fatcat:t6vghwt4pvas5fah22ktmqach4
Real-Time Object Tracking with Template Tracking and Foreground Detection Network
2019
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.
The proposed framework consists of a backbone network, which feeds into two parallel networks, TmpNet for template tracking and FgNet for foreground detection. ...
The backbone network is a pre-trained modified VGG network, in which a few parameters need to be fine-tuned for adapting to the tracked object. ...
Recently, more and more Siamese network trackers [1, 18, 19] utilize cross-correlation to compute the similarity between target template and the search feature maps, for its efficient implementation ...
doi:10.3390/s19183945
fatcat:buaelpdzpvgalfcb3fhgi7keme
« Previous
Showing results 1 — 15 out of 2,232 results