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Masked Siamese ConvNets [article]

Li Jing, Jiachen Zhu, Yann LeCun
2022 arXiv   pre-print
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

Hiba Alqasir, Damien Muselet, Christophe Ducottet
2020 Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications  
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]

Amirreza Shaban, Shray Bansal, Zhen Liu, Irfan Essa, Byron Boots
2017 arXiv   pre-print
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

Weide Liu, Chi Zhang, Guosheng Lin, Fayao Liu
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
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]

Weide Liu, Chi Zhang, Guosheng Lin, Fayao Liu
2020 arXiv   pre-print
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]

Jinghao Zhou, Peng Wang, Haoyang Sun
2019 arXiv   pre-print
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]

Jinhai Yang, Hua Yang
2021 arXiv   pre-print
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

Yude Wang, Jie Zhang, Meina Kan, Shiguang Shan, Xilin Chen
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
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

Jinghao Zhou, Peng Wang, Haoyang Sun
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
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]

Yude Wang, Jie Zhang, Meina Kan, Shiguang Shan, Xilin Chen
2020 arXiv   pre-print
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

Dawood Al Chanti, Vanessa Gonzalez Duque, Marion Crouzier, Antoine Nordez, Lilian Lacourpaille, Diana Mateus
2021 IEEE Transactions on Medical Imaging  
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]

Athanasios Vlontzos, Krystian Mikolajczyk
2018 arXiv   pre-print
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]

Wei Shen, Zelin Peng, Xuehui Wang, Huayu Wang, Jiazhong Cen, Dongsheng Jiang, Lingxi Xie, Xiaokang Yang, Qi Tian
2022 arXiv   pre-print
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]

Zhirong Wu, Zihang Lai, Xiao Sun, Stephen Lin
2022 arXiv   pre-print
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

Kaiheng Dai, Yuehuan Wang, Qiong Song
2019 Sensors  
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
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