604:
83:. For learning by triplet loss a baseline vector (anchor image) is compared against a positive vector (truthy image) and a negative vector (falsy image). The negative vector will force learning in the network, while the positive vector will act like a regularizer. For learning by contrastive loss there must be a weight decay to regularize the weights, or some similar operation like a normalization.
342:
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search image. By measuring the similarity between exemplar and each part of the search image, a map of similarity score can be given by the twin network. Furthermore, using a Fully
Convolutional Network, the process of computing each sector's similarity score can be replaced with only one cross correlation layer.
846:
1762:
Twin networks have been used in object tracking because of its unique two tandem inputs and similarity measurement. In object tracking, one input of the twin network is user pre-selected exemplar image, the other input is a larger search image, which twin network's job is to locate exemplar inside of
1766:
After being first introduced in 2016, Twin fully convolutional network has been used in many High-performance Real-time Object
Tracking Neural Networks. Like CFnet, StructSiam, SiamFC-tri, DSiam, SA-Siam, SiamRPN, DaSiamRPN, Cascaded SiamRPN, SiamMask, SiamRPN++, Deeper and Wider SiamRPN.
599:{\displaystyle {\begin{aligned}\delta (x^{(i)},x^{(j)})={\begin{cases}\min \ \|\operatorname {f} \left(x^{(i)}\right)-\operatorname {f} \left(x^{(j)}\right)\|\,,i=j\\\max \ \|\operatorname {f} \left(x^{(i)}\right)-\operatorname {f} \left(x^{(j)}\right)\|\,,i\neq j\end{cases}}\end{aligned}}}
59:, where known images of people are precomputed and compared to an image from a turnstile or similar. It is not obvious at first, but there are two slightly different problems. One is recognizing a person among a large number of other persons, that is the facial recognition problem.
1404:
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that uses the same weights while working in tandem on two different input vectors to compute comparable output vectors. Often one of the output vectors is precomputed, thus forming a baseline against which the other output vector is compared. This is similar to comparing
1207:
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It is possible to build an architecture that is functionally similar to a twin network but implements a slightly different function. This is typically used for comparing similar instances in different type sets.
1631:{\displaystyle {\begin{aligned}{\text{if}}\,i=j\,{\text{then}}&\,\operatorname {\delta } \left\,{\text{is small}}\\{\text{otherwise}}&\,\operatorname {\delta } \left\,{\text{is large}}\end{aligned}}}
1089:{\displaystyle {\begin{aligned}{\text{if}}\,i=j\,{\text{then}}&\,\operatorname {\delta } \left\,{\text{is small}}\\{\text{otherwise}}&\,\operatorname {\delta } \left\,{\text{is large}}\end{aligned}}}
323:
67:, that is to verify whether the photo in a pass is the same as the person claiming he or she is the same person. The twin network might be the same, but the implementation can be quite different.
1409:
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1372:{\displaystyle \operatorname {\delta } (\mathbf {x} ^{(i)},\mathbf {x} ^{(j)})\approx (\mathbf {x} ^{(i)}-\mathbf {x} ^{(j)})^{T}\mathbf {M} (\mathbf {x} ^{(i)}-\mathbf {x} ^{(j)})}
186:
129:
841:{\displaystyle \operatorname {\delta } (\mathbf {x} ^{(i)},\mathbf {x} ^{(j)})\approx (\mathbf {x} ^{(i)}-\mathbf {x} ^{(j)})^{T}(\mathbf {x} ^{(i)}-\mathbf {x} ^{(j)})}
328:
In particular, the triplet loss algorithm is often defined with squared
Euclidean (which unlike Euclidean, does not have triangle inequality) distance at its core.
1663:
1121:
631:
2252:
Li, Bo; Wu, Wei; Wang, Qiang; Zhang, Fangyi; Xing, Junliang; Yan, Junjie (2018). "SiamRPN++: Evolution of
Siamese Visual Tracking with Very Deep Networks".
856:
A more general case is where the output vector from the twin network is passed through additional network layers implementing non-linear distance metrics.
2231:
Wang, Qiang; Zhang, Li; Bertinetto, Luca; Hu, Weiming; Torr, Philip H. S. (2018). "Fast Online Object
Tracking and Segmentation: A Unifying Approach".
2104:
336:
The common learning goal is to minimize a distance metric for similar objects and maximize for distinct ones. This gives a loss function like
2299:
2000:
1882:
1819:
2121:
2189:
Zhu, Zheng; Wang, Qiang; Li, Bo; Wu, Wei; Yan, Junjie; Hu, Weiming (2018). "Distractor-aware
Siamese Networks for Visual Object Tracking".
1670:
2138:
63:
is an example of such a system. In its most extreme form this is recognizing a single person at a train station or airport. The other is
1939:
Chopra, S.; Hadsell, R.; LeCun, Y. (June 2005). "Learning a
Similarity Metric Discriminatively, with Application to Face Verification".
1914:
2172:
1983:
Taigman, Y.; Yang, M.; Ranzato, M.; Wolf, L. (June 2014). "DeepFace: Closing the Gap to Human-Level
Performance in Face Verification".
1956:
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in camera images, and matching queries with indexed documents. The perhaps most well-known application of twin networks are
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This form also allows the twin network to be more of a half-twin, implementing a slightly different functions
136:
48:
2210:
Fan, Heng; Ling, Haibin (2018). "Siamese
Cascaded Region Proposal Networks for Real-Time Visual Tracking".
2273:
Zhang, Zhipeng; Peng, Houwen (2019). "Deeper and Wider
Siamese Networks for Real-Time Visual Tracking".
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1865:, Methods in Molecular Biology, vol. 2190 (3rd ed.), New York City, New York, USA:
1802:, Methods in Molecular Biology, vol. 2190 (3rd ed.), New York City, New York, USA:
1988:
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2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)
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Bromley, Jane; Guyon, Isabelle; LeCun, Yann; Säckinger, Eduard; Shah, Roopak (1994).
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Uses of similarity measures where a twin network might be used are such things as
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2091:"End-to-end representation learning for Correlation Filter based tracking"
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function implemented by the network joining outputs from the twin network
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function implemented by the network joining outputs from the twin network
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675:, in case of which the loss function can be rewritten in matrix form as
86:
A distance metric for a loss function may have the following properties
2173:"High Performance Visual Tracking with Siamese Region Proposal Network"
1710:{\displaystyle \operatorname {f} (\cdot ),\operatorname {g} (\cdot )}
2026:"Similarity Learning with (or without) Convolutional Neural Network"
1915:"Signature verification using a "Siamese" time delay neural network"
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but can be described more technically as a distance function for
1985:
2014 IEEE Conference on Computer Vision and Pattern Recognition
2139:"Learning Dynamic Siamese Network for Visual Object Tracking"
588:
2105:"Structured Siamese Network for Real-Time Visual Tracking"
2057:
Proceedings of the National Institute of Sciences of India
318:{\displaystyle \delta (x,z)\leq \delta (x,y)+\delta (y,z)}
2156:"A Twofold Siamese Network for Real-Time Object Tracking"
2073:
Fully-Convolutional Siamese Networks for Object Tracking
1197:
On a matrix form the previous is often approximated as a
2122:"Triplet Loss in Siamese Network for Object Tracking"
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1922:Advances in Neural Information Processing Systems
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1186:{\displaystyle \operatorname {\delta } (\cdot )}
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332:Predefined metrics, Euclidean distance metric
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2050:"On the generalized distance in statistics"
1382:This can be further subdivided in at least
75:Learning in twin networks can be done with
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1150:{\displaystyle \operatorname {f} (\cdot )}
852:Learned metrics, nonlinear distance metric
660:{\displaystyle \operatorname {f} (\cdot )}
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239:{\displaystyle \delta (x,y)=\delta (y,x)}
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1943:. Vol. 1. pp. 539–546 vol. 1.
1157:function implemented by the twin network
671:The most common distance metric used is
667:function implemented by the twin network
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1859:"Siamese neural networks: an overview"
1796:"Siamese neural networks: an overview"
181:{\displaystyle \delta (x,y)=0\iff x=y}
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1394:Learned metrics, half-twin networks
2024:Chatterjee, Moitreya; Luo, Yunan.
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124:{\displaystyle \delta (x,y)\geq 0}
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1758:Twin networks for object tracking
1665:are indexes into a set of vectors
1123:are indexes into a set of vectors
633:are indexes into a set of vectors
1869:, Humana Press, pp. 73–94,
1806:, Humana Press, pp. 73–94,
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133:Identity of Non-discernibles:
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2300:Neural network architectures
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1812:10.1007/978-1-0716-0826-5_3
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1863:Artificial Neural Networks
1800:Artificial Neural Networks
38:locality-sensitive hashing
1777:Artificial neural network
29:artificial neural network
1857:Chicco, Davide (2020),
1794:Chicco, Davide (2020),
1201:for a linear space as
49:recognizing handwritten
2048:Chandra, M.P. (1936).
1987:. pp. 1701–1708.
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21:Siamese neural network
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23:(sometimes called a
1658:{\displaystyle i,j}
1388:Supervised learning
1116:{\displaystyle i,j}
626:{\displaystyle i,j}
25:twin neural network
1867:Springer Protocols
1804:Springer Protocols
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53:detection of faces
51:checks, automatic
2002:978-1-4799-5118-5
1884:978-1-0716-0826-5
1821:978-1-0716-0826-5
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57:face recognition
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1782:Triplet loss
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2259:1812.11703
2238:1812.05050
2217:1812.06148
2196:1808.06048
2079:1606.09549
2034:2018-12-07
1928:: 737–744.
1845:References
1901:221144012
1838:221144012
1738:⋅
1732:
1728:δ
1702:⋅
1696:
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1530:otherwise
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298:δ
277:δ
274:≤
256:δ
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198:δ
166:⟺
141:δ
116:≥
98:δ
2294:Category
2063:: 49–55.
1893:32804361
1830:32804361
1771:See also
1621:is large
1521:is small
1079:is large
979:is small
71:Learning
61:DeepFace
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2233:arXiv
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2191:arXiv
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2075:arXiv
2059:. 1.
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2007:S2CID
1963:S2CID
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1826:PMID
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406:min
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