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Patent Agency Ranking
公开(公告)号：US20230123493A1
公开(公告)日：2023-04-20
申请号：US17502729
申请日：2021-10-15
Applicant: NavInfo Europe B.V.
Abstract: A computer implemented network for executing a self-supervised scene change detection method, wherein at least one image pair with images captured at different instances of time is processed to detect structural changes caused by an appearance or disappearance of an object in the image pair, and wherein a self-supervised pretraining method is employed that utilizes an unlabelled image pair or pairs to learn representations for scene change detection, and wherein the aligned image pair is subjected to a differencing based self-supervised pre-training method to maximize a correlation between changed regions in the images which provide the structural changes that occur in the image pairs.
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公开(公告)号：US12147502B2
公开(公告)日：2024-11-19
申请号：US17502729
申请日：2021-10-15
Applicant: NavInfo Europe B.V.
IPC: G06F18/214 , G06F18/213 , G06F18/22 , G06N20/00 , G06V10/70 , G06V10/94 , G06V20/00
Abstract: A computer implemented network for executing a self-supervised scene change detection method, wherein at least one image pair with images captured at different instances of time is processed to detect structural changes caused by an appearance or disappearance of an object in the image pair, and wherein a self-supervised pretraining method is employed that utilizes an unlabelled image pair or pairs to learn representations for scene change detection, and wherein the aligned image pair is subjected to a differencing based self-supervised pre-training method to maximize a correlation between changed regions in the images which provide the structural changes that occur in the image pairs.
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3.发明公开公开(公告)号：US20240127066A1
公开(公告)日：2024-04-18
申请号：US18161312
申请日：2023-01-30
Applicant: NavInfo Europe B.V.
Abstract: A computer-implemented method for improving generalization in training deep neural networks in online settings. The method includes a general learning paradigm for sequential data that is referred to as Learn, Unlearn, RElearn (LURE), a dynamic re-initialization method to address the above-mentioned larger problem of generalization of parameterized networks on sequential data by selectively retaining the task-specific connections through the important criteria and re-randomizing the less important parameters at each mega batch of training. The method of selectively forgetting retains previous information all the while improving generalization to unseen samples.
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4.发明授权公开(公告)号：US12062188B2
公开(公告)日：2024-08-13
申请号：US17691397
申请日：2022-03-10
Applicant: NavInfo Europe B.V.
CPC classification number: G06T7/20 , G06N3/088 , G06T2207/20081 , G06T2207/20084
Abstract: A computer implemented network for executing a self-supervised scene change detection method in which image pairs (T0, T1) from different time instances are subjected to random photometric transformations to obtain two pairs of augmented images (T0→T0′, T0″; T1→T1′, T1″), which augmented images are passed into an encoder (fθ) and a projection head (gϕ) to provide corresponding feature representations. Absolute feature differencing is applied over the outputs of the projection head (gϕ) to obtain difference representations (d1, d2) of changed features between the pair of images, and a self-supervised objective function (LSSL) is applied on the difference representations d1 and d2 to maximize a cross-correlation of the changed features, wherein d1 and d2 are defined as
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Furthermore, an invariant prediction and change consistency loss is applied in the D-SSCD Network to reduce the effects of differences in the lighting conditions or camera viewpoints by enhancing the image alignment between the temporal images in the decision and feature space.-
5.发明公开公开(公告)号：US20230289977A1
公开(公告)日：2023-09-14
申请号：US17691397
申请日：2022-03-10
Applicant: NavInfo Europe B.V.
CPC classification number: G06T7/20 , G06N3/088 , G06T2207/20081 , G06T2207/20084
Abstract: A computer implemented network for executing a self-supervised scene change detection method in which image pairs (T0, T1) from different time instances are subjected to random photometric transformations to obtain two pairs of augmented images (T0 → T 0′, T 0‴ ; T1 → T 1′, T1″), which augmented images are passed into an encoder (fθ) and a projection head (gϕ) to provide corresponding feature representations. Absolute feature differencing is applied over the outputs of the projection head (gϕ) to obtain difference representations (d1, d2) of changed features between the pair of images, and a self-supervised objective function (LSSL) is applied on the difference representations d1 and d2 to maximize a cross-correlation of the changed features, wherein d1 and d2 are defined as
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Furthermore, an invariant prediction and change consistency loss is applied in the D-SSCD Network to reduce the effects of differences in the lighting conditions or camera viewpoints by enhancing the image alignment between the temporal images in the decision and feature space.
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