公开(公告)号：US20230385644A1

公开(公告)日：2023-11-30

申请号：US17853682

申请日：2022-06-29

Applicant: NavInfo Europe B.V.

Inventor： Arnav Varma ,  Elahe Arani ,  Bahram Zonooz

IPC: G06N3/08 ,  G06N3/04

CPC classification number: G06N3/082 ,  G06N3/0481

Abstract: A computer-implemented method for general continual learning combines rehearsal-based methods with dynamic modularity and compositionality. Concretely, the method aims at achieving three objectives: dynamic, sparse, and compositional response to inputs; competent application performance; and—reducing catastrophic forgetting. The proposed method can work without knowledge of task-identities at test-time, it does not employ task-boundaries and it has bounded memory even when training on longer sequences.

公开(公告)号：US20240119304A1

公开(公告)日：2024-04-11

申请号：US18180719

申请日：2023-03-08

Applicant: NavInfo Europe B.V.

Inventor： Arnav Varma ,  Elahe Arani ,  Bahram Zonooz

IPC: G06N3/096 ,  G06N3/045

CPC classification number: G06N3/096 ,  G06N3/045

Abstract: A computer-implemented method including the step of formulating a continual learning algorithm with both element similarity as well as relational similarity between the stable and plastic model in a dual-memory setup with rehearsal. While the method includes the step of using only two memories to simplify the analysis of impact of relational similarity, the method can be trivially extended to more than two memories. Specifically, the plastic model learns on the data stream as well as on memory samples, while the stable model maintains an exponentially moving average of the plastic model, resulting in a more generalizable model. Simultaneously, to mitigate forgetting and to enable forward transfer, the stable model distills instance-wise and relational knowledge to the plastic model on memory samples. Instance-wise knowledge distillation maintains element similarities, while relational similarity loss maintains relational similarities. The memory samples are maintained in a small constant-sized memory buffer which is updated using reservoir sampling. The method of the current invention was tested under multiple evaluation protocols, showing the efficacy of relational similarity for continual learning with dual-memory setup and rehearsal.

公开(公告)号：US20230245463A1

公开(公告)日：2023-08-03

申请号：US17579367

申请日：2022-01-19

Applicant: NavInfo Europe B.V.

Inventor： Arnav Varma ,  Hemang Chawla ,  Bahram Zonooz ,  Elahe Arani

IPC: G06V20/58 ,  G06T7/55 ,  G06T7/80 ,  G06N3/08

CPC classification number: G06V20/58 ,  G06N3/088 ,  G06T7/55 ,  G06T7/80

Abstract: A computer-implemented method of self-supervised learning in neural network for scene understanding in autonomously moving vehicles wherein the method to estimate the ego-motion and the intrinsics (focal lengths and principal point) robustly in a unified manner from a pair of input overlapping images captured from a monocular camera, within a self-supervised monocular depth and ego-motion estimation problem by including multi-head self-attention modules within a transformer architecture.

公开(公告)号：US12061094B2

公开(公告)日：2024-08-13

申请号：US17674596

申请日：2022-02-17

Applicant: NavInfo Europe B.V.

Inventor： Haris Iqbal ,  Shruthi Gowda ,  Ahmed Badar ,  Terence Brouns ,  Arnav Varma ,  Elahe Arani ,  Bahram Zonooz

IPC: G01C21/32 ,  G01C21/00 ,  G06N3/08 ,  G06V20/56 ,  G06V20/58

CPC classification number: G01C21/3815 ,  G01C21/32 ,  G06N3/08 ,  G06V20/582 ,  G06V20/588

Abstract: An AI based change detection system for executing a method to detect changes in geo-tagged videos to update HD maps, the method employing a neural network of modular components including a keyframe extraction module for processing two or more videos relating to separate traversals of an area of interest to which the HD map which is to be updated relates, a deep neural network module processing output of the keyframe extraction module, a change detection module processing output of the deep neural network module, and an auxiliary computations module which is designed to aid the change detection module.

公开(公告)号：US11948272B2

公开(公告)日：2024-04-02

申请号：US17402349

申请日：2021-08-13

Applicant: NavInfo Europe B.V.

Inventor： Hemang Chawla ,  Arnav Varma ,  Elahe Arani ,  Bahram Zonooz

IPC: G06T3/40 ,  G06F18/214 ,  G06N3/045 ,  G06N3/08 ,  G06T3/4038 ,  G06T3/4046 ,  G06T7/50 ,  G06V20/40

CPC classification number: G06T3/4046 ,  G06F18/2148 ,  G06N3/045 ,  G06N3/08 ,  G06T3/4038 ,  G06T7/50 ,  G06V20/41

Abstract: A computer-implemented method to improve scale consistency and/or scale awareness in a model of self-supervised depth and ego-motion prediction neural networks processing a video stream of monocular images, wherein complementary GPS coordinates synchronized with the images are used to calculate a GPS to scale loss to enforce the scale-consistency and/or -awareness on the monocular self-supervised ego-motion and depth estimation. A relative weight assigned to the GPS to scale loss exponentially increases as training progresses. The depth and ego-motion prediction neural networks are trained using an appearance-based photometric loss between real and synthesized target images, as well as a smoothness loss on the depth predictions.

公开(公告)号：US20230258471A1

公开(公告)日：2023-08-17

申请号：US17674596

申请日：2022-02-17

Applicant: NavInfo Europe B.V.

Inventor： Haris Iqbal ,  Shruthi Gowda ,  Ahmed Badar ,  Terence Brouns ,  Arnav Varma ,  Elahe Arani ,  Bahram Zonooz

IPC: G01C21/00 ,  G06N3/08 ,  G01C21/32 ,  G06V20/56 ,  G06V20/58

CPC classification number: G01C21/3815 ,  G06N3/08 ,  G01C21/32 ,  G06V20/588 ,  G06V20/582

Abstract: An AI based change detection system for executing a method to detect changes in geo-tagged videos to update HD maps, the method employing a neural network of modular components including a keyframe extraction module for processing two or more videos relating to separate traversals of an area of interest to which the HD map which is to be updated relates, a deep neural network module processing output of the keyframe extraction module, a change detection module processing output of the deep neural network module, and an auxiliary computations module which is designed to aid the change detection module.

公开(公告)号：US20220156882A1

公开(公告)日：2022-05-19

申请号：US17402349

申请日：2021-08-13

Applicant: NavInfo Europe B.V.

Inventor： Hemang Chawla ,  Arnav Varma ,  Elahe Arani ,  Bahram Zonooz

IPC: G06T3/40 ,  G06T7/50 ,  G06K9/62 ,  G06K9/00 ,  G06N3/08 ,  G06N3/04

Abstract: A computer-implemented method to improve scale consistency and/or scale awareness in a model of self-supervised depth and ego-motion prediction neural networks processing a video stream of monocular images, wherein complementary GPS coordinates synchronized with the images are used to calculate a GPS to scale loss to enforce the scale-consistency and/or -awareness on the monocular self-supervised ego-motion and depth estimation. A relative weight assigned to the GPS to scale loss exponentially increases as training progresses. The depth and ego-motion prediction neural networks are trained using an appearance-based photometric loss between real and synthesized target images, as well as a smoothness loss on the depth predictions.