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4ddf79b3-fcfc-4730-a50a-8a67c1cbeeb9 | We validate our approach, MODEST (Mobile Object Detection with Ephemerality and Self-Training) on the Lyft Level 5 Perception Dataset [1]} and the nuScenes Dataset [2]} with various types of detectors [3]}, [4]}, [5]}, [6]}. We demonstrate that MODEST yields remarkably accurate mobile object detectors, comparable to th... | [2] | [
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58439ca8-5b5b-4830-ab80-cc8b7bff123a | We validate our approach, MODEST (Mobile Object Detection with Ephemerality and Self-Training) on the Lyft Level 5 Perception Dataset [1]} and the nuScenes Dataset [2]} with various types of detectors [3]}, [4]}, [5]}, [6]}. We demonstrate that MODEST yields remarkably accurate mobile object detectors, comparable to th... | [3] | [
[
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7bdf97ba-6df9-4dd2-9558-46beedefdcfb | We validate our approach, MODEST (Mobile Object Detection with Ephemerality and Self-Training) on the Lyft Level 5 Perception Dataset [1]} and the nuScenes Dataset [2]} with various types of detectors [3]}, [4]}, [5]}, [6]}. We demonstrate that MODEST yields remarkably accurate mobile object detectors, comparable to th... | [5] | [
[
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15729042-80fe-44d2-99bf-5bf143654f06 | We validate our approach, MODEST (Mobile Object Detection with Ephemerality and Self-Training) on the Lyft Level 5 Perception Dataset [1]} and the nuScenes Dataset [2]} with various types of detectors [3]}, [4]}, [5]}, [6]}. We demonstrate that MODEST yields remarkably accurate mobile object detectors, comparable to th... | [6] | [
[
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deb1491b-509e-4a06-b024-3412e48093e2 | 3D object detection and existing datasets. Most existing 3D object detectors take 3D point clouds generated by LiDAR as input. They either consist of specialized neural architectures that can operate on point clouds directly [1]}, [2]}, [3]}, [4]}, [5]} or voxelize the point clouds to leverage 2D or 3D convolutional ne... | [1] | [
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290c4146-dd84-43dc-8b20-d18ebafc466c | 3D object detection and existing datasets. Most existing 3D object detectors take 3D point clouds generated by LiDAR as input. They either consist of specialized neural architectures that can operate on point clouds directly [1]}, [2]}, [3]}, [4]}, [5]} or voxelize the point clouds to leverage 2D or 3D convolutional ne... | [2] | [
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afa5aa97-7c96-4c38-9671-72867b97113a | 3D object detection and existing datasets. Most existing 3D object detectors take 3D point clouds generated by LiDAR as input. They either consist of specialized neural architectures that can operate on point clouds directly [1]}, [2]}, [3]}, [4]}, [5]} or voxelize the point clouds to leverage 2D or 3D convolutional ne... | [3] | [
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2dd7949d-2f09-4b0f-87b3-ddad673b4318 | 3D object detection and existing datasets. Most existing 3D object detectors take 3D point clouds generated by LiDAR as input. They either consist of specialized neural architectures that can operate on point clouds directly [1]}, [2]}, [3]}, [4]}, [5]} or voxelize the point clouds to leverage 2D or 3D convolutional ne... | [4] | [
[
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] | https://openalex.org/W2949708697 |
af598327-b522-474c-81f1-8abca08c205c | 3D object detection and existing datasets. Most existing 3D object detectors take 3D point clouds generated by LiDAR as input. They either consist of specialized neural architectures that can operate on point clouds directly [1]}, [2]}, [3]}, [4]}, [5]} or voxelize the point clouds to leverage 2D or 3D convolutional ne... | [6] | [
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] | https://openalex.org/W2963727135 |
43a2ce96-2d3d-424e-9fdd-1a64b1e4a2ac | 3D object detection and existing datasets. Most existing 3D object detectors take 3D point clouds generated by LiDAR as input. They either consist of specialized neural architectures that can operate on point clouds directly [1]}, [2]}, [3]}, [4]}, [5]} or voxelize the point clouds to leverage 2D or 3D convolutional ne... | [7] | [
[
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] | https://openalex.org/W3031752193 |
8d15e1e7-ae65-446b-9b4b-ad7a5b0b8d20 | 3D object detection and existing datasets. Most existing 3D object detectors take 3D point clouds generated by LiDAR as input. They either consist of specialized neural architectures that can operate on point clouds directly [1]}, [2]}, [3]}, [4]}, [5]} or voxelize the point clouds to leverage 2D or 3D convolutional ne... | [8] | [
[
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] | https://openalex.org/W2798965597 |
b05f478d-cb02-4ff9-9b72-9c20119c2bb4 | 3D object detection and existing datasets. Most existing 3D object detectors take 3D point clouds generated by LiDAR as input. They either consist of specialized neural architectures that can operate on point clouds directly [1]}, [2]}, [3]}, [4]}, [5]} or voxelize the point clouds to leverage 2D or 3D convolutional ne... | [9] | [
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] | https://openalex.org/W2897529137 |
be69b078-04db-4920-a697-3f68ba5a86ca | 3D object detection and existing datasets. Most existing 3D object detectors take 3D point clouds generated by LiDAR as input. They either consist of specialized neural architectures that can operate on point clouds directly [1]}, [2]}, [3]}, [4]}, [5]} or voxelize the point clouds to leverage 2D or 3D convolutional ne... | [10] | [
[
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] | https://openalex.org/W3034314779 |
7cf0666a-0826-44e0-8de5-91a7f0a0e979 | 3D object detection and existing datasets. Most existing 3D object detectors take 3D point clouds generated by LiDAR as input. They either consist of specialized neural architectures that can operate on point clouds directly [1]}, [2]}, [3]}, [4]}, [5]} or voxelize the point clouds to leverage 2D or 3D convolutional ne... | [11] | [
[
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] | https://openalex.org/W3108486966 |
c017a118-822a-4a63-bd9d-edfa22da1905 | 3D object detection and existing datasets. Most existing 3D object detectors take 3D point clouds generated by LiDAR as input. They either consist of specialized neural architectures that can operate on point clouds directly [1]}, [2]}, [3]}, [4]}, [5]} or voxelize the point clouds to leverage 2D or 3D convolutional ne... | [13] | [
[
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] | https://openalex.org/W2555618208 |
b68be34f-ec6b-46be-9903-ff17d45aea39 | 3D object detection and existing datasets. Most existing 3D object detectors take 3D point clouds generated by LiDAR as input. They either consist of specialized neural architectures that can operate on point clouds directly [1]}, [2]}, [3]}, [4]}, [5]} or voxelize the point clouds to leverage 2D or 3D convolutional ne... | [14] | [
[
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] | https://openalex.org/W2150066425 |
1597a5eb-3b90-42fe-bbb8-57468ae2aec0 | 3D object detection and existing datasets. Most existing 3D object detectors take 3D point clouds generated by LiDAR as input. They either consist of specialized neural architectures that can operate on point clouds directly [1]}, [2]}, [3]}, [4]}, [5]} or voxelize the point clouds to leverage 2D or 3D convolutional ne... | [15] | [
[
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637
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] | https://openalex.org/W2115579991 |
cde76ff3-1388-438a-916e-f8e36d00acbb | 3D object detection and existing datasets. Most existing 3D object detectors take 3D point clouds generated by LiDAR as input. They either consist of specialized neural architectures that can operate on point clouds directly [1]}, [2]}, [3]}, [4]}, [5]} or voxelize the point clouds to leverage 2D or 3D convolutional ne... | [17] | [
[
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651
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] | https://openalex.org/W3035574168 |
378886c3-6ecc-4622-abc9-8871bb18e481 | 3D object detection and existing datasets. Most existing 3D object detectors take 3D point clouds generated by LiDAR as input. They either consist of specialized neural architectures that can operate on point clouds directly [1]}, [2]}, [3]}, [4]}, [5]} or voxelize the point clouds to leverage 2D or 3D convolutional ne... | [18] | [
[
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658
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] | https://openalex.org/W2955189650 |
aa0a60cf-ecae-4d98-a70b-77a922e9d22c | 3D object detection and existing datasets. Most existing 3D object detectors take 3D point clouds generated by LiDAR as input. They either consist of specialized neural architectures that can operate on point clouds directly [1]}, [2]}, [3]}, [4]}, [5]} or voxelize the point clouds to leverage 2D or 3D convolutional ne... | [19] | [
[
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] | https://openalex.org/W3034975685 |
e5e70a45-16d6-4838-8af0-4369d30eac00 | Unsupervised Object Discovery in 2D/3D. Our work follows prior work on discovering objects both from 2D images as well as from 3D data.
A first step in object discovery is to identify candidate objects, or “proposals” from a single scene/image.
For 2D images, this is typically done by segmenting the image using appeara... | [3] | [
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] | https://openalex.org/W2979654309 |
6f935b40-ccfc-4b4a-82b7-c3ffbbd93fad | Unsupervised Object Discovery in 2D/3D. Our work follows prior work on discovering objects both from 2D images as well as from 3D data.
A first step in object discovery is to identify candidate objects, or “proposals” from a single scene/image.
For 2D images, this is typically done by segmenting the image using appeara... | [4] | [
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] | https://openalex.org/W1919709169 |
48938c3e-838c-4ff0-9732-d22435682279 | Unsupervised Object Discovery in 2D/3D. Our work follows prior work on discovering objects both from 2D images as well as from 3D data.
A first step in object discovery is to identify candidate objects, or “proposals” from a single scene/image.
For 2D images, this is typically done by segmenting the image using appeara... | [12] | [
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] | https://openalex.org/W2049776679 |
5e2cd74a-bfe3-43f7-8752-edaba6f5baae | Unsupervised Object Discovery in 2D/3D. Our work follows prior work on discovering objects both from 2D images as well as from 3D data.
A first step in object discovery is to identify candidate objects, or “proposals” from a single scene/image.
For 2D images, this is typically done by segmenting the image using appeara... | [24] | [
[
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]
] | https://openalex.org/W2963170338 |
90123595-9cfd-4181-bc3a-885955566240 | Unsupervised Object Discovery in 2D/3D. Our work follows prior work on discovering objects both from 2D images as well as from 3D data.
A first step in object discovery is to identify candidate objects, or “proposals” from a single scene/image.
For 2D images, this is typically done by segmenting the image using appeara... | [34] | [
[
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] | https://openalex.org/W3106670560 |
1805295a-986f-42da-835a-05f1dc515ac2 | Unsupervised Object Discovery in 2D/3D. Our work follows prior work on discovering objects both from 2D images as well as from 3D data.
A first step in object discovery is to identify candidate objects, or “proposals” from a single scene/image.
For 2D images, this is typically done by segmenting the image using appeara... | [39] | [
[
1782,
1786
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] | https://openalex.org/W1984034752 |
edbaf067-105d-4b58-b8b3-1e476b4590cf | Unsupervised Object Discovery in 2D/3D. Our work follows prior work on discovering objects both from 2D images as well as from 3D data.
A first step in object discovery is to identify candidate objects, or “proposals” from a single scene/image.
For 2D images, this is typically done by segmenting the image using appeara... | [41] | [
[
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1800
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] | https://openalex.org/W2964283970 |
97a869b7-eea0-4483-af9c-f4943fc47445 | Self-training, semi-supervised and self-supervised learning.
When training our detector, we use self-training, which has been shown to be highly effective for semi-supervised learning[1]}, [2]}, domain adaptation [3]}, [4]}, [5]}, [6]}, [7]}, [8]} and few-shot/transfer learning [9]}, [10]}, [11]}, [12]}. Interestingly,... | [2] | [
[
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] | https://openalex.org/W3035160371 |
406925b9-7399-474a-9e8d-c7206f2b0f2b | Self-training, semi-supervised and self-supervised learning.
When training our detector, we use self-training, which has been shown to be highly effective for semi-supervised learning[1]}, [2]}, domain adaptation [3]}, [4]}, [5]}, [6]}, [7]}, [8]} and few-shot/transfer learning [9]}, [10]}, [11]}, [12]}. Interestingly,... | [3] | [
[
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] | https://openalex.org/W2895281799 |
f5680c4a-8d2f-44f0-85da-be38801d80a2 | Self-training, semi-supervised and self-supervised learning.
When training our detector, we use self-training, which has been shown to be highly effective for semi-supervised learning[1]}, [2]}, domain adaptation [3]}, [4]}, [5]}, [6]}, [7]}, [8]} and few-shot/transfer learning [9]}, [10]}, [11]}, [12]}. Interestingly,... | [4] | [
[
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] | https://openalex.org/W2963240485 |
644401b9-8347-4976-a4be-dcf2227fb4e9 | Self-training, semi-supervised and self-supervised learning.
When training our detector, we use self-training, which has been shown to be highly effective for semi-supervised learning[1]}, [2]}, domain adaptation [3]}, [4]}, [5]}, [6]}, [7]}, [8]} and few-shot/transfer learning [9]}, [10]}, [11]}, [12]}. Interestingly,... | [5] | [
[
225,
228
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] | https://openalex.org/W2985406498 |
3459c54f-2461-48c6-b1db-7f8a040fcb01 | Self-training, semi-supervised and self-supervised learning.
When training our detector, we use self-training, which has been shown to be highly effective for semi-supervised learning[1]}, [2]}, domain adaptation [3]}, [4]}, [5]}, [6]}, [7]}, [8]} and few-shot/transfer learning [9]}, [10]}, [11]}, [12]}. Interestingly,... | [6] | [
[
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] | https://openalex.org/W2970092410 |
462932a8-1591-4245-93ff-b3ad459c827c | Self-training, semi-supervised and self-supervised learning.
When training our detector, we use self-training, which has been shown to be highly effective for semi-supervised learning[1]}, [2]}, domain adaptation [3]}, [4]}, [5]}, [6]}, [7]}, [8]} and few-shot/transfer learning [9]}, [10]}, [11]}, [12]}. Interestingly,... | [7] | [
[
237,
240
]
] | https://openalex.org/W3108566666 |
c2568917-4783-4a55-8b3e-3b87a40a90d2 | Self-training, semi-supervised and self-supervised learning.
When training our detector, we use self-training, which has been shown to be highly effective for semi-supervised learning[1]}, [2]}, domain adaptation [3]}, [4]}, [5]}, [6]}, [7]}, [8]} and few-shot/transfer learning [9]}, [10]}, [11]}, [12]}. Interestingly,... | [8] | [
[
243,
246
]
] | https://openalex.org/W3175269419 |
325c9b93-4b28-44fb-a349-27fddfc17e6f | Self-training, semi-supervised and self-supervised learning.
When training our detector, we use self-training, which has been shown to be highly effective for semi-supervised learning[1]}, [2]}, domain adaptation [3]}, [4]}, [5]}, [6]}, [7]}, [8]} and few-shot/transfer learning [9]}, [10]}, [11]}, [12]}. Interestingly,... | [9] | [
[
279,
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]
] | https://openalex.org/W3108975329 |
45224ba8-16cf-483d-9402-ac524cc1affd | Self-training, semi-supervised and self-supervised learning.
When training our detector, we use self-training, which has been shown to be highly effective for semi-supervised learning[1]}, [2]}, domain adaptation [3]}, [4]}, [5]}, [6]}, [7]}, [8]} and few-shot/transfer learning [9]}, [10]}, [11]}, [12]}. Interestingly,... | [10] | [
[
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289
]
] | https://openalex.org/W3128167848 |
18a55fb5-66f8-4a48-9454-336be5b97baf | Self-training, semi-supervised and self-supervised learning.
When training our detector, we use self-training, which has been shown to be highly effective for semi-supervised learning[1]}, [2]}, domain adaptation [3]}, [4]}, [5]}, [6]}, [7]}, [8]} and few-shot/transfer learning [9]}, [10]}, [11]}, [12]}. Interestingly,... | [12] | [
[
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] | https://openalex.org/W3204397973 |
2187df7f-b83a-453d-82e9-e7ecc28e2ed4 | Self-training, semi-supervised and self-supervised learning.
When training our detector, we use self-training, which has been shown to be highly effective for semi-supervised learning[1]}, [2]}, domain adaptation [3]}, [4]}, [5]}, [6]}, [7]}, [8]} and few-shot/transfer learning [9]}, [10]}, [11]}, [12]}. Interestingly,... | [13] | [
[
516,
520
]
] | https://openalex.org/W2963735582 |
aab9c985-e4c9-47b2-bc42-d971870f9650 | Self-training, semi-supervised and self-supervised learning.
When training our detector, we use self-training, which has been shown to be highly effective for semi-supervised learning[1]}, [2]}, domain adaptation [3]}, [4]}, [5]}, [6]}, [7]}, [8]} and few-shot/transfer learning [9]}, [10]}, [11]}, [12]}. Interestingly,... | [14] | [
[
523,
527
]
] | https://openalex.org/W3004146535 |
919489a8-2de9-410e-a23b-bf56f62351b0 | Self-training, semi-supervised and self-supervised learning.
When training our detector, we use self-training, which has been shown to be highly effective for semi-supervised learning[1]}, [2]}, domain adaptation [3]}, [4]}, [5]}, [6]}, [7]}, [8]} and few-shot/transfer learning [9]}, [10]}, [11]}, [12]}. Interestingly,... | [15] | [
[
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534
]
] | https://openalex.org/W2963096987 |
08b2a223-ae79-48b5-8d07-f483023db56d | Self-training, semi-supervised and self-supervised learning.
When training our detector, we use self-training, which has been shown to be highly effective for semi-supervised learning[1]}, [2]}, domain adaptation [3]}, [4]}, [5]}, [6]}, [7]}, [8]} and few-shot/transfer learning [9]}, [10]}, [11]}, [12]}. Interestingly,... | [16] | [
[
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541
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] | https://openalex.org/W2575671312 |
42d9a250-788a-4d70-a460-142456129ac2 | Self-training, semi-supervised and self-supervised learning.
When training our detector, we use self-training, which has been shown to be highly effective for semi-supervised learning[1]}, [2]}, domain adaptation [3]}, [4]}, [5]}, [6]}, [7]}, [8]} and few-shot/transfer learning [9]}, [10]}, [11]}, [12]}. Interestingly,... | [17] | [
[
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] | https://openalex.org/W2145494108 |
4890480e-9f78-4759-adcc-23994bc86779 | Self-training, semi-supervised and self-supervised learning.
When training our detector, we use self-training, which has been shown to be highly effective for semi-supervised learning[1]}, [2]}, domain adaptation [3]}, [4]}, [5]}, [6]}, [7]}, [8]} and few-shot/transfer learning [9]}, [10]}, [11]}, [12]}. Interestingly,... | [18] | [
[
632,
636
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] | https://openalex.org/W2978426779 |
75c1a013-a367-4f90-9f02-ed69743c8bc8 | Self-training, semi-supervised and self-supervised learning.
When training our detector, we use self-training, which has been shown to be highly effective for semi-supervised learning[1]}, [2]}, domain adaptation [3]}, [4]}, [5]}, [6]}, [7]}, [8]} and few-shot/transfer learning [9]}, [10]}, [11]}, [12]}. Interestingly,... | [19] | [
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] | https://openalex.org/W2989700832 |
6fc51ad7-ebf6-4dfc-880b-e90b52febbc9 | Self-training, semi-supervised and self-supervised learning.
When training our detector, we use self-training, which has been shown to be highly effective for semi-supervised learning[1]}, [2]}, domain adaptation [3]}, [4]}, [5]}, [6]}, [7]}, [8]} and few-shot/transfer learning [9]}, [10]}, [11]}, [12]}. Interestingly,... | [20] | [
[
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650
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] | https://openalex.org/W3001197829 |
02dc9dc1-6b79-4c4a-847c-2c5df7c8935b | Overview. We propose simple, high-level common-sense properties that can easily identify a few seed objects in the unlabeled data.
These discovered objects then serve as labels to train an off-the-shelf object detector.
Specifically, building upon the neural network's ability to learn consistent patterns from initial s... | [2] | [
[
381,
384
]
] | https://openalex.org/W3035160371 |
271449ee-de13-48fb-b9b5-b4e28235b0e9 | What properties define mobile objects or traffic participants?
Clearly, the most important characteristic is that they are mobile, i.e., they move around.
If such an object is spotted at a particular location (e.g., a car at an intersection), it is unlikely that the object will still be there when one visits the inters... | [1] | [
[
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418
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] | https://openalex.org/W2963170338 |
2788e5b9-e830-4e54-967a-21668742b9e4 | We assume that our unlabeled data include a set of locations \(L\) which are traversed multiple times in separate driving sessions (or traversals).
For every traversal \(t\) through location \(c \in L\) , we aggregate point clouds captured within a range of \([-H_s, H_e]\) of \(c\) to produce a dense 3D point cloud... | [1] | [
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] | https://openalex.org/W2963170338 |
4dd3e45d-4cb8-4c1e-9a3c-524275b03026 | The graph structure together with the edge weights define a new metric that quantifies the similarity between two points. In this graph, two points that are connected by a path are considered to be close if the path has low total edge weight, namely, the points along the path share similar PP scores, indicating these p... | [1] | [
[
615,
618
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] | https://openalex.org/W1673310716 |
6201e7f7-3a1e-4090-8037-85dc3dc0b6ec | Concretely, we simply take off-the-shelf 3D object detectors [1]}, [2]}, [3]}, [4]} and directly train them from scratch on these initial seed labels via minimizing the corresponding detection loss from the detection algorithms.
| [1] | [
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64
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] | https://openalex.org/W2949708697 |
8ae03edd-0665-494f-9ca8-ce09f486e586 | Concretely, we simply take off-the-shelf 3D object detectors [1]}, [2]}, [3]}, [4]} and directly train them from scratch on these initial seed labels via minimizing the corresponding detection loss from the detection algorithms.
| [3] | [
[
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76
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] | https://openalex.org/W2963727135 |
a0e21ac6-c0fe-49fe-a19c-15402908a7dd | Concretely, we simply take off-the-shelf 3D object detectors [1]}, [2]}, [3]}, [4]} and directly train them from scratch on these initial seed labels via minimizing the corresponding detection loss from the detection algorithms.
| [4] | [
[
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82
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] | https://openalex.org/W2897529137 |
75f16918-8728-4067-aa02-bd9c5a5c93dc | Intriguingly, the object detector trained in this way outperforms the original seed bounding boxes themselves — the “detected” boxes have higher recall and are more accurate than the “discovered” boxes on the same training point clouds. See fig:teaser for an illustration.
This phenomenon of a neural network improving o... | [1] | [
[
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441
]
] | https://openalex.org/W2575671312 |
cb62e046-926a-4efe-9d5a-6a86e26b6b3c | Intriguingly, the object detector trained in this way outperforms the original seed bounding boxes themselves — the “detected” boxes have higher recall and are more accurate than the “discovered” boxes on the same training point clouds. See fig:teaser for an illustration.
This phenomenon of a neural network improving o... | [2] | [
[
1018,
1021
]
] | https://openalex.org/W3178826664 |
8a31145f-7c0d-4c50-8098-fac58f12af24 | Intriguingly, the object detector trained in this way outperforms the original seed bounding boxes themselves — the “detected” boxes have higher recall and are more accurate than the “discovered” boxes on the same training point clouds. See fig:teaser for an illustration.
This phenomenon of a neural network improving o... | [3] | [
[
1063,
1066
]
] | https://openalex.org/W2566079294 |
d949639f-062f-40b8-8e4d-d6a178b412b8 | Automatic improvement through self-training.
Given that the trained detector has discovered many more objects, we can use the detector itself to produce an improved set of ground-truth labels, and re-train a new detector from scratch with these better ground truths.
Furthermore, we can iterate this process: the new ret... | [2] | [
[
661,
664
]
] | https://openalex.org/W3035160371 |
2ec18b39-97e0-4dc6-9b28-4252a6728f02 | Datasets. We validate our approach on two datasets: Lyft Level 5 Perception [1]} and nuScenes [2]}.
To the best of our knowledge, these are the only two publicly available autonomous driving datasets that have both bounding box annotations and multiple traversals with accurate localization. To ensure fair assessment of... | [2] | [
[
94,
97
]
] | https://openalex.org/W3035574168 |
d7e7d570-0746-4887-a62e-2309fe3d7b8e | In addition, we convert the raw Lyft and nuScenes data into the KITTI format to leverage off-the-shelf 3D object detectors that is predominantly built for KITTI [1]}. We use the roof LiDAR (40 or 60 beam in Lyft; 32 beam in nuScenes), and the global 6-DoF localization along with the calibration matrices directly from ... | [1] | [
[
162,
165
]
] | https://openalex.org/W2115579991 |
d7b8a73b-26ca-4f3e-b681-ef94acdb7e7b | On localization. With current localization technology, we can reliably achieve accurate localization (e.g., 1-2 cm-level accuracy with RTKhttps://en.wikipedia.org/wiki/Real-time_kinematic_positioning, 10 cm-level with Monte Carlo Localization scheme [1]} as adopted in the nuScenes dataset [2]}). We assume good localiza... | [2] | [
[
290,
293
]
] | https://openalex.org/W3035574168 |
356886dd-fdee-4810-9ad2-25129961f7f1 | Evaluation metric. We follow KITTI [1]} to evaluate object detection in the bird's-eye view (BEV) and in 3D for the mobile objects. We report average precision (AP) with the intersection over union (IoU) thresholds at 0.5/0.25, which are used to evaluate cyclists and pedestrians objects in KITTI.
We further follow [2]... | [1] | [
[
36,
39
]
] | https://openalex.org/W2150066425 |
be7a2f02-59bb-4ed1-a2cd-567bade6990c | Evaluation metric. We follow KITTI [1]} to evaluate object detection in the bird's-eye view (BEV) and in 3D for the mobile objects. We report average precision (AP) with the intersection over union (IoU) thresholds at 0.5/0.25, which are used to evaluate cyclists and pedestrians objects in KITTI.
We further follow [2]... | [2] | [
[
317,
320
]
] | https://openalex.org/W3034975685 |
cc48ad30-a6a7-46bf-b093-49186de7e8f9 | Implementation. We present results on PointRCNN [1]} (the conclusions hold for other detectors such as PointPillars [2]}, and VoxelNet (SECOND) [3]}, [4]}. See more details in the supplementary materials). For reproducibility, we use the publicly available code from OpenPCDet [5]} for all models. We use the default hy... | [1] | [
[
49,
52
]
] | https://openalex.org/W2949708697 |
62d9cd11-7e91-4794-92de-4badebd15e3c | Implementation. We present results on PointRCNN [1]} (the conclusions hold for other detectors such as PointPillars [2]}, and VoxelNet (SECOND) [3]}, [4]}. See more details in the supplementary materials). For reproducibility, we use the publicly available code from OpenPCDet [5]} for all models. We use the default hy... | [3] | [
[
145,
148
]
] | https://openalex.org/W2963727135 |
9611e80e-ad1b-4737-895e-16a83762e127 | Implementation. We present results on PointRCNN [1]} (the conclusions hold for other detectors such as PointPillars [2]}, and VoxelNet (SECOND) [3]}, [4]}. See more details in the supplementary materials). For reproducibility, we use the publicly available code from OpenPCDet [5]} for all models. We use the default hy... | [4] | [
[
151,
154
]
] | https://openalex.org/W2897529137 |
30512044-188f-464a-9a67-6499055ea4d1 | Acknowledgements
This research is supported by grants from the National Science Foundation NSF (III-1618134, III-1526012, IIS-1149882, IIS-1724282, TRIPODS-1740822, IIS-2107077, OAC-2118240, OAC-2112606 and IIS-2107161),
the Office of Naval Research DOD (N00014-17-1-2175), the DARPA Learning with Less Labels program (H... | [1] | [
[
1887,
1890
]
] | https://openalex.org/W2949708697 |
f98ceb15-6941-4259-a496-137e244149c4 | Acknowledgements
This research is supported by grants from the National Science Foundation NSF (III-1618134, III-1526012, IIS-1149882, IIS-1724282, TRIPODS-1740822, IIS-2107077, OAC-2118240, OAC-2112606 and IIS-2107161),
the Office of Naval Research DOD (N00014-17-1-2175), the DARPA Learning with Less Labels program (H... | [3] | [
[
1972,
1975
]
] | https://openalex.org/W2963727135 |
269e60a8-f66f-451c-8c4a-c206a34767bc | Acknowledgements
This research is supported by grants from the National Science Foundation NSF (III-1618134, III-1526012, IIS-1149882, IIS-1724282, TRIPODS-1740822, IIS-2107077, OAC-2118240, OAC-2112606 and IIS-2107161),
the Office of Naval Research DOD (N00014-17-1-2175), the DARPA Learning with Less Labels program (H... | [4] | [
[
1978,
1981
]
] | https://openalex.org/W2897529137 |
91a91ad7-c57f-46bf-851d-513f26cb4e83 | Besides the PointRCNN detector [1]}, We experiment with two other detectors PointPillars [2]} and VoxelNet (SECOND) [3]}, [4]}, and show their results in tbl:second and tbl:pointpillars. We apply the default hyper-parameters of these two models tuned on KITTI, and apply the same procedure as that on PointRCNN models. N... | [1] | [
[
31,
34
]
] | https://openalex.org/W2949708697 |
2e44d642-1e6c-465c-99c3-b4c2e600c56f | Besides the PointRCNN detector [1]}, We experiment with two other detectors PointPillars [2]} and VoxelNet (SECOND) [3]}, [4]}, and show their results in tbl:second and tbl:pointpillars. We apply the default hyper-parameters of these two models tuned on KITTI, and apply the same procedure as that on PointRCNN models. N... | [3] | [
[
116,
119
]
] | https://openalex.org/W2963727135 |
4e47c945-7464-492c-af5f-fbf0e8bbccb8 | Besides the PointRCNN detector [1]}, We experiment with two other detectors PointPillars [2]} and VoxelNet (SECOND) [3]}, [4]}, and show their results in tbl:second and tbl:pointpillars. We apply the default hyper-parameters of these two models tuned on KITTI, and apply the same procedure as that on PointRCNN models. N... | [4] | [
[
122,
125
]
] | https://openalex.org/W2897529137 |
a30e2945-44da-4aa7-b69c-9008f8ae8ab3 | Image inpainting, or image completion, is a task about image synthesis technique aims to filling occluded regions or missing pixels with appropriate semantic contents. The main objective of image inpainting is producing visually authentic images with less semantic inconsistency using computer vision-based approaches. T... | [3] | [
[
958,
961
]
] | https://openalex.org/W2963420272 |
d08be01e-5527-45bb-8962-682e875615bd | Image inpainting, or image completion, is a task about image synthesis technique aims to filling occluded regions or missing pixels with appropriate semantic contents. The main objective of image inpainting is producing visually authentic images with less semantic inconsistency using computer vision-based approaches. T... | [4] | [
[
964,
967
]
] | https://openalex.org/W2738588019 |
f725bb2c-14c4-40de-97cc-7e7a1a7eefeb | Image inpainting, or image completion, is a task about image synthesis technique aims to filling occluded regions or missing pixels with appropriate semantic contents. The main objective of image inpainting is producing visually authentic images with less semantic inconsistency using computer vision-based approaches. T... | [5] | [
[
970,
973
]
] | https://openalex.org/W2796286534 |
4aac10d4-3934-4eeb-9c20-8ed4d5ef875f | Image inpainting, or image completion, is a task about image synthesis technique aims to filling occluded regions or missing pixels with appropriate semantic contents. The main objective of image inpainting is producing visually authentic images with less semantic inconsistency using computer vision-based approaches. T... | [6] | [
[
976,
979
]
] | https://openalex.org/W3043547428 |
833eac53-d21d-48d1-8556-63ab46e85b9e | Image inpainting, or image completion, is a task about image synthesis technique aims to filling occluded regions or missing pixels with appropriate semantic contents. The main objective of image inpainting is producing visually authentic images with less semantic inconsistency using computer vision-based approaches. T... | [7] | [
[
982,
985
]
] | https://openalex.org/W2982763192 |
f0d84e4a-e011-43aa-86b7-3861411d01da | Image inpainting, or image completion, is a task about image synthesis technique aims to filling occluded regions or missing pixels with appropriate semantic contents. The main objective of image inpainting is producing visually authentic images with less semantic inconsistency using computer vision-based approaches. T... | [8] | [
[
988,
991
]
] | https://openalex.org/W3026446890 |
64304234-ed2b-40fd-9f92-ba48976da5e8 | However, despite GAN's high image restoration performance, some pixel artifacts or color inconsistency called 'fake texture' inevitably occur in the process of decoding [1]}, [2]}. Fake pixels cause degradation of image restoration performance by dropping the appearance consistency in the synthesized image. To tackle t... | [2] | [
[
175,
178
]
] | https://openalex.org/W3012472557 |
91f8c837-28f1-4f41-b55c-057a1c9dd28b | However, despite GAN's high image restoration performance, some pixel artifacts or color inconsistency called 'fake texture' inevitably occur in the process of decoding [1]}, [2]}. Fake pixels cause degradation of image restoration performance by dropping the appearance consistency in the synthesized image. To tackle t... | [3] | [
[
485,
488
]
] | https://openalex.org/W2804078698 |
5f76847c-cf08-4fb3-ab5a-895ab8592911 | However, despite GAN's high image restoration performance, some pixel artifacts or color inconsistency called 'fake texture' inevitably occur in the process of decoding [1]}, [2]}. Fake pixels cause degradation of image restoration performance by dropping the appearance consistency in the synthesized image. To tackle t... | [4] | [
[
620,
623
]
] | https://openalex.org/W2985764327 |
206db2dd-0956-4b1b-be7f-ff78ef22c107 | However, despite GAN's high image restoration performance, some pixel artifacts or color inconsistency called 'fake texture' inevitably occur in the process of decoding [1]}, [2]}. Fake pixels cause degradation of image restoration performance by dropping the appearance consistency in the synthesized image. To tackle t... | [5] | [
[
626,
629
]
] | https://openalex.org/W3026446890 |
64d69187-7a1a-4281-8336-e682ab994d65 | Traditional image inpainting methods were based on the exemplar-search approach, which divides image into patches to refill missing areas with other patches according to similarity computations such as PatchMatch [1]}. Recently, progressive improvement of deep learning based generative models have demonstrated high fea... | [3] | [
[
454,
457
],
[
814,
817
]
] | https://openalex.org/W2963420272 |
7b18da8d-5f3e-4c13-af18-0a7143549d35 | Traditional image inpainting methods were based on the exemplar-search approach, which divides image into patches to refill missing areas with other patches according to similarity computations such as PatchMatch [1]}. Recently, progressive improvement of deep learning based generative models have demonstrated high fea... | [4] | [
[
544,
547
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[
823,
826
]
] | https://openalex.org/W2738588019 |
f63a1da9-3bee-4629-be9a-b3f0969fcbc9 | Traditional image inpainting methods were based on the exemplar-search approach, which divides image into patches to refill missing areas with other patches according to similarity computations such as PatchMatch [1]}. Recently, progressive improvement of deep learning based generative models have demonstrated high fea... | [5] | [
[
664,
667
],
[
835,
838
]
] | https://openalex.org/W3043547428 |
c8b04b9f-4157-4698-83dc-124e9b9502bc | Partial conv [1]} did not employ GAN for inpainting, but solved the problem of generalization on irregular masks. It propose rule-based binary mask which is updated layer by layer in encoder-decoder network and showed high feasibility of refilling irregular masks. This mask-based inpainting approach is advanced in Gate... | [1] | [
[
13,
16
],
[
417,
420
]
] | https://openalex.org/W2798365772 |
61a1fb55-80c9-4bbf-adf8-1209a3e80e9c | Partial conv [1]} did not employ GAN for inpainting, but solved the problem of generalization on irregular masks. It propose rule-based binary mask which is updated layer by layer in encoder-decoder network and showed high feasibility of refilling irregular masks. This mask-based inpainting approach is advanced in Gate... | [2] | [
[
327,
330
],
[
437,
440
]
] | https://openalex.org/W2982763192 |
311a7cb3-6c20-4889-8f40-e6f5a14039b6 | Partial conv [1]} did not employ GAN for inpainting, but solved the problem of generalization on irregular masks. It propose rule-based binary mask which is updated layer by layer in encoder-decoder network and showed high feasibility of refilling irregular masks. This mask-based inpainting approach is advanced in Gate... | [5] | [
[
546,
549
]
] | https://openalex.org/W2804078698 |
2487d61a-b68b-42ed-8e2a-a145fcf52944 | The goal of generator \(G\) is to fill missing parts with appropriate contents by understanding the input image \(x\) (encoding) and synthesizing the output image \(G(x)\) (decoding). Fig. REF describes the overall architecture of generator \(G\) . The coarse reconstruction stage begins by filling pixels with a rou... | [1] | [
[
515,
518
]
] | https://openalex.org/W2194775991 |
589e2785-8407-4215-96bd-9d561d799c2b | The goal of generator \(G\) is to fill missing parts with appropriate contents by understanding the input image \(x\) (encoding) and synthesizing the output image \(G(x)\) (decoding). Fig. REF describes the overall architecture of generator \(G\) . The coarse reconstruction stage begins by filling pixels with a rou... | [2] | [
[
594,
597
]
] | https://openalex.org/W1901129140 |
635a17e8-f207-4696-8943-e86350a5d9ab | The goal of generator \(G\) is to fill missing parts with appropriate contents by understanding the input image \(x\) (encoding) and synthesizing the output image \(G(x)\) (decoding). Fig. REF describes the overall architecture of generator \(G\) . The coarse reconstruction stage begins by filling pixels with a rou... | [3] | [
[
623,
626
]
] | https://openalex.org/W2412782625 |
7106f6f6-e877-49cd-a447-5a11cdbcfcca | Discriminator \(D\) serves as a criticizer that distinguishes between real and synthesized images. Adversarial training between \(G\) and \(D\) can further improve the quality of synthesized image. Because local discriminator has critical limitations on handling irregular mask as mentioned in section 2., we use one ... | [1] | [
[
422,
425
]
] | https://openalex.org/W3043547428 |
2d314833-f297-4066-9ce0-7d5924329d78 | Similar to fakeness prediction in [1]}, fakeness map \({M}_{i}\) is produced through 1x1 convolutional filters and sigmoid function from feature \({F}_{i}\) . Then, we can use \({M}_{i}\) as an attention map like [2]}. After element-wise multiplication of \({M}_{i} \otimes {F}_{i}\) , the output feature \({F^{\prime ... | [2] | [
[
215,
218
]
] | https://openalex.org/W2804078698 |
89439ebe-58df-4219-9ad6-ff5934c03876 | Our model was trained on two datasets: CelebA-HQ and [1]} Places2 [2]}. We randomly divided the 30,000 images in CelebA-HQ dataset into a training set of 27,000 images and a validation set of 3,000 images. In Places2 dataset, we select same categories as [3]} in training set and tested our model on validation set. All ... | [1] | [
[
53,
56
]
] | https://openalex.org/W2962760235 |
12a97c61-29f3-46b3-af9c-4f5747670de4 | Our model was trained on two datasets: CelebA-HQ and [1]} Places2 [2]}. We randomly divided the 30,000 images in CelebA-HQ dataset into a training set of 27,000 images and a validation set of 3,000 images. In Places2 dataset, we select same categories as [3]} in training set and tested our model on validation set. All ... | [2] | [
[
66,
69
]
] | https://openalex.org/W2732026016 |
694fed95-3aaa-45ae-9911-6fb09a6cebc8 | Our model was trained on two datasets: CelebA-HQ and [1]} Places2 [2]}. We randomly divided the 30,000 images in CelebA-HQ dataset into a training set of 27,000 images and a validation set of 3,000 images. In Places2 dataset, we select same categories as [3]} in training set and tested our model on validation set. All ... | [3] | [
[
255,
258
]
] | https://openalex.org/W3175375202 |
cd10e4d4-0796-4156-b537-a717d2b1c4f2 | To prepare input images for our model, we defined the centered mask and random mask. The centered mask has 64 \(\times \) 64 size fixed in the center of the image, and the random mask has an irregular shape following the mask generation approach in [1]}. We used an ADAM optimizer [2]} in this experiment, and hyper-par... | [2] | [
[
282,
285
]
] | https://openalex.org/W2964121744 |
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