TeFF: Tracking-enhanced Forgetting-free Few-shot 3D LiDAR Semantic Segmentation Article Swipe
Junbao Zhou
,
Jilin Mei
,
Pengze Wu
,
Liang Chen
,
Fangzhou Zhao
,
Xijun Zhao
,
Yu Hu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2408.15657
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2408.15657
In autonomous driving, 3D LiDAR plays a crucial role in understanding the vehicle's surroundings. However, the newly emerged, unannotated objects presents few-shot learning problem for semantic segmentation. This paper addresses the limitations of current few-shot semantic segmentation by exploiting the temporal continuity of LiDAR data. Employing a tracking model to generate pseudo-ground-truths from a sequence of LiDAR frames, our method significantly augments the dataset, enhancing the model's ability to learn on novel classes. However, this approach introduces a data imbalance biased to novel data that presents a new challenge of catastrophic forgetting. To mitigate this, we incorporate LoRA, a technique that reduces the number of trainable parameters, thereby preserving the model's performance on base classes while improving its adaptability to novel classes. This work represents a significant step forward in few-shot 3D LiDAR semantic segmentation for autonomous driving. Our code is available at https://github.com/junbao-zhou/Track-no-forgetting.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2408.15657
- https://arxiv.org/pdf/2408.15657
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402705939
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4402705939Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2408.15657Digital Object Identifier
- Title
-
TeFF: Tracking-enhanced Forgetting-free Few-shot 3D LiDAR Semantic SegmentationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-08-28Full publication date if available
- Authors
-
Junbao Zhou, Jilin Mei, Pengze Wu, Liang Chen, Fangzhou Zhao, Xijun Zhao, Yu HuList of authors in order
- Landing page
-
https://arxiv.org/abs/2408.15657Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2408.15657Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2408.15657Direct OA link when available
- Concepts
-
Shot (pellet), Lidar, Forgetting, Segmentation, Artificial intelligence, Tracking (education), Computer science, Computer vision, Single shot, Remote sensing, Geology, Cognitive psychology, Psychology, Physics, Materials science, Optics, Metallurgy, PedagogyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.approach | 75 |
| abstract_inverted_index.augments | 61 |
| abstract_inverted_index.classes. | 72, 121 |
| abstract_inverted_index.dataset, | 63 |
| abstract_inverted_index.driving, | 2 |
| abstract_inverted_index.driving. | 137 |
| abstract_inverted_index.emerged, | 17 |
| abstract_inverted_index.few-shot | 21, 34, 130 |
| abstract_inverted_index.generate | 50 |
| abstract_inverted_index.learning | 22 |
| abstract_inverted_index.mitigate | 93 |
| abstract_inverted_index.presents | 20, 85 |
| abstract_inverted_index.semantic | 25, 35, 133 |
| abstract_inverted_index.sequence | 54 |
| abstract_inverted_index.temporal | 40 |
| abstract_inverted_index.tracking | 47 |
| abstract_inverted_index.Employing | 45 |
| abstract_inverted_index.addresses | 29 |
| abstract_inverted_index.available | 141 |
| abstract_inverted_index.challenge | 88 |
| abstract_inverted_index.enhancing | 64 |
| abstract_inverted_index.imbalance | 79 |
| abstract_inverted_index.improving | 116 |
| abstract_inverted_index.technique | 99 |
| abstract_inverted_index.trainable | 105 |
| abstract_inverted_index.vehicle's | 12 |
| abstract_inverted_index.autonomous | 1, 136 |
| abstract_inverted_index.continuity | 41 |
| abstract_inverted_index.exploiting | 38 |
| abstract_inverted_index.introduces | 76 |
| abstract_inverted_index.preserving | 108 |
| abstract_inverted_index.represents | 124 |
| abstract_inverted_index.forgetting. | 91 |
| abstract_inverted_index.incorporate | 96 |
| abstract_inverted_index.limitations | 31 |
| abstract_inverted_index.parameters, | 106 |
| abstract_inverted_index.performance | 111 |
| abstract_inverted_index.significant | 126 |
| abstract_inverted_index.unannotated | 18 |
| abstract_inverted_index.adaptability | 118 |
| abstract_inverted_index.catastrophic | 90 |
| abstract_inverted_index.segmentation | 36, 134 |
| abstract_inverted_index.segmentation. | 26 |
| abstract_inverted_index.significantly | 60 |
| abstract_inverted_index.surroundings. | 13 |
| abstract_inverted_index.understanding | 10 |
| abstract_inverted_index.pseudo-ground-truths | 51 |
| abstract_inverted_index.https://github.com/junbao-zhou/Track-no-forgetting. | 143 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |