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NDD20: A large-scale few-shot dolphin dataset for coarse and fine-grained categorisation

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arxiv 2005.13359 v1 pith:DCAE5A5X submitted 2020-05-27 cs.CV

classification cs.CV
keywords ndd20datasetcoarsedolphinfine-grainedcategorisationcollectionnorthumberland
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We introduce the Northumberland Dolphin Dataset 2020 (NDD20), a challenging image dataset annotated for both coarse and fine-grained instance segmentation and categorisation. This dataset, the first release of the NDD, was created in response to the rapid expansion of computer vision into conservation research and the production of field-deployable systems suited to extreme environmental conditions -- an area with few open source datasets. NDD20 contains a large collection of above and below water images of two different dolphin species for traditional coarse and fine-grained segmentation. All data contained in NDD20 was obtained via manual collection in the North Sea around the Northumberland coastline, UK. We present experimentation using standard deep learning network architecture trained using NDD20 and report baselines results.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Segment This Thing: Foveated Tokenization for Efficient Point-Prompted Segmentation

    cs.CV 2025-06 conditional novelty 7.0 of 10

    A point-prompted segmentation model gains efficiency by foveated tokenization, cutting tokens from 4096 to 172 while staying competitive on mIoU benchmarks.

  2. GaRA-SAM: Robustifying Segment Anything Model with Gated-Rank Adaptation

    cs.CV 2025-06 conditional novelty 6.0 of 10

    GaRA-SAM improves SAM's robustness to image corruption by using input-dependent gating to adjust the effective rank of low-rank adapters, beating prior methods on robust segmentation benchmarks.

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