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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | R. Austin, McEver | - |
| dc.contributor.author | Bowen, Zhang | - |
| dc.contributor.author | Connor, Levenson | - |
| dc.date.accessioned | 2023-04-24T02:08:30Z | - |
| dc.date.available | 2023-04-24T02:08:30Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.uri | https://link.springer.com/article/10.1007/s11263-023-01755-4 | - |
| dc.identifier.uri | https://dlib.phenikaa-uni.edu.vn/handle/PNK/8236 | - |
| dc.description | CC BY | vi |
| dc.description.abstract | Each year, underwater remotely operated vehicles (ROVs) collect thousands of hours of video of unexplored ocean habitats revealing a plethora of information regarding biodiversity on Earth. However, fully utilizing this information remains a challenge as proper annotations and analysis require trained scientists’ time, which is both limited and costly. To this end, we present a Dataset for Underwater Substrate and Invertebrate Analysis (DUSIA), a benchmark suite and growing large-scale dataset to train, validate, and test methods for temporally localizing four underwater substrates as well as temporally and spatially localizing 59 underwater invertebrate species. | vi |
| dc.language.iso | en | vi |
| dc.publisher | Springer | vi |
| dc.subject | ROVs | vi |
| dc.subject | DUSIA | vi |
| dc.title | Context-Driven Detection of Invertebrate Species in Deep-Sea Video | vi |
| dc.type | Book | vi |
| Appears in Collections | OER - Công nghệ thông tin | |
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