DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Jeongsoo | ko |
dc.contributor.author | Woo, Sangmin | ko |
dc.contributor.author | PARK, BYEONGJUN | ko |
dc.contributor.author | Kim, Changick | ko |
dc.date.accessioned | 2022-11-21T07:00:59Z | - |
dc.date.available | 2022-11-21T07:00:59Z | - |
dc.date.created | 2022-11-18 | - |
dc.date.created | 2022-11-18 | - |
dc.date.issued | 2022-10 | - |
dc.identifier.citation | IEEE International Conference on Image Processing, ICIP 2022, pp.2152 - 2156 | - |
dc.identifier.issn | 1522-4880 | - |
dc.identifier.uri | http://hdl.handle.net/10203/300292 | - |
dc.description.abstract | Camera traps, unmanned observation devices, and deep learning-based image recognition systems have greatly reduced human effort in collecting and analyzing wildlife images. However, data collected via above apparatus exhibits 1) long-tailed and 2) open-ended distribution problems. To tackle the open-set long-tailed recognition problem, we propose the Temporal Flow Mask Attention Network that comprises three key building blocks: 1) an optical flow module, 2) an attention residual module, and 3) a meta-embedding classifier. We extract temporal features of sequential frames using the optical flow module and learn informative representation using attention residual blocks. Moreover, we show that applying the meta-embedding technique boosts the performance of the method in open-set long-tailed recognition. We apply this method on a Korean De-militarized Zone (DMZ) dataset. We conduct extensive experiments, and quantitative and qualitative analyses to prove that our method effectively tackles the open-set long-tailed recognition problem while being robust to unknown classes. | - |
dc.language | English | - |
dc.publisher | IEEE | - |
dc.title | Temporal Flow Mask Attention for Open-Set Long-Tailed Recognition of Wild Animals in Camera-Trap Images | - |
dc.type | Conference | - |
dc.identifier.scopusid | 2-s2.0-85146681336 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 2152 | - |
dc.citation.endingpage | 2156 | - |
dc.citation.publicationname | IEEE International Conference on Image Processing, ICIP 2022 | - |
dc.identifier.conferencecountry | FR | - |
dc.identifier.conferencelocation | Bordeaux | - |
dc.identifier.doi | 10.1109/ICIP46576.2022.9898042 | - |
dc.contributor.localauthor | Kim, Changick | - |
dc.contributor.nonIdAuthor | Kim, Jeongsoo | - |
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