DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yoon, Jae Shin | ko |
dc.contributor.author | Rameau, Francois | ko |
dc.contributor.author | Kim, Junsik | ko |
dc.contributor.author | Lee, Seokju | ko |
dc.contributor.author | Shin, Seunghak | ko |
dc.contributor.author | Kweon, In-So | ko |
dc.date.accessioned | 2017-12-05T02:20:31Z | - |
dc.date.available | 2017-12-05T02:20:31Z | - |
dc.date.created | 2017-11-29 | - |
dc.date.created | 2017-11-29 | - |
dc.date.created | 2017-11-29 | - |
dc.date.issued | 2017-10 | - |
dc.identifier.citation | 16th IEEE International Conference on Computer Vision (ICCV), pp.2186 - 2195 | - |
dc.identifier.issn | 1550-5499 | - |
dc.identifier.uri | http://hdl.handle.net/10203/227591 | - |
dc.description.abstract | We propose a novel video object segmentation algorithm based on pixel-level matching using Convolutional Neural Networks (CNN). Our network aims to distinguish the target area from the background on the basis of the pixel-level similarity between two object units. The proposed network represents a target object using features from different depth layers in order to take advantage of both the spatial details and the category-level semantic information. Furthermore, we propose a feature compression technique that drastically reduces the memory requirements while maintaining the capability of feature representation. Two-stage training (pretraining and fine-tuning) allows our network to handle any target object regardless of its category (even if the object's type does not belong to the pre-training data) or of variations in its appearance through a video sequence. Experiments on large datasets demonstrate the effectiveness of our model -against related methods - in terms of accuracy, speed, and stability. Finally, we introduce the transferability of our network to different domains, such as the infrared data domain. | - |
dc.language | English | - |
dc.publisher | IEEE Computer Society and the Computer Vision Foundation (CVF) | - |
dc.title | Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks | - |
dc.type | Conference | - |
dc.identifier.wosid | 000425498402026 | - |
dc.identifier.scopusid | 2-s2.0-85041906830 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 2186 | - |
dc.citation.endingpage | 2195 | - |
dc.citation.publicationname | 16th IEEE International Conference on Computer Vision (ICCV) | - |
dc.identifier.conferencecountry | IT | - |
dc.identifier.conferencelocation | Venice Convention Center, Venice | - |
dc.identifier.doi | 10.1109/ICCV.2017.238 | - |
dc.contributor.localauthor | Kweon, In-So | - |
dc.contributor.nonIdAuthor | Yoon, Jae Shin | - |
dc.contributor.nonIdAuthor | Rameau, Francois | - |
dc.contributor.nonIdAuthor | Kim, Junsik | - |
dc.contributor.nonIdAuthor | Lee, Seokju | - |
dc.contributor.nonIdAuthor | Shin, Seunghak | - |
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