Meta-learning deep visual words for fast video object segmentation
Accurate video object segmentation methods finetune a model using the first annotated frame, and/or use additional inputs such as optical flow and complex post-processing. In contrast, we develop a fast algorithm that requires no finetuning, auxiliary inputs or post-processing, and segments a variab...
| 主要な著者: | , , , |
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| フォーマット: | Conference item |
| 言語: | English |
| 出版事項: |
2019
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| _version_ | 1826295845226545152 |
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| author | Behl, HS Najaf, M Arnab, A Torr, PHS |
| author_facet | Behl, HS Najaf, M Arnab, A Torr, PHS |
| author_sort | Behl, HS |
| collection | OXFORD |
| description | Accurate video object segmentation methods finetune a model using the first annotated frame, and/or use additional
inputs such as optical flow and complex post-processing. In contrast, we develop a fast algorithm that requires no finetuning, auxiliary inputs or post-processing, and segments a variable number of objects in a single forward-pass. We
represent an object with clusters, or “visual words”, in the embedding space, which correspond to object parts in the image space. This allows us to robustly match to the reference objects throughout the video, because although the
global appearance of an object changes as it undergoes occlusions and deformations, the appearance of more local parts may stay consistent. We learn these visual words in an unsupervised manner, using meta-learning to ensure that
our training objective matches our inference procedure. We achieve comparable accuracy to finetuning based methods,
and state-of-the-art in terms of speed/accuracy trade-offs on four video segmentation datasets. |
| first_indexed | 2024-03-07T04:07:14Z |
| format | Conference item |
| id | oxford-uuid:c697af74-5ec8-4d5b-addc-bd2a0ae7e189 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T04:07:14Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | oxford-uuid:c697af74-5ec8-4d5b-addc-bd2a0ae7e1892022-03-27T06:39:11ZMeta-learning deep visual words for fast video object segmentationConference itemhttp://purl.org/coar/resource_type/c_5794uuid:c697af74-5ec8-4d5b-addc-bd2a0ae7e189EnglishSymplectic Elements2019Behl, HSNajaf, MArnab, ATorr, PHSAccurate video object segmentation methods finetune a model using the first annotated frame, and/or use additional inputs such as optical flow and complex post-processing. In contrast, we develop a fast algorithm that requires no finetuning, auxiliary inputs or post-processing, and segments a variable number of objects in a single forward-pass. We represent an object with clusters, or “visual words”, in the embedding space, which correspond to object parts in the image space. This allows us to robustly match to the reference objects throughout the video, because although the global appearance of an object changes as it undergoes occlusions and deformations, the appearance of more local parts may stay consistent. We learn these visual words in an unsupervised manner, using meta-learning to ensure that our training objective matches our inference procedure. We achieve comparable accuracy to finetuning based methods, and state-of-the-art in terms of speed/accuracy trade-offs on four video segmentation datasets. |
| spellingShingle | Behl, HS Najaf, M Arnab, A Torr, PHS Meta-learning deep visual words for fast video object segmentation |
| title | Meta-learning deep visual words for fast video object segmentation |
| title_full | Meta-learning deep visual words for fast video object segmentation |
| title_fullStr | Meta-learning deep visual words for fast video object segmentation |
| title_full_unstemmed | Meta-learning deep visual words for fast video object segmentation |
| title_short | Meta-learning deep visual words for fast video object segmentation |
| title_sort | meta learning deep visual words for fast video object segmentation |
| work_keys_str_mv | AT behlhs metalearningdeepvisualwordsforfastvideoobjectsegmentation AT najafm metalearningdeepvisualwordsforfastvideoobjectsegmentation AT arnaba metalearningdeepvisualwordsforfastvideoobjectsegmentation AT torrphs metalearningdeepvisualwordsforfastvideoobjectsegmentation |