Temporal RoI Align for Video Object Recognition Article Swipe
Tao Gong
,
Kai Chen
,
Xinjiang Wang
,
Qi Chu
,
Feng Zhu
,
Dahua Lin
,
Nenghai Yu
,
Huamin Feng
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2109.03495
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2109.03495
Video object detection is challenging in the presence of appearance deterioration in certain video frames. Therefore, it is a natural choice to aggregate temporal information from other frames of the same video into the current frame. However, RoI Align, as one of the most core procedures of video detectors, still remains extracting features from a single-frame feature map for proposals, making the extracted RoI features lack temporal information from videos. In this work, considering the features of the same object instance are highly similar among frames in a video, a novel Temporal RoI Align operator is proposed to extract features from other frames feature maps for current frame proposals by utilizing feature similarity. The proposed Temporal RoI Align operator can extract temporal information from the entire video for proposals. We integrate it into single-frame video detectors and other state-of-the-art video detectors, and conduct quantitative experiments to demonstrate that the proposed Temporal RoI Align operator can consistently and significantly boost the performance. Besides, the proposed Temporal RoI Align can also be applied into video instance segmentation. Codes are available at https://github.com/open-mmlab/mmtracking
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2109.03495
- https://arxiv.org/pdf/2109.03495
- OA Status
- green
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3198281901
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3198281901Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2109.03495Digital Object Identifier
- Title
-
Temporal RoI Align for Video Object RecognitionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-09-08Full publication date if available
- Authors
-
Tao Gong, Kai Chen, Xinjiang Wang, Qi Chu, Feng Zhu, Dahua Lin, Nenghai Yu, Huamin FengList of authors in order
- Landing page
-
https://arxiv.org/abs/2109.03495Publisher landing page
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-
https://arxiv.org/pdf/2109.03495Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2109.03495Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Computer vision, Frame (networking), Region of interest, Feature (linguistics), Segmentation, Object (grammar), Detector, Video tracking, Pattern recognition (psychology), Philosophy, Linguistics, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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36Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.feature | 56, 103, 111 |
| abstract_inverted_index.frames. | 14 |
| abstract_inverted_index.natural | 19 |
| abstract_inverted_index.remains | 50 |
| abstract_inverted_index.similar | 83 |
| abstract_inverted_index.videos. | 69 |
| abstract_inverted_index.Besides, | 161 |
| abstract_inverted_index.However, | 36 |
| abstract_inverted_index.Temporal | 91, 115, 150, 164 |
| abstract_inverted_index.features | 52, 64, 75, 99 |
| abstract_inverted_index.instance | 80, 173 |
| abstract_inverted_index.operator | 94, 118, 153 |
| abstract_inverted_index.presence | 7 |
| abstract_inverted_index.proposed | 96, 114, 149, 163 |
| abstract_inverted_index.temporal | 23, 66, 121 |
| abstract_inverted_index.aggregate | 22 |
| abstract_inverted_index.available | 177 |
| abstract_inverted_index.detection | 2 |
| abstract_inverted_index.detectors | 135 |
| abstract_inverted_index.extracted | 62 |
| abstract_inverted_index.integrate | 130 |
| abstract_inverted_index.proposals | 108 |
| abstract_inverted_index.utilizing | 110 |
| abstract_inverted_index.Therefore, | 15 |
| abstract_inverted_index.appearance | 9 |
| abstract_inverted_index.detectors, | 48, 140 |
| abstract_inverted_index.extracting | 51 |
| abstract_inverted_index.procedures | 45 |
| abstract_inverted_index.proposals, | 59 |
| abstract_inverted_index.proposals. | 128 |
| abstract_inverted_index.challenging | 4 |
| abstract_inverted_index.considering | 73 |
| abstract_inverted_index.demonstrate | 146 |
| abstract_inverted_index.experiments | 144 |
| abstract_inverted_index.information | 24, 67, 122 |
| abstract_inverted_index.similarity. | 112 |
| abstract_inverted_index.consistently | 155 |
| abstract_inverted_index.performance. | 160 |
| abstract_inverted_index.quantitative | 143 |
| abstract_inverted_index.single-frame | 55, 133 |
| abstract_inverted_index.deterioration | 10 |
| abstract_inverted_index.segmentation. | 174 |
| abstract_inverted_index.significantly | 157 |
| abstract_inverted_index.state-of-the-art | 138 |
| abstract_inverted_index.https://github.com/open-mmlab/mmtracking | 179 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.4399999976158142 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| citation_normalized_percentile |