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231225s2023 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2021.3059923
|2 doi
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|a eng
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| 100 |
1 |
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|a Qi, Zhaobo
|e verfasserin
|4 aut
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| 245 |
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|a Self-Regulated Learning for Egocentric Video Activity Anticipation
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|c 2023
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|a Text
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|a ƒaComputermedien
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|a Date Completed 07.05.2023
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|a Date Revised 07.05.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Future activity anticipation is a challenging problem in egocentric vision. As a standard future activity anticipation paradigm, recursive sequence prediction suffers from the accumulation of errors. To address this problem, we propose a simple and effective Self-Regulated Learning framework, which aims to regulate the intermediate representation consecutively to produce representation that (a) emphasizes the novel information in the frame of the current time-stamp in contrast to previously observed content, and (b) reflects its correlation with previously observed frames. The former is achieved by minimizing a contrastive loss, and the latter can be achieved by a dynamic reweighing mechanism to attend to informative frames in the observed content with a similarity comparison between feature of the current frame and observed frames. The learned final video representation can be further enhanced by multi-task learning which performs joint feature learning on the target activity labels and the automatically detected action and object class tokens. SRL sharply outperforms existing state-of-the-art in most cases on two egocentric video datasets and two third-person video datasets. Its effectiveness is also verified by the experimental fact that the action and object concepts that support the activity semantics can be accurately identified
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|a Journal Article
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1 |
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|a Wang, Shuhui
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Su, Chi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Su, Li
|e verfasserin
|4 aut
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| 700 |
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|a Huang, Qingming
|e verfasserin
|4 aut
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| 700 |
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|a Tian, Qi
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 45(2023), 6 vom: 01. Juni, Seite 6715-6730
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
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|g volume:45
|g year:2023
|g number:6
|g day:01
|g month:06
|g pages:6715-6730
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|u http://dx.doi.org/10.1109/TPAMI.2021.3059923
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