Understanding Persistence in 3D Object Memory from Egocentric Videos
Overview
A study explores object memory persistence using egocentric videos, improving accuracy with location tracking. It enhances HD-EPIC and UCS-Bench scores and achieves high precision in Ego4D object localization.
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Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · Reported accuracy: 29.7 percent · Basis not reported · Differing source assertions
“ion noise. Short descriptions preserve details such as an object's contents or supporting surface. It saves these records to later answer spatial questions without having to access the original images or video. Our memory raises HD-EPIC accuracy from 29.7% to 42.6%, UCS-Bench accuracy from 33.8% to 38.5% and localizes Ego4D objects with a 0.99 m median error on returned predictions. Our analyses identify complementar”
Exact source · revision 1Source owner not reported
Reported quantity · Reported accuracy: 42.6 percent · Basis not reported · Differing source assertions
“ion noise. Short descriptions preserve details such as an object's contents or supporting surface. It saves these records to later answer spatial questions without having to access the original images or video. Our memory raises HD-EPIC accuracy from 29.7% to 42.6%, UCS-Bench accuracy from 33.8% to 38.5% and localizes Ego4D objects with a 0.99 m median error on returned predictions. Our analyses identify complementar”
Exact source · revision 1Source owner not reported
Reported quantity · Reported accuracy: 33.8 percent · Basis not reported · Differing source assertions
“ion noise. Short descriptions preserve details such as an object's contents or supporting surface. It saves these records to later answer spatial questions without having to access the original images or video. Our memory raises HD-EPIC accuracy from 29.7% to 42.6%, UCS-Bench accuracy from 33.8% to 38.5% and localizes Ego4D objects with a 0.99 m median error on returned predictions. Our analyses identify complementar”
Exact source · revision 1Source owner not reported
Reported quantity · Reported accuracy: 38.5 percent · Basis not reported · Differing source assertions
“ion noise. Short descriptions preserve details such as an object's contents or supporting surface. It saves these records to later answer spatial questions without having to access the original images or video. Our memory raises HD-EPIC accuracy from 29.7% to 42.6%, UCS-Bench accuracy from 33.8% to 38.5% and localizes Ego4D objects with a 0.99 m median error on returned predictions. Our analyses identify complementar”
Exact source · revision 1Source owner not reported
Reported quantity · median error: 0.99 other · Basis not reported
“ion noise. Short descriptions preserve details such as an object's contents or supporting surface. It saves these records to later answer spatial questions without having to access the original images or video. Our memory raises HD-EPIC accuracy from 29.7% to 42.6%, UCS-Bench accuracy from 33.8% to 38.5% and localizes Ego4D objects with a 0.99 m median error on returned predictions. Our analyses identify complementar”
Exact source · revision 1Source owner not reported
Reported quantity · number of streams: 100 other · Basis not reported
“y roles for temporal persistence, contextual descriptions, and retrieval. Our study on 100 stitched streams of multiple scenes each further exposes failures in both retrieval and construction. Per-scene construction partially recovers the performance lost across scene changes compared to that of single scene streams.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsUnderstanding Persistence in 3D Object Memory from Egocentric Videos
Ledger improves object memory accuracy by tracking locations and histories. It enhances HD-EPIC and UCS-Bench scores and localizes Ego4D objects with high precision.
Event coverage history
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