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 with Learning Through Interaction (LTI)%2C allowing a model to learn by observing scene alterations and leverage visual consistency despite temporal gaps without requiring curated data of interaction sequences. As a result%2C our approach exploits partially annotated data through self-supervision and incorporates temporal context using pseudo-sequences generated from unlabeled still images. We validate our method on two common benchmarks%2C ARMBench mix-object-tote and OCID%2C where it achieves state-of-the-art performance. Notably%2C on ARMBench%2C we attain an %24text{AP}_{50}%24 of %2486.37%24%2C almost a %2420%25%24 improvement over existing work%2C and obtain remarkable results in scenarios with extremely low annotation%2C achieving an %24text{AP}_{50}%24 score of %2484.89%24 with just %241 %25%24 of annotated data compared to %2472%24 presented in ARMBench on the fully annotated counterpart.?quality=80&w=800)
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