L2G-Det detects and segments novel object instances in open scenes by using local template patch matches to generate points that prompt an augmented SAM for global masks.
What makes for good views for contrastive learning?Advances in neural information processing systems, 33:6827–6839, 2020
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CoUn emulates retrained-model behavior on forget data by using contrastive learning on retain data to adjust semantic representations while preserving retain clusters via supervised learning, outperforming prior MU methods in experiments.
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From Local Matches to Global Masks: Template-Guided Instance Detection and Segmentation in Open-World Scenes
L2G-Det detects and segments novel object instances in open scenes by using local template patch matches to generate points that prompt an augmented SAM for global masks.
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CoUn: Empowering Machine Unlearning via Contrastive Learning
CoUn emulates retrained-model behavior on forget data by using contrastive learning on retain data to adjust semantic representations while preserving retain clusters via supervised learning, outperforming prior MU methods in experiments.