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Selective Synergistic Learning (SSync) improves video object-centric learning by selectively distilling reliable cues via pseudo-labeling and transitive merging, avoiding error propagation from indiscriminate dense alignment.
SemiPrune is a label-efficient dataset pruning framework that uses semi-supervised learning to generate pseudo-labels from a small labeled subset, enabling existing supervised pruning methods to work with unlabeled data. It achieves state-of-the-art performance on domain-specific, image-corrupted, and long-tailed datasets.