Figure 1.

An illustration of weakly-supervised clustering: Unlike the original feature space (left) that encloses different semantics and noisy annotations in neighboring images, weakly supervised clustering (right) finds a new embedding space where image clusters possess visual-semantic coherence. The proposed approach scales up to a large number of images, and offers outlier/noise pruning by design. Each cluster is re-annotated as the same class by majority voting, and will be included for training AU detectors.