The authors introduce VoteCut and CuVLER, innovative methods for unsupervised object discovery and segmentation, showcasing significant improvements over previous state-of-the-art models.
VoteCut and CuVLER revolutionize unsupervised object discovery with self-supervised transformers.
Unsupervised object discovery can be improved by selectively masking background regions during training and using multi-query slot attention to learn more stable and generalizable object representations.