  In this paper, we introduce VoteCut, an innovative method for unsupervised
object discovery that leverages feature representations from multiple
self-supervised models. VoteCut employs normalized-cut based graph
partitioning, clustering and a pixel voting approach. Additionally, We present
CuVLER (Cut-Vote-and-LEaRn), a zero-shot model, trained using pseudo-labels,
generated by VoteCut, and a novel soft target loss to refine segmentation
accuracy. Through rigorous evaluations across multiple datasets and several
unsupervised setups, our methods demonstrate significant improvements in
comparison to previous state-of-the-art models. Our ablation studies further
highlight the contributions of each component, revealing the robustness and
efficacy of our approach. Collectively, VoteCut and CuVLER pave the way for
future advancements in image segmentation.
