  Unsupervised object discovery and localization aims to detect or segment
objects in an image without any supervision. Recent efforts have demonstrated a
notable potential to identify salient foreground objects by utilizing
self-supervised transformer features. However, their scopes only build upon
patch-level features within an image, neglecting region/image-level and
cross-image relationships at a broader scale. Moreover, these methods cannot
differentiate various semantics from multiple instances. To address these
problems, we introduce Hierarchical mErging framework via contrAstive grouPing
(HEAP). Specifically, a novel lightweight head with cross-attention mechanism
is designed to adaptively group intra-image patches into semantically coherent
regions based on correlation among self-supervised features. Further, to ensure
the distinguishability among various regions, we introduce a region-level
contrastive clustering loss to pull closer similar regions across images. Also,
an image-level contrastive loss is present to push foreground and background
representations apart, with which foreground objects and background are
accordingly discovered. HEAP facilitates efficient hierarchical image
decomposition, which contributes to more accurate object discovery while also
enabling differentiation among objects of various classes. Extensive
experimental results on semantic segmentation retrieval, unsupervised object
discovery, and saliency detection tasks demonstrate that HEAP achieves
state-of-the-art performance.
