  Unsupervised object discovery (UOD) refers to the task of discriminating the
whole region of objects from the background within a scene without relying on
labeled datasets, which benefits the task of bounding-box-level localization
and pixel-level segmentation. This task is promising due to its ability to
discover objects in a generic manner. We roughly categorise existing techniques
into two main directions, namely the generative solutions based on image
resynthesis, and the clustering methods based on self-supervised models. We
have observed that the former heavily relies on the quality of image
reconstruction, while the latter shows limitations in effectively modeling
semantic correlations. To directly target at object discovery, we focus on the
latter approach and propose a novel solution by incorporating weakly-supervised
contrastive learning (WCL) to enhance semantic information exploration. We
design a semantic-guided self-supervised learning model to extract high-level
semantic features from images, which is achieved by fine-tuning the feature
encoder of a self-supervised model, namely DINO, via WCL. Subsequently, we
introduce Principal Component Analysis (PCA) to localize object regions. The
principal projection direction, corresponding to the maximal eigenvalue, serves
as an indicator of the object region(s). Extensive experiments on benchmark
unsupervised object discovery datasets demonstrate the effectiveness of our
proposed solution. The source code and experimental results are publicly
available via our project page at https://github.com/npucvr/WSCUOD.git.
