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In this paper, we study the problem of unsupervised object segmentation fromsingle images. We do not introduce a new algorithm, but systematicallyinvestigate the effectiveness of existing unsupervised models on challengingreal-world images. We firstly introduce four complexity factors toquantitatively measure the distributions of object- and scene-level biases inappearance and geometry for datasets with human annotations. With the aid ofthese factors, we empirically find that, not surprisingly, existingunsupervised models catastrophically fail to segment generic objects inreal-world images, although they can easily achieve excellent performance onnumerous simple synthetic datasets, due to the vast gap in objectness biasesbetween synthetic and real images. By conducting extensive experiments onmultiple groups of ablated real-world datasets, we ultimately find that the keyfactors underlying the colossal failure of existing unsupervised models onreal-world images are the challenging distributions of object- and scene-levelbiases in appearance and geometry. Because of this, the inductive biasesintroduced in existing unsupervised models can hardly capture the diverseobject distributions. Our research results suggest that future work shouldexploit more explicit objectness biases in the network design.

Promising or Elusive? Unsupervised Object Segmentation from Real-world Single Images