gen2seg: Generative Models Enable Generalizable Instance Segmentation
Authors: name: Om Khangaonkar, name: Hamed Pirsiavash

By pretraining to synthesize coherent images from perturbed inputs,
generative models inherently learn to understand object boundaries and scene
compositions. How can we repurpose these generative representations for
general-purpose perceptual organization? We finetune Stable Diffusion and MAE
(encoder+decoder) for category-agnostic instance segmentation using our
instance coloring loss exclusively on a narrow set of object types (indoor
furnishings and cars). Surprisingly, our models exhibit strong zero-shot
generalization, accurately segmenting objects of types and styles unseen in
finetuning (and in many cases, MAE's ImageNet-1K pretraining too). Our
best-performing models closely approach the heavily supervised SAM when
evaluated on unseen object types and styles, and outperform it when segmenting
fine structures and ambiguous boundaries. In contrast, existing promptable
segmentation architectures or discriminatively pretrained models fail to
generalize. This suggests that generative models learn an inherent grouping
mechanism that transfers across categories and domains, even without
internet-scale pretraining. Code, pretrained models, and demos are available on
our website.