Improve object discovery with self-supervised transformers using TokenCut

Improve object discovery with self-supervised transformers using TokenCut

Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut

arXiv paper abstract https://arxiv.org/abs/2202.11539

arXiv PDF paper https://arxiv.org/pdf/2202.11539.pdf

Transformers trained with self-supervised learning using self-distillation loss (DINO) have been shown to produce attention maps that highlight salient foreground objects.

... In this paper, ... demonstrate a graph-based approach that uses the self-supervised transformer features to discover an object from an image.

Visual tokens are ... nodes in a weighted graph with edges representing a connectivity score based on the similarity of tokens.

Foreground objects can then be segmented using a normalized graph-cut to group self-similar regions.

... solve the graph-cut ... using spectral clustering with generalized eigen-decomposition and show ... second smallest eigenvector ... a cutting solution since its absolute value indicates ... token belongs to a foreground object.

... performance of unsupervised object discovery: ... improve over ... state of the art LOST by a margin of 6.9%, 8.1%, and 8.1% respectively on the VOC07, VOC12, and COCO20K. ...

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