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Conference Papers Year : 2022

Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut

Abstract

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, we demonstrate a graph-based approach that uses the self-supervised transformer features to discover an object from an image. Visual tokens are viewed as 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. We solve the graph-cut problem using spectral clustering with generalized eigen-decomposition and show that the second smallest eigenvector provides a cutting solution since its absolute value indicates the likelihood that a token belongs to a foreground object. Despite its simplicity, this approach significantly boosts the performance of unsupervised object discovery: we improve over the recent state of the art LOST by a margin of 6.9%, 8.1%, and 8.1% respectively on the VOC07, VOC12, and COCO20K. The performance can be further improved by adding a second stage class-agnostic detector (CAD). Our proposed method can be easily extended to unsupervised saliency detection and weakly supervised object detection. For unsupervised saliency detection, we improve IoU for 4.9%, 5.2%, 12.9% on ECSSD, DUTS, DUT-OMRON respectively compared to previous state of the art. For weakly supervised object detection, we achieve competitive performance on CUB and ImageNet.
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Dates and versions

hal-03585410 , version 1 (23-02-2022)
hal-03585410 , version 2 (24-03-2022)

Identifiers

Cite

Yangtao Wang, Xi Shen, Shell Hu, Yuan Yuan, James L. Crowley, et al.. Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut. CVPR 2022 - Conference on Computer Vision and Pattern Recognition, Jun 2022, New Orleans, United States. ⟨hal-03585410v2⟩
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