Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut

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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Results from the Paper


 Ranked #1 on Weakly-Supervised Object Localization on CUB-200-2011 (Top-1 Localization Accuracy metric)

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Single-object discovery COCO_20k TokenCut + CAD CorLoc 62.6 # 4
Single-object discovery COCO_20k TokenCut CorLoc 58.8 # 5
Weakly-Supervised Object Localization CUB TokenCut Top-1 Localization Accuracy 72.9 # 1
Weakly-Supervised Object Localization CUB-200-2011 TokenCut Top-1 Localization Accuracy 72.9 # 1
Unsupervised Saliency Detection DUT-OMRON TokenCut Accuracy 89.7 # 3
Unsupervised Saliency Detection DUTS TokenCut Accuracy 91.4 # 3
Unsupervised Saliency Detection ECSSD TokenCut Accuracy 93.4 # 3
Weakly-Supervised Object Localization ImageNet TokenCut GT-known localization accuracy 65.4 # 3
Top-1 Localization Accuracy 52.3 # 3

Methods