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| Try this demo for <a href="https://github.com/hasibzunair/peekaboo">PEEKABOO</a>, | |
| introduced in our <strong>BMVC'2024</strong> paper <a href="https://arxiv.org/abs/2407.17628">PEEKABOO: Hiding Parts of an Image for Unsupervised Object Localization</a>. | |
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| Peekaboo aims to explicitly model contextual relationship among pixels through image masking for unsupervised object localization. | |
| In a self-supervised procedure (i.e. pretext task) without any additional training (i.e. downstream task), context-based representation learning is done at both | |
| the pixel-level by making predictions on masked images and at shape-level by matching the predictions of the masked input to the unmasked one. | |
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| You can use this demo to segment the most salient as well as novel object(s) in your images. To use it, simply | |
| upload an image of your choice and hit submit. You will get one or more segmentation maps of the most salient objects present | |
| in your images. | |
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| <a href="https://hasibzunair.github.io/peekaboo/"><strong>Project Page</strong></a> | |
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