MUFASA: A Multi-Layer Framework for Slot Attention

• URL: http://arxiv.org/abs/2602.07544v1
• Date: Sat, 07 Feb 2026 13:44:56 GMT
• Title: MUFASA: A Multi-Layer Framework for Slot Attention
• Authors: Sebastian Bock, Leonie Schüßler, Krishnakant Singh, Simone Schaub-Meyer, Stefan Roth,
• Abstract summary: We introduce MUFASA, a plug-and-play framework for slot attention-based approaches to unsupervised object segmentation.<n>Our model computes slot attention across multiple feature layers of the ViT encoder, fully leveraging their semantic richness.<n>We propose a fusion strategy to aggregate slots obtained on multiple layers into a unified object-centric representation.
• Score: 16.325300304610035
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Unsupervised object-centric learning (OCL) decomposes visual scenes into distinct entities. Slot attention is a popular approach that represents individual objects as latent vectors, called slots. Current methods obtain these slot representations solely from the last layer of a pre-trained vision transformer (ViT), ignoring valuable, semantically rich information encoded across the other layers. To better utilize this latent semantic information, we introduce MUFASA, a lightweight plug-and-play framework for slot attention-based approaches to unsupervised object segmentation. Our model computes slot attention across multiple feature layers of the ViT encoder, fully leveraging their semantic richness. We propose a fusion strategy to aggregate slots obtained on multiple layers into a unified object-centric representation. Integrating MUFASA into existing OCL methods improves their segmentation results across multiple datasets, setting a new state of the art while simultaneously improving training convergence with only minor inference overhead.

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