SSMProbe framework reveals importance of token order in visual representations

Researchers have developed SSMProbe, a new framework for analyzing visual representations in AI models. This method utilizes State Space Models (SSMs) to account for the critical role of token order, challenging the traditional approach of treating patch representations as unstructured data. SSMProbe demonstrates that the sequence of tokens significantly impacts performance, especially when using learned soft permutations to exploit this order-dependent heterogeneity in representations. AI

影响 Introduces a novel method for analyzing visual representations, potentially leading to better understanding and development of vision models.

排序理由 This is a research paper published on arXiv detailing a new probing framework for visual models. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

• arXiv
• BEiT
• DINOv2
• MAE
• SSMProbe
• ViT
• State Space Model

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