LOOPE: Learnable Optimal Patch Order in Positional Embeddings for Vision Transformers

[2504.14386] LOOPE: Learnable Optimal Patch Order in Positional Embeddings for Vision Transformers Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2504.14386 (cs) [Submitted on 19 Apr 2025 ( v1 ), last revised 10 Apr 2026 (this version, v2)] Title: LOOPE: Learnable Optimal Patch Order in Positional Embeddings for Vision Transformers Authors: Md Abtahi Majeed Chowdhury , Md Rifat Ur Rahman , Akil Ahmad Taki View a PDF of the paper titled LOOPE: Learnable Optimal Patch Order in Positional Embeddings for Vision Transformers, by Md Abtahi Majeed Chowdhury and 2 other authors View PDF HTML (experimental) Abstract: Positional embeddings (PE) play a crucial role in Vision Transformers (ViTs) by providing spatial information otherwise lost due to the permutation invariant nature of self attention. While absolute positional embeddings (APE) have shown theoretical advantages over relative positional embeddings (RPE), particularly due to the ability of sinusoidal functions to preserve spatial inductive biases like monotonicity and shift invariance, a fundamental challenge arises when mapping a 2D grid to a 1D sequence. Existing methods have mostly overlooked or never explored the impact of patch ordering in positional embeddings. To address this, we propose LOOPE, a learnable patch-ordering method that optimizes spatial representation for a given set of frequencies, providing a principled approach to patch order optimization. Empirical results show that our PE significantly improves classification accuracy across various ViT architectures. To rigorously evaluate the effectiveness of positional embeddings, we introduce the "Three Cell Experiment", a novel benchmarking framework that assesses the ability of PEs to retain relative and absolute positional information across different ViT architectures. Unlike standard evaluations, which typically report a performance gap of 4 to 6% between models with and without PE, our method reveals a striking 30 to 35% difference, offering a more sensitive diagnostic tool to measure the efficacy of PEs. Our experimental analysis confirms that the proposed LOOPE demonstrates enhanced effectiveness in retaining both relative and absolute positional information. Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2504.14386 [cs.CV] (or arXiv:2504.14386v2 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2504.14386 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Md Abtahi Majeed Chowdhury [ view email ] [v1] Sat, 19 Apr 2025 19:20:47 UTC (2,349 KB) [v2] Fri, 10 Apr 2026 20:17:02 UTC (10,515 KB) Full-text links: Access Paper: View a PDF of the paper titled LOOPE: Learnable Optimal Patch Order in Positional Embeddings for Vision Transformers, by Md Abtahi Majeed Chowdhury and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2025-04 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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