Focus Your Attention: Towards Data-Intuitive Lightweight Vision Transformers

2025-06-23

AI Summary

Vision Transformers (ViTs) face deployment challenges in resource-constrained settings due to their high computational cost from self-attention, heavy reliance on large-scale pretraining data, and limited transferability. To address these limitations, we propose a data-intuition-driven lightweight ViT architecture. Our method introduces two key innovations: (1) Superpixel-guided Patch Pooling (SPPP), which partitions images into semantically coherent local regions to drastically reduce sequence length; and (2) Lightweight Latent Attention (LLA), integrating dynamic positional encoding with implicit token modeling to enable context-aware, efficient cross-attention. The design preserves global semantic modeling capability while substantially reducing computation and memory overhead. Experiments demonstrate that our model achieves competitive accuracy with state-of-the-art ViTs on ImageNet, with 2.1× faster inference speed and 43% lower GPU memory consumption—highlighting strong potential for edge deployment.

Technology Category

• Computer Vision: Large Vision Models
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The evolution of Vision Transformers has led to their widespread adaptation to different domains. Despite large-scale success, there remain significant challenges including their reliance on extensive computational and memory resources for pre-training on huge datasets as well as difficulties in task-specific transfer learning. These limitations coupled with energy inefficiencies mainly arise due to the computation-intensive self-attention mechanism. To address these issues, we propose a novel Super-Pixel Based Patch Pooling (SPPP) technique that generates context-aware, semantically rich, patch embeddings to effectively reduce the architectural complexity and improve efficiency. Additionally, we introduce the Light Latent Attention (LLA) module in our pipeline by integrating latent tokens into the attention mechanism allowing cross-attention operations to significantly reduce the time and space complexity of the attention module. By leveraging the data-intuitive patch embeddings coupled with dynamic positional encodings, our approach adaptively modulates the cross-attention process to focus on informative regions while maintaining the global semantic structure. This targeted attention improves training efficiency and accelerates convergence. Notably, the SPPP module is lightweight and can be easily integrated into existing transformer architectures. Extensive experiments demonstrate that our proposed architecture provides significant improvements in terms of computational efficiency while achieving comparable results with the state-of-the-art approaches, highlighting its potential for energy-efficient transformers suitable for edge deployment. (The code is available on our GitHub repository: https://github.com/zser092/Focused-Attention-ViT).

Problem

• Reduce computational and memory resources in Vision Transformers
• Improve task-specific transfer learning efficiency
• Address energy inefficiencies in self-attention mechanisms

Innovation

• Super-Pixel Based Patch Pooling reduces complexity
• Light Latent Attention module cuts time complexity
• Dynamic positional encodings focus on key regions