标题： CS-VLM：用于高效视觉-语言表示学习的压缩感知注意力 显示英文标题

标题： CS-VLM: Compressed Sensing Attention for Efficient Vision-Language Representation Learning

摘要： 视觉-语言模型（vLLMs）已成为在视觉和文本输入上进行联合推理的强大架构，使图像描述、跨模态检索和多模态对话等领域取得了突破。 然而，随着这些模型扩展到更长的视频序列和更丰富的语言描述，标准注意力机制的二次复杂度带来了基本的计算瓶颈。 在vLLMs中，这一挑战更加严重，因为注意力不仅需要在模态内部计算，还需要在模态之间计算，导致高昂的内存和延迟成本。 在这项工作中，我们引入了压缩感知注意力变换器（CSAT），这是一种通过压缩感知视角重新构想注意力计算的新架构。 通过使用随机测量矩阵将高维键和值表示投影到低维子空间，并利用稀疏恢复算法重建注意力输出，CSAT显著降低了注意力复杂度，同时保持了语义保真度。 应用于vLLMs时，CSAT利用了视觉和文本表示的固有可压缩性，尤其是在视频中，时间冗余较高，以及在语言中，跨模态定位通常较为稀疏。 与必须经常建模纠缠符号依赖关系的LLMs不同，vLLMs受益于对齐和场景组合中的结构稀疏性，这使得它们特别适合压缩注意力。 我们提供了CSAT的形式化数学处理，展示了其在视觉语言流程中的集成，并在标准基准上验证了其性能，突显了其作为下一代多模态变换器的可扩展、可解释和资源高效解决方案的前景。 显示英文摘要

摘要： Vision-Language Models (vLLMs) have emerged as powerful architectures for joint reasoning over visual and textual inputs, enabling breakthroughs in image captioning, cross modal retrieval, and multimodal dialogue. However, as these models scale to longer video sequences and richer language descriptions, the quadratic complexity of the standard attention mechanism presents a fundamental computational bottleneck. This challenge is exacerbated in vLLMs, where attention must be computed not only within modalities but also across them, leading to prohibitive memory and latency costs. In this work, we introduce the Compressed Sensing Attention Transformer (CSAT), a novel architecture that reimagines attention computation through the lens of compressed sensing. By projecting high dimensional key and value representations into a lower-dimensional subspace via random measurement matrices and reconstructing the attention outputs using sparse recovery algorithms, CSAT significantly reduces attention complexity while maintaining semantic fidelity. Applied to vLLMs, CSAT exploits the inherent compressibility of both visual and textual representations especially evident in video, where temporal redundancy is high, and in language, where cross-modal grounding is often sparse. In contrast to LLMs, which must often model entangled symbolic dependencies, vLLMs benefit from structured sparsity in alignment and scene composition, making them particularly well-suited to compressed attention. We provide a formal mathematical treatment of CSAT, demonstrate its integration into vision language pipelines, and validate its performance on standard benchmarks, highlighting its promise as a scalable, interpretable, and resource efficient solution for next generation multimodal transformers.