Abstract
Lightweight vision networks have witnessed remarkable progress in recent years, yet achieving a satisfactory balance among parameter scale, computational overhead, and task performance remains difficult. Although many existing lightweight models manage to reduce computation considerably, they often do so at the expense of a substantial increase in parameter count (e.g., LSNet, MobileMamba), which still poses obstacles for deployment on resource-limited devices. In parallel, some studies attempt to draw inspiration from human visual perception, but their modeling tends to oversimplify the visual process, making it hard to reflect how perception truly operates. Revisiting the cooperative mechanism of the human visual system, we propose GPM (Global-to-Parallel Multi-scale Encoding). GPM first employs a Global Insight Generator (GIG) to extract holistic cues, and subsequently processes features of different scales through parallel branches: LSAE emphasizes mid-/large-scale semantic relations, while IRB (Inverted Residual Block) preserves fine-grained texture information, jointly enabling coherent representation of global and local features. As such, GPM conforms to two characteristic behaviors of human vision perceiving the whole before focusing on details, and maintaining broad contextual awareness even during local attention. Built upon GPM, we further develop the lightweight H-GPE network. Experiments on image classification, object detection, and semantic segmentation show that H-GPE achieves strong performance while maintaining a balanced footprint in both FLOPs and parameters, delivering a more favorable accuracy-efficiency trade-off compared with recent state-of-the-art lightweight models.
Abstract (translated)
近年来，轻量级视觉网络取得了显著的进步，但要在参数规模、计算开销和任务性能之间取得满意的平衡仍然具有挑战性。虽然许多现有的轻量级模型能够大幅减少计算量，但这通常是以增加大量参数数量为代价的（例如LSNet、MobileMamba），这仍对资源受限设备的应用造成了障碍。与此同时，一些研究试图从人类视觉感知中汲取灵感，但其建模往往过于简化了视觉过程，难以真实反映感知机制。 重新审视人眼视觉系统的协同机制，我们提出了GPM（全局到并行多尺度编码）。GPM首先使用全局洞察生成器（Global Insight Generator, GIG）提取整体线索，并随后通过平行分支处理不同规模的特征：LSAE强调中/大规模语义关系，而IRB（倒残差块）则保留精细纹理信息。这种机制共同实现了对全球和局部特征的一致性表示。因此，GPM符合人类视觉感知先整体后细节、在局部注意时仍保持广泛上下文意识的两个特性。 基于GPM，我们进一步开发了轻量级H-GPE网络。实验结果表明，在图像分类、目标检测和语义分割任务中，H-GPE在网络性能强劲的同时，还能维持计算量（FLOPs）与参数数量之间的平衡，相较于近期的先进轻量模型而言，它在精度-效率权衡方面表现更佳。
URL
https://arxiv.org/abs/2601.08190
