降低输入图像分辨率可显著压缩视觉模型的计算与内存开销。
Input Resolution Downsizing as a Compression Technique for Vision Deep Learning Systems
- 通过降低输入图像分辨率实现模型压缩，作为剪枝、量化等方法的补充。
- 在分类与语义分割任务上，保持竞争力性能的同时大幅减少资源消耗。
- 适用于对效率敏感的视觉应用，尤其适合部署在资源受限设备上。
模型压缩是深度学习中至关重要的研究方向，尤其在视觉领域，旨在减轻模型的内存或计算负担。尽管已有大量压缩方法被提出，如剪枝、量化和知识蒸馏，但多数研究集中于这些技术。本文探索一个被忽视的途径：将输入图像分辨率降低，作为其他压缩手段的补充。我们系统研究了输入分辨率降低对分类与语义分割任务的影响，涵盖卷积网络（convnets）与基于变压器（transformer-based）架构。实验结果表明，该策略在标准基准上具有显著潜力，可在保持竞争性性能的同时大幅降低计算与内存需求。本研究确立了输入分辨率降低作为视觉模型压缩中一种可行且有前景的新方向。
原文摘要 · Abstract (English)
Model compression is a critical area of research in deep learning, in particular in vision, driven by the need to lighten models memory or computational footprints. While numerous methods for model compression have been proposed, most focus on pruning, quantization, or knowledge distillation. In this work, we delve into an under-explored avenue: reducing the resolution of the input image as a complementary approach to other types of compression. By systematically investigating the impact of input resolution reduction, on both tasks of classification and semantic segmentation, and on convnets and transformer-based architectures, we demonstrate that this strategy provides an interesting alternative for model compression. Our experimental results on standard benchmarks highlight the potential of this method, achieving competitive performance while significantly reducing computational and memory requirements. This study establishes input resolution reduction as a viable and promising direction in the broader landscape of model compression techniques for vision applications.
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